Articles | Volume 25, issue 4
https://doi.org/10.5194/nhess-25-1481-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-25-1481-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Apr 2025
Research article |
| 24 Apr 2025
| 24 Apr 2025
Prediction of the volume of shallow landslides due to rainfall using data-driven models
Jérémie Tuganishuri
Department of Civil and Environmental Engineering, Gangneung–Wonju National University, Gangneung, Gangwon 25457, South Korea
Chan-Young Yune
Department of Civil and Environmental Engineering, Gangneung–Wonju National University, Gangneung, Gangwon 25457, South Korea
Gihong Kim
Department of Civil and Environmental Engineering, Gangneung–Wonju National University, Gangneung, Gangwon 25457, South Korea
Seung Woo Lee
Department of Civil and Environmental Engineering, Gangneung–Wonju National University, Gangneung, Gangwon 25457, South Korea
Manik Das Adhikari
Department of Civil and Environmental Engineering, Gangneung–Wonju National University, Gangneung, Gangwon 25457, South Korea
Sang-Guk Yum
CORRESPONDING AUTHOR
Department of Civil and Environmental Engineering, Gangneung–Wonju National University, Gangneung, Gangwon 25457, South Korea
Related authors
Tuganishuri Jérémie, Chan-Young Yune, Gihong Kim, Seung Woo Lee, Manik Adhikari, and Sang-Guk Yum
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2023-73, https://doi.org/10.5194/nhess-2023-73, 2023
Manuscript not accepted for further review
Short summary
Short summary
The prediction of the size of rainfall-induced debris in South Korea was analyzed. The model suitability was carried out and Random forest was the most suitable for the Size of debris prediction. The most contributing factor in the model was slope length and the most vulnerable region to higher frequency and severe debris was Gangwon province. The findings may be used for rainfall induced-debris prevention policies and post-disaster rehabilitation planning.
Sang-Guk Yum, Moon-Soo Song, and Manik Das Adhikari
Nat. Hazards Earth Syst. Sci., 23, 2449–2474, https://doi.org/10.5194/nhess-23-2449-2023, https://doi.org/10.5194/nhess-23-2449-2023, 2023
Short summary
Short summary
This study performed analysis on typhoon-induced coastal morphodynamics for the Mokpo coast. Wetland vegetation was severely impacted by Typhoon Soulik, with 87.35 % of shoreline transects experiencing seaward migration. This result highlights the fact that sediment resuspension controls the land alteration process over the typhoon period. The land accretion process dominated during the pre- to post-typhoon periods.
Tuganishuri Jérémie, Chan-Young Yune, Gihong Kim, Seung Woo Lee, Manik Adhikari, and Sang-Guk Yum
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2023-73, https://doi.org/10.5194/nhess-2023-73, 2023
Manuscript not accepted for further review
Short summary
Short summary
The prediction of the size of rainfall-induced debris in South Korea was analyzed. The model suitability was carried out and Random forest was the most suitable for the Size of debris prediction. The most contributing factor in the model was slope length and the most vulnerable region to higher frequency and severe debris was Gangwon province. The findings may be used for rainfall induced-debris prevention policies and post-disaster rehabilitation planning.
Seok Bum Hong, Hong Sik Yun, Sang Guk Yum, Seung Yeop Ryu, In Seong Jeong, and Jisung Kim
Nat. Hazards Earth Syst. Sci., 22, 3435–3459, https://doi.org/10.5194/nhess-22-3435-2022, https://doi.org/10.5194/nhess-22-3435-2022, 2022
Short summary
Short summary
This study advances previous models through machine learning and multi-sensor-verified results. Using spatial and meteorological data from the study area (Suncheon–Wanju Highway in Gurye-gun), the amount and location of black ice were modelled based on system dynamics to predict black ice and then simulated with the geographic information system (m2). Based on the model results, multiple sensors were buried at four selected points in the study area, and the model was compared with sensor data.
Ji-Myong Kim, Sang-Guk Yum, Hyunsoung Park, and Junseo Bae
Nat. Hazards Earth Syst. Sci., 22, 2131–2144, https://doi.org/10.5194/nhess-22-2131-2022, https://doi.org/10.5194/nhess-22-2131-2022, 2022
Short summary
Short summary
Insurance data has been utilized with deep learning techniques to predict natural disaster damage losses in South Korea.
Moon-Soo Song, Hong-Sik Yun, Jae-Joon Lee, and Sang-Guk Yum
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2022-118, https://doi.org/10.5194/nhess-2022-118, 2022
Manuscript not accepted for further review
Short summary
Short summary
In this study, emerging engineering techniques such as machine learning and deep learning technique was applied to predict heavy snowfall prediction in the Korean Peninsula. More specifically, it was observed that the predictive model using the RFR algorithm had the best performance based on a comparison between the observed and predicted data. In addition, it was observed that the performance of the ensemble models (RFR and XGB) was better than that of the single regression models.
Sang-Guk Yum, Hsi-Hsien Wei, and Sung-Hwan Jang
Nat. Hazards Earth Syst. Sci., 21, 2611–2631, https://doi.org/10.5194/nhess-21-2611-2021, https://doi.org/10.5194/nhess-21-2611-2021, 2021
Short summary
Short summary
Developed statistical models to predict the non-exceedance probability of extreme storm surge-induced typhoons. Various probability distribution models were applied to find the best fitting to empirical storm-surge data.
D. Kim, J. Youn, T. Kim, and G. Kim
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 431–435, https://doi.org/10.5194/isprs-archives-XLII-2-W13-431-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-431-2019, 2019
J. Youn, T. Kim, D. Kim, and G. Kim
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W4, 573–578, https://doi.org/10.5194/isprs-archives-XLII-3-W4-573-2018, https://doi.org/10.5194/isprs-archives-XLII-3-W4-573-2018, 2018
G. Kim, C. Y. Yune, J. Paik, and S. W. Lee
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 85–88, https://doi.org/10.5194/isprs-archives-XLI-B8-85-2016, https://doi.org/10.5194/isprs-archives-XLI-B8-85-2016, 2016
S. K. Nath, M. D. Adhikari, N. Devaraj, and S. K. Maiti
Nat. Hazards Earth Syst. Sci., 15, 1103–1121, https://doi.org/10.5194/nhess-15-1103-2015, https://doi.org/10.5194/nhess-15-1103-2015, 2015
Short summary
Short summary
We microzoned seismic hazard of the City of Kolkata, India by integrating seismological, geological and geotechnical themes in GIS which in turn is integrated with the vulnerability components in a logic-tree framework for the estimation of both the socio-economic and structural risk of the City. The cumulative damage probabilities in terms of ‘none’, ‘slight’, ‘moderate’, ‘extensive’ and ‘complete’ have been assessed for the predominantly four model building types RM2L, RM2M, URML and URMM.
S. K. Nath, M. D. Adhikari, S. K. Maiti, N. Devaraj, N. Srivastava, and L. D. Mohapatra
Nat. Hazards Earth Syst. Sci., 14, 2549–2575, https://doi.org/10.5194/nhess-14-2549-2014, https://doi.org/10.5194/nhess-14-2549-2014, 2014
Related subject area
Databases, GIS, Remote Sensing, Early Warning Systems and Monitoring Technologies
Monitoring snow depth variations in an avalanche release area using low-cost lidar and optical sensors
Satellite-based data for agricultural index insurance: a systematic quantitative literature review
A methodology to compile multi-hazard interrelationships in a data-scarce setting: an application to the Kathmandu Valley, Nepal
An automated approach for developing geohazard inventories using news: Integrating NLP, machine learning, and mapping
Review article: Physical vulnerability database for critical infrastructure hazard risk assessments – a systematic review and data collection
Exploring drought hazard, vulnerability, and related impacts on agriculture in Brandenburg
Dynamical changes in seismic properties prior to, during, and after the 2014–2015 Holuhraun eruption, Iceland
The World Wide Lightning Location Network (WWLLN) over Spain
Automated Rapid Estimation of Flood Depth using Digital Elevation Model and EOS-04 Satellite derived Flood inundation
AscDAMs: advanced SLAM-based channel detection and mapping system
Shoreline and land use–land cover changes along the 2004-tsunami-affected South Andaman coast: understanding changing hazard susceptibility
What can we learn from global disaster records about multi-hazards and their risk dynamics?
Insights into the development of a landslide early warning system prototype in an informal settlement: the case of Bello Oriente in Medellín, Colombia
Tsunami hazard perception and knowledge of alert: early findings in five municipalities along the French Mediterranean coastlines
Exploiting radar polarimetry for nowcasting thunderstorm hazards using deep learning
Machine-learning-based nowcasting of the Vögelsberg deep-seated landslide: why predicting slow deformation is not so easy
Fixed photogrammetric systems for natural hazard monitoring with high spatio-temporal resolution
A neural network model for automated prediction of avalanche danger level
Brief communication: Landslide activity on the Argentinian Santa Cruz River mega dam works confirmed by PSI DInSAR
Impact of topography on in situ soil wetness measurements for regional landslide early warning – a case study from the Swiss Alpine Foreland
Earthquake building damage detection based on synthetic-aperture-radar imagery and machine learning
Assessing riverbank erosion in Bangladesh using time series of Sentinel-1 radar imagery in the Google Earth Engine
Quantifying unequal urban resilience to rainfall across China from location-aware big data
Comparison of machine learning techniques for reservoir outflow forecasting
Development of black ice prediction model using GIS-based multi-sensor model validation
Forecasting vegetation condition with a Bayesian auto-regressive distributed lags (BARDL) model
A dynamic hierarchical Bayesian approach for forecasting vegetation condition
Using a single remote-sensing image to calculate the height of a landslide dam and the maximum volume of a lake
Enhancing disaster risk resilience using greenspace in urbanising Quito, Ecuador
Gridded flood depth estimates from satellite-derived inundations
ProbFire: a probabilistic fire early warning system for Indonesia
Index establishment and capability evaluation of space–air–ground remote sensing cooperation in geohazard emergency response
Brief communication: Monitoring a soft-rock coastal cliff using webcams and strain sensors
Multiscale analysis of surface roughness for the improvement of natural hazard modelling
EUNADICS-AV early warning system dedicated to supporting aviation in the case of a crisis from natural airborne hazards and radionuclide clouds
Are sirens effective tools to alert the population in France?
UAV survey method to monitor and analyze geological hazards: the case study of the mud volcano of Villaggio Santa Barbara, Caltanissetta (Sicily)
Timely prediction potential of landslide early warning systems with multispectral remote sensing: a conceptual approach tested in the Sattelkar, Austria
CHILDA – Czech Historical Landslide Database
Review article: Detection of actionable tweets in crisis events
Long-term magnetic anomalies and their possible relationship to the latest greater Chilean earthquakes in the context of the seismo-electromagnetic theory
HazMapper: a global open-source natural hazard mapping application in Google Earth Engine
Opportunities and risks of disaster data from social media: a systematic review of incident information
Online urban-waterlogging monitoring based on a recurrent neural network for classification of microblogging text
Predicting power outages caused by extratropical storms
Near-real-time automated classification of seismic signals of slope failures with continuous random forests
Assessing the accuracy of remotely sensed fire datasets across the southwestern Mediterranean Basin
Responses to severe weather warnings and affective decision-making
The object-specific flood damage database HOWAS 21
A spaceborne SAR-based procedure to support the detection of landslides
Pia Ruttner, Annelies Voordendag, Thierry Hartmann, Julia Glaus, Andreas Wieser, and Yves Bühler
Nat. Hazards Earth Syst. Sci., 25, 1315–1330, https://doi.org/10.5194/nhess-25-1315-2025, https://doi.org/10.5194/nhess-25-1315-2025, 2025
Short summary
Short summary
Snow depth variations caused by wind are an important factor in avalanche danger, but detailed and up-to-date information is rarely available. We propose a monitoring system, using lidar and optical sensors, to measure the snow depth distribution at high spatial and temporal resolution. First results show that we can quantify snow depth changes with an accuracy on the low decimeter level, or better, and can identify events such as avalanches or displacement of snow during periods of strong winds.
Thuy T. Nguyen, Shahbaz Mushtaq, Jarrod Kath, Thong Nguyen-Huy, and Louis Reymondin
Nat. Hazards Earth Syst. Sci., 25, 913–927, https://doi.org/10.5194/nhess-25-913-2025, https://doi.org/10.5194/nhess-25-913-2025, 2025
Short summary
Short summary
We reviewed the use of satellite-based data in designing agricultural index-based insurance (IBI) products, an effective tool for managing climate risk and promoting sustainable development. Despite the increasing number of studies since 2010 to present, the review revealed a gap in applying the approach to perennial crops in developing countries. We also highlighted the growing importance of satellite data for index insurance, employing high-resolution datasets to reduce basis risk.
Harriet E. Thompson, Joel C. Gill, Robert Šakić Trogrlić, Faith E. Taylor, and Bruce D. Malamud
Nat. Hazards Earth Syst. Sci., 25, 353–381, https://doi.org/10.5194/nhess-25-353-2025, https://doi.org/10.5194/nhess-25-353-2025, 2025
Short summary
Short summary
We present a methodology to compile single hazards and multi-hazard interrelationships in data-scarce urban settings, which we apply to the Kathmandu Valley, Nepal. Using blended sources, we collate evidence of 21 single natural hazard types and 83 multi-hazard interrelationships that could impact the Kathmandu Valley. We supplement these exemplars with multi-hazard scenarios developed by practitioner stakeholders, emphasising the need for inclusive disaster preparedness and response approaches.
Aydoğan Avcıoğlu, Ogün Demir, and Tolga Görüm
EGUsphere, https://doi.org/10.5194/egusphere-2025-7, https://doi.org/10.5194/egusphere-2025-7, 2025
Short summary
Short summary
Here we demonstrate an approach for the development of inventories from internet sources to geolocalized geohazard incidents. We created a tool that autonomously gets news, processes it using NLP and machine learning, and maps using Open Street Map. Consequently, we present spatiotemporal inventories for geohazards resulting in a total of 13940 incidents between 1997 and 2023 in Türkiye. Our alternative and easy-to-implement development inventory method aids geohazard management and resilience.
Sadhana Nirandjan, Elco E. Koks, Mengqi Ye, Raghav Pant, Kees C. H. Van Ginkel, Jeroen C. J. H. Aerts, and Philip J. Ward
Nat. Hazards Earth Syst. Sci., 24, 4341–4368, https://doi.org/10.5194/nhess-24-4341-2024, https://doi.org/10.5194/nhess-24-4341-2024, 2024
Short summary
Short summary
Critical infrastructures (CIs) are exposed to natural hazards, which may result in significant damage and burden society. Vulnerability is a key determinant for reducing these risks, yet crucial information is scattered in the literature. Our study reviews over 1510 fragility and vulnerability curves for CI assets, creating a unique publicly available physical vulnerability database that can be directly used for hazard risk assessments, including floods, earthquakes, windstorms, and landslides.
Fabio Brill, Pedro Henrique Lima Alencar, Huihui Zhang, Friedrich Boeing, Silke Hüttel, and Tobia Lakes
Nat. Hazards Earth Syst. Sci., 24, 4237–4265, https://doi.org/10.5194/nhess-24-4237-2024, https://doi.org/10.5194/nhess-24-4237-2024, 2024
Short summary
Short summary
Droughts are a threat to agricultural crops, but different factors influence how much damage occurs. This is important to know to create meaningful risk maps and to evaluate adaptation options. We investigate the years 2013–2022 in Brandenburg, Germany, and find in particular the soil quality and meteorological drought in June to be statistically related to the observed damage. Measurement of crop health from satellites is also related to soil quality and not necessarily to anomalous yields.
Maria R. P. Sudibyo, Eva P. S. Eibl, Sebastian Hainzl, and Matthias Ohrnberger
Nat. Hazards Earth Syst. Sci., 24, 4075–4089, https://doi.org/10.5194/nhess-24-4075-2024, https://doi.org/10.5194/nhess-24-4075-2024, 2024
Short summary
Short summary
We assessed the performance of permutation entropy (PE), phase permutation entropy (PPE), and instantaneous frequency (IF), which are estimated from a single seismic station, to detect changes before, during, and after the 2014–2015 Holuhraun eruption in Iceland. We show that these three parameters are sensitive to the pre-eruptive and eruptive processes. Finally, we discuss their potential and limitations in eruption monitoring.
Enrique A. Navarro, Jorge A. Portí, Alfonso Salinas, Sergio Toledo-Redondo, Jaume Segura-García, Aida Castilla, Víctor Montagud-Camps, and Inmaculada Albert
Nat. Hazards Earth Syst. Sci., 24, 3925–3943, https://doi.org/10.5194/nhess-24-3925-2024, https://doi.org/10.5194/nhess-24-3925-2024, 2024
Short summary
Short summary
The World Wide Lightning Location Network (WWLLN) operates a globally distributed network of stations that detect lightning signals at a planetary scale. A detection efficiency of 29 % with a location accuracy of between 2 and 3 km is obtained for the area of Spain by comparing WWLLN data with those of the Spanish State Meteorological Agency. The network's capability to resolve convective-storm cells generated in a cutoff low-pressure system is also demonstrated in the west Mediterranean Sea.
Lakshmi Amani Chimata, Suresh Babu Anuvala Setty Venkata, Shashi Vardhan Reddy Patlolla, Durga Rao Korada Hari Venkata, Sreenivas Kandrika, and Prakash Chauhan
EGUsphere, https://doi.org/10.5194/egusphere-2024-2278, https://doi.org/10.5194/egusphere-2024-2278, 2024
Short summary
Short summary
Fast flood assessments are important for providing effective help during emergencies and planning for future floods. This study presents a new automated way to quickly measure flood depth. By using satellite images and digital elevation models, this method makes it easier to get real-time flood information. We applied this new method in several flood-prone areas in India and found that it provides more accurate results than existing flood measurement tools.
Tengfei Wang, Fucheng Lu, Jintao Qin, Taosheng Huang, Hui Kong, and Ping Shen
Nat. Hazards Earth Syst. Sci., 24, 3075–3094, https://doi.org/10.5194/nhess-24-3075-2024, https://doi.org/10.5194/nhess-24-3075-2024, 2024
Short summary
Short summary
Harsh environments limit the use of drone, satellite, and simultaneous localization and mapping technology to obtain precise channel morphology data. We propose AscDAMs, which includes a deviation correction algorithm to reduce errors, a point cloud smoothing algorithm to diminish noise, and a cross-section extraction algorithm to quantitatively assess the morphology data. AscDAMs solves the problems and provides researchers with more reliable channel morphology data for further analysis.
Vikas Ghadamode, Aruna Kumari Kondarathi, Anand K. Pandey, and Kirti Srivastava
Nat. Hazards Earth Syst. Sci., 24, 3013–3033, https://doi.org/10.5194/nhess-24-3013-2024, https://doi.org/10.5194/nhess-24-3013-2024, 2024
Short summary
Short summary
In 2004-tsunami-affected South Andaman, tsunami wave propagation, arrival times, and run-up heights at 13 locations are computed to analyse pre- and post-tsunami shoreline and land use–land cover changes to understand the evolving hazard scenario. The LULC changes and dynamic shoreline changes are observed in zones 3, 4, and 5 owing to dynamic population changes, infrastructural growth, and gross state domestic product growth. Economic losses would increase 5-fold for a similar tsunami.
Wiebke S. Jäger, Marleen C. de Ruiter, Timothy Tiggeloven, and Philip J. Ward
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-134, https://doi.org/10.5194/nhess-2024-134, 2024
Revised manuscript under review for NHESS
Short summary
Short summary
Multiple hazards, occurring at the same time or shortly after one another, can have more extreme impacts than single hazards. We examined the disaster records in the global emergency events database EM-DAT to better understand this phenomenon. We developed a method to identify such multi-hazards and analyzed their reported impacts using statistics. Multi-hazards have accounted for a disproportionate amount of the overall impacts, but there are different patterns in which the impacts compound.
Christian Werthmann, Marta Sapena, Marlene Kühnl, John Singer, Carolina Garcia, Tamara Breuninger, Moritz Gamperl, Bettina Menschik, Heike Schäfer, Sebastian Schröck, Lisa Seiler, Kurosch Thuro, and Hannes Taubenböck
Nat. Hazards Earth Syst. Sci., 24, 1843–1870, https://doi.org/10.5194/nhess-24-1843-2024, https://doi.org/10.5194/nhess-24-1843-2024, 2024
Short summary
Short summary
Early warning systems (EWSs) promise to decrease the vulnerability of self-constructed (informal) settlements. A living lab developed a partially functional prototype of an EWS for landslides in a Medellín neighborhood. The first findings indicate that technical aspects can be manageable, unlike social and political dynamics. A resilient EWS for informal settlements has to achieve sufficient social and technical redundancy to maintain basic functionality in a reduced-support scenario.
Johnny Douvinet, Noé Carles, Pierre Foulquier, and Matthieu Peroche
Nat. Hazards Earth Syst. Sci., 24, 715–735, https://doi.org/10.5194/nhess-24-715-2024, https://doi.org/10.5194/nhess-24-715-2024, 2024
Short summary
Short summary
This study provided an opportunity to assess both the perception of the tsunami hazard and the knowledge of alerts in five municipalities located along the French Mediterranean coastlines. The age and location of the respondents explain several differences between the five municipalities surveyed – more so than gender or residence status. This study may help local authorities to develop future tsunami awareness actions and to identify more appropriate strategies to be applied in the short term.
Nathalie Rombeek, Jussi Leinonen, and Ulrich Hamann
Nat. Hazards Earth Syst. Sci., 24, 133–144, https://doi.org/10.5194/nhess-24-133-2024, https://doi.org/10.5194/nhess-24-133-2024, 2024
Short summary
Short summary
Severe weather such as hail, lightning, and heavy rainfall can be hazardous to humans and property. Dual-polarization weather radars provide crucial information to forecast these events by detecting precipitation types. This study analyses the importance of dual-polarization data for predicting severe weather for 60 min using an existing deep learning model. The results indicate that including these variables improves the accuracy of predicting heavy rainfall and lightning.
Adriaan L. van Natijne, Thom A. Bogaard, Thomas Zieher, Jan Pfeiffer, and Roderik C. Lindenbergh
Nat. Hazards Earth Syst. Sci., 23, 3723–3745, https://doi.org/10.5194/nhess-23-3723-2023, https://doi.org/10.5194/nhess-23-3723-2023, 2023
Short summary
Short summary
Landslides are one of the major weather-related geohazards. To assess their potential impact and design mitigation solutions, a detailed understanding of the slope is required. We tested if the use of machine learning, combined with satellite remote sensing data, would allow us to forecast deformation. Our results on the Vögelsberg landslide, a deep-seated landslide near Innsbruck, Austria, show that the formulation of such a machine learning system is not as straightforward as often hoped for.
Xabier Blanch, Marta Guinau, Anette Eltner, and Antonio Abellan
Nat. Hazards Earth Syst. Sci., 23, 3285–3303, https://doi.org/10.5194/nhess-23-3285-2023, https://doi.org/10.5194/nhess-23-3285-2023, 2023
Short summary
Short summary
We present cost-effective photogrammetric systems for high-resolution rockfall monitoring. The paper outlines the components, assembly, and programming codes required. The systems utilize prime cameras to generate 3D models and offer comparable performance to lidar for change detection monitoring. Real-world applications highlight their potential in geohazard monitoring which enables accurate detection of pre-failure deformation and rockfalls with a high temporal resolution.
Vipasana Sharma, Sushil Kumar, and Rama Sushil
Nat. Hazards Earth Syst. Sci., 23, 2523–2530, https://doi.org/10.5194/nhess-23-2523-2023, https://doi.org/10.5194/nhess-23-2523-2023, 2023
Short summary
Short summary
Snow avalanches are a natural hazard that can cause danger to human lives. This threat can be reduced by accurate prediction of the danger levels. The development of mathematical models based on past data and present conditions can help to improve the accuracy of prediction. This research aims to develop a neural-network-based model for correlating complex relationships between the meteorological variables and the profile variables.
Guillermo Tamburini-Beliveau, Sebastián Balbarani, and Oriol Monserrat
Nat. Hazards Earth Syst. Sci., 23, 1987–1999, https://doi.org/10.5194/nhess-23-1987-2023, https://doi.org/10.5194/nhess-23-1987-2023, 2023
Short summary
Short summary
Landslides and ground deformation associated with the construction of a hydropower mega dam in the Santa Cruz River in Argentine Patagonia have been monitored using radar and optical satellite data, together with the analysis of technical reports. This allowed us to assess the integrity of the construction, providing a new and independent dataset. We have been able to identify ground deformation trends that put the construction works at risk.
Adrian Wicki, Peter Lehmann, Christian Hauck, and Manfred Stähli
Nat. Hazards Earth Syst. Sci., 23, 1059–1077, https://doi.org/10.5194/nhess-23-1059-2023, https://doi.org/10.5194/nhess-23-1059-2023, 2023
Short summary
Short summary
Soil wetness measurements are used for shallow landslide prediction; however, existing sites are often located in flat terrain. Here, we assessed the ability of monitoring sites at flat locations to detect critically saturated conditions compared to if they were situated at a landslide-prone location. We found that differences exist but that both sites could equally well distinguish critical from non-critical conditions for shallow landslide triggering if relative changes are considered.
Anirudh Rao, Jungkyo Jung, Vitor Silva, Giuseppe Molinario, and Sang-Ho Yun
Nat. Hazards Earth Syst. Sci., 23, 789–807, https://doi.org/10.5194/nhess-23-789-2023, https://doi.org/10.5194/nhess-23-789-2023, 2023
Short summary
Short summary
This article presents a framework for semi-automated building damage assessment due to earthquakes from remote-sensing data and other supplementary datasets including high-resolution building inventories, while also leveraging recent advances in machine-learning algorithms. For three out of the four recent earthquakes studied, the machine-learning framework is able to identify over 50 % or nearly half of the damaged buildings successfully.
Jan Freihardt and Othmar Frey
Nat. Hazards Earth Syst. Sci., 23, 751–770, https://doi.org/10.5194/nhess-23-751-2023, https://doi.org/10.5194/nhess-23-751-2023, 2023
Short summary
Short summary
In Bangladesh, riverbank erosion occurs every year during the monsoon and affects thousands of households. Information on locations and extent of past erosion can help anticipate where erosion might occur in the upcoming monsoon season and to take preventive measures. In our study, we show how time series of radar satellite imagery can be used to retrieve information on past erosion events shortly after the monsoon season using a novel interactive online tool based on the Google Earth Engine.
Jiale Qian, Yunyan Du, Jiawei Yi, Fuyuan Liang, Nan Wang, Ting Ma, and Tao Pei
Nat. Hazards Earth Syst. Sci., 23, 317–328, https://doi.org/10.5194/nhess-23-317-2023, https://doi.org/10.5194/nhess-23-317-2023, 2023
Short summary
Short summary
Human activities across China show a similar trend in response to rains. However, urban resilience varies significantly by region. The northwestern arid region and the central underdeveloped areas are very fragile, and even low-intensity rains can trigger significant human activity anomalies. By contrast, even high-intensity rains might not affect residents in the southeast.
Orlando García-Feal, José González-Cao, Diego Fernández-Nóvoa, Gonzalo Astray Dopazo, and Moncho Gómez-Gesteira
Nat. Hazards Earth Syst. Sci., 22, 3859–3874, https://doi.org/10.5194/nhess-22-3859-2022, https://doi.org/10.5194/nhess-22-3859-2022, 2022
Short summary
Short summary
Extreme events have increased in the last few decades; having a good estimation of the outflow of a reservoir can be an advantage for water management or early warning systems. This study analyzes the efficiency of different machine learning techniques to predict reservoir outflow. The results obtained showed that the proposed models provided a good estimation of the outflow of the reservoirs, improving the results obtained with classical approaches.
Seok Bum Hong, Hong Sik Yun, Sang Guk Yum, Seung Yeop Ryu, In Seong Jeong, and Jisung Kim
Nat. Hazards Earth Syst. Sci., 22, 3435–3459, https://doi.org/10.5194/nhess-22-3435-2022, https://doi.org/10.5194/nhess-22-3435-2022, 2022
Short summary
Short summary
This study advances previous models through machine learning and multi-sensor-verified results. Using spatial and meteorological data from the study area (Suncheon–Wanju Highway in Gurye-gun), the amount and location of black ice were modelled based on system dynamics to predict black ice and then simulated with the geographic information system (m2). Based on the model results, multiple sensors were buried at four selected points in the study area, and the model was compared with sensor data.
Edward E. Salakpi, Peter D. Hurley, James M. Muthoka, Adam B. Barrett, Andrew Bowell, Seb Oliver, and Pedram Rowhani
Nat. Hazards Earth Syst. Sci., 22, 2703–2723, https://doi.org/10.5194/nhess-22-2703-2022, https://doi.org/10.5194/nhess-22-2703-2022, 2022
Short summary
Short summary
The devastating effects of recurring drought conditions are mostly felt by pastoralists that rely on grass and shrubs as fodder for their animals. Using historical information from precipitation, soil moisture, and vegetation health data, we developed a model that can forecast vegetation condition and the probability of drought occurrence up till a 10-week lead time with an accuracy of 74 %. Our model can be adopted by policymakers and relief agencies for drought early warning and early action.
Edward E. Salakpi, Peter D. Hurley, James M. Muthoka, Andrew Bowell, Seb Oliver, and Pedram Rowhani
Nat. Hazards Earth Syst. Sci., 22, 2725–2749, https://doi.org/10.5194/nhess-22-2725-2022, https://doi.org/10.5194/nhess-22-2725-2022, 2022
Short summary
Short summary
The impact of drought may vary in a given region depending on whether it is dominated by trees, grasslands, or croplands. The differences in impact can also be the agro-ecological zones within the region. This paper proposes a hierarchical Bayesian model (HBM) for forecasting vegetation condition in spatially diverse areas. Compared to a non-hierarchical model, the HBM proved to be a more natural method for forecasting drought in areas with different land covers and
agro-ecological zones.
Weijie Zou, Yi Zhou, Shixin Wang, Futao Wang, Litao Wang, Qing Zhao, Wenliang Liu, Jinfeng Zhu, Yibing Xiong, Zhenqing Wang, and Gang Qin
Nat. Hazards Earth Syst. Sci., 22, 2081–2097, https://doi.org/10.5194/nhess-22-2081-2022, https://doi.org/10.5194/nhess-22-2081-2022, 2022
Short summary
Short summary
Landslide dams are secondary disasters caused by landslides, which can cause great damage to mountains. We have proposed a procedure to calculate the key parameters of these dams that uses only a single remote-sensing image and a pre-landslide DEM combined with landslide theory. The core of this study is a modeling problem. We have found the bridge between the theory of landslide dams and the requirements of disaster relief.
C. Scott Watson, John R. Elliott, Susanna K. Ebmeier, María Antonieta Vásquez, Camilo Zapata, Santiago Bonilla-Bedoya, Paulina Cubillo, Diego Francisco Orbe, Marco Córdova, Jonathan Menoscal, and Elisa Sevilla
Nat. Hazards Earth Syst. Sci., 22, 1699–1721, https://doi.org/10.5194/nhess-22-1699-2022, https://doi.org/10.5194/nhess-22-1699-2022, 2022
Short summary
Short summary
We assess how greenspaces could guide risk-informed planning and reduce disaster risk for the urbanising city of Quito, Ecuador, which experiences earthquake, volcano, landslide, and flood hazards. We use satellite data to evaluate the use of greenspaces as safe spaces following an earthquake. We find disparities regarding access to and availability of greenspaces. The availability of greenspaces that could contribute to community resilience is high; however, many require official designation.
Seth Bryant, Heather McGrath, and Mathieu Boudreault
Nat. Hazards Earth Syst. Sci., 22, 1437–1450, https://doi.org/10.5194/nhess-22-1437-2022, https://doi.org/10.5194/nhess-22-1437-2022, 2022
Short summary
Short summary
The advent of new satellite technologies improves our ability to study floods. While the depth of water at flooded buildings is generally the most important variable for flood researchers, extracting this accurately from satellite data is challenging. The software tool presented here accomplishes this, and tests show the tool is more accurate than competing tools. This achievement unlocks more detailed studies of past floods and improves our ability to plan for and mitigate disasters.
Tadas Nikonovas, Allan Spessa, Stefan H. Doerr, Gareth D. Clay, and Symon Mezbahuddin
Nat. Hazards Earth Syst. Sci., 22, 303–322, https://doi.org/10.5194/nhess-22-303-2022, https://doi.org/10.5194/nhess-22-303-2022, 2022
Short summary
Short summary
Extreme fire episodes in Indonesia emit large amounts of greenhouse gasses and have negative effects on human health in the region. In this study we show that such burning events can be predicted several months in advance in large parts of Indonesia using existing seasonal climate forecasts and forest cover change datasets. A reliable early fire warning system would enable local agencies to prepare and mitigate the worst of the effects.
Yahong Liu and Jin Zhang
Nat. Hazards Earth Syst. Sci., 22, 227–244, https://doi.org/10.5194/nhess-22-227-2022, https://doi.org/10.5194/nhess-22-227-2022, 2022
Short summary
Short summary
Through a comprehensive analysis of the current remote sensing technology resources, this paper establishes the database to realize the unified management of heterogeneous sensor resources and proposes a capability evaluation method of remote sensing cooperative technology in geohazard emergencies, providing a decision-making basis for the establishment of remote sensing cooperative observations in geohazard emergencies.
Diego Guenzi, Danilo Godone, Paolo Allasia, Nunzio Luciano Fazio, Michele Perrotti, and Piernicola Lollino
Nat. Hazards Earth Syst. Sci., 22, 207–212, https://doi.org/10.5194/nhess-22-207-2022, https://doi.org/10.5194/nhess-22-207-2022, 2022
Short summary
Short summary
In the Apulia region (southeastern Italy) we are monitoring a soft-rock coastal cliff using webcams and strain sensors. In this urban and touristic area, coastal recession is extremely rapid and rockfalls are very frequent. In our work we are using low-cost and open-source hardware and software, trying to correlate both meteorological information with measures obtained from crack meters and webcams, aiming to recognize potential precursor signals that could be triggered by instability phenomena.
Natalie Brožová, Tommaso Baggio, Vincenzo D'Agostino, Yves Bühler, and Peter Bebi
Nat. Hazards Earth Syst. Sci., 21, 3539–3562, https://doi.org/10.5194/nhess-21-3539-2021, https://doi.org/10.5194/nhess-21-3539-2021, 2021
Short summary
Short summary
Surface roughness plays a great role in natural hazard processes but is not always well implemented in natural hazard modelling. The results of our study show how surface roughness can be useful in representing vegetation and ground structures, which are currently underrated. By including surface roughness in natural hazard modelling, we could better illustrate the processes and thus improve hazard mapping, which is crucial for infrastructure and settlement planning in mountainous areas.
Hugues Brenot, Nicolas Theys, Lieven Clarisse, Jeroen van Gent, Daniel R. Hurtmans, Sophie Vandenbussche, Nikolaos Papagiannopoulos, Lucia Mona, Timo Virtanen, Andreas Uppstu, Mikhail Sofiev, Luca Bugliaro, Margarita Vázquez-Navarro, Pascal Hedelt, Michelle Maree Parks, Sara Barsotti, Mauro Coltelli, William Moreland, Simona Scollo, Giuseppe Salerno, Delia Arnold-Arias, Marcus Hirtl, Tuomas Peltonen, Juhani Lahtinen, Klaus Sievers, Florian Lipok, Rolf Rüfenacht, Alexander Haefele, Maxime Hervo, Saskia Wagenaar, Wim Som de Cerff, Jos de Laat, Arnoud Apituley, Piet Stammes, Quentin Laffineur, Andy Delcloo, Robertson Lennart, Carl-Herbert Rokitansky, Arturo Vargas, Markus Kerschbaum, Christian Resch, Raimund Zopp, Matthieu Plu, Vincent-Henri Peuch, Michel Van Roozendael, and Gerhard Wotawa
Nat. Hazards Earth Syst. Sci., 21, 3367–3405, https://doi.org/10.5194/nhess-21-3367-2021, https://doi.org/10.5194/nhess-21-3367-2021, 2021
Short summary
Short summary
The purpose of the EUNADICS-AV (European Natural Airborne Disaster Information and Coordination System for Aviation) prototype early warning system (EWS) is to develop the combined use of harmonised data products from satellite, ground-based and in situ instruments to produce alerts of airborne hazards (volcanic, dust, smoke and radionuclide clouds), satisfying the requirement of aviation air traffic management (ATM) stakeholders (https://cordis.europa.eu/project/id/723986).
Johnny Douvinet, Anna Serra-Llobet, Esteban Bopp, and G. Mathias Kondolf
Nat. Hazards Earth Syst. Sci., 21, 2899–2920, https://doi.org/10.5194/nhess-21-2899-2021, https://doi.org/10.5194/nhess-21-2899-2021, 2021
Short summary
Short summary
This study proposes to combine results of research regarding the spatial inequalities due to the siren coverage, the political dilemma of siren activation, and the social problem of siren awareness and trust for people in France. Surveys were conducted using a range of complementary methods (GIS analysis, statistical analysis, questionnaires, interviews) through different scales. Results show that siren coverage in France is often determined by population density but not risks or disasters.
Fabio Brighenti, Francesco Carnemolla, Danilo Messina, and Giorgio De Guidi
Nat. Hazards Earth Syst. Sci., 21, 2881–2898, https://doi.org/10.5194/nhess-21-2881-2021, https://doi.org/10.5194/nhess-21-2881-2021, 2021
Short summary
Short summary
In this paper we propose a methodology to mitigate hazard in a natural environment in an urbanized context. The deformation of the ground is a precursor of paroxysms in mud volcanoes. Therefore, through the analysis of the deformation supported by a statistical approach, this methodology was tested to reduce the hazard around the mud volcano. In the future, the goal is that this dangerous area will become both a naturalistic heritage and a source of development for the community of the area.
Doris Hermle, Markus Keuschnig, Ingo Hartmeyer, Robert Delleske, and Michael Krautblatter
Nat. Hazards Earth Syst. Sci., 21, 2753–2772, https://doi.org/10.5194/nhess-21-2753-2021, https://doi.org/10.5194/nhess-21-2753-2021, 2021
Short summary
Short summary
Multispectral remote sensing imagery enables landslide detection and monitoring, but its applicability to time-critical early warning is rarely studied. We present a concept to operationalise its use for landslide early warning, aiming to extend lead time. We tested PlanetScope and unmanned aerial system images on a complex mass movement and compared processing times to historic benchmarks. Acquired data are within the forecasting window, indicating the feasibility for landslide early warning.
Michal Bíl, Pavel Raška, Lukáš Dolák, and Jan Kubeček
Nat. Hazards Earth Syst. Sci., 21, 2581–2596, https://doi.org/10.5194/nhess-21-2581-2021, https://doi.org/10.5194/nhess-21-2581-2021, 2021
Short summary
Short summary
The online landslide database CHILDA (Czech Historical Landslide Database) summarises information about landslides which occurred in the area of Czechia (the Czech Republic). The database is freely accessible via the https://childa.cz/ website. It includes 699 records (spanning the period of 1132–1989). Overall, 55 % of all recorded landslide events occurred only within 15 years of the extreme landslide incidence.
Anna Kruspe, Jens Kersten, and Friederike Klan
Nat. Hazards Earth Syst. Sci., 21, 1825–1845, https://doi.org/10.5194/nhess-21-1825-2021, https://doi.org/10.5194/nhess-21-1825-2021, 2021
Short summary
Short summary
Messages on social media can be an important source of information during crisis situations. This article reviews approaches for the reliable detection of informative messages in a flood of data. We demonstrate the varying goals of these approaches and present existing data sets. We then compare approaches based (1) on keyword and location filtering, (2) on crowdsourcing, and (3) on machine learning. We also point out challenges and suggest future research.
Enrique Guillermo Cordaro, Patricio Venegas-Aravena, and David Laroze
Nat. Hazards Earth Syst. Sci., 21, 1785–1806, https://doi.org/10.5194/nhess-21-1785-2021, https://doi.org/10.5194/nhess-21-1785-2021, 2021
Short summary
Short summary
We developed a methodology that generates free externally disturbed magnetic variations in ground magnetometers close to the Chilean convergent margin. Spectral analysis (~ mHz) and magnetic anomalies increased prior to large Chilean earthquakes (Maule 2010, Mw 8.8; Iquique 2014, Mw 8.2; Illapel 2015, Mw 8.3). These findings relate to microcracks within the lithosphere due to stress state changes. This physical evidence should be thought of as a last stage of the earthquake preparation process.
Corey M. Scheip and Karl W. Wegmann
Nat. Hazards Earth Syst. Sci., 21, 1495–1511, https://doi.org/10.5194/nhess-21-1495-2021, https://doi.org/10.5194/nhess-21-1495-2021, 2021
Short summary
Short summary
For many decades, natural disasters have been monitored by trained analysts using multiple satellite images to observe landscape change. This approach is incredibly useful, but our new tool, HazMapper, offers researchers and the scientifically curious public a web-accessible
cloud-based tool to perform similar analysis. We intend for the tool to both be used in scientific research and provide rapid response to global natural disasters like landslides, wildfires, and volcanic eruptions.
Matti Wiegmann, Jens Kersten, Hansi Senaratne, Martin Potthast, Friederike Klan, and Benno Stein
Nat. Hazards Earth Syst. Sci., 21, 1431–1444, https://doi.org/10.5194/nhess-21-1431-2021, https://doi.org/10.5194/nhess-21-1431-2021, 2021
Short summary
Short summary
In this paper, we study when social media is an adequate source to find metadata about incidents that cannot be acquired by traditional means. We identify six major use cases: impact assessment and verification of model predictions, narrative generation, recruiting citizen volunteers, supporting weakly institutionalized areas, narrowing surveillance areas, and reporting triggers for periodical surveillance.
Hui Liu, Ya Hao, Wenhao Zhang, Hanyue Zhang, Fei Gao, and Jinping Tong
Nat. Hazards Earth Syst. Sci., 21, 1179–1194, https://doi.org/10.5194/nhess-21-1179-2021, https://doi.org/10.5194/nhess-21-1179-2021, 2021
Short summary
Short summary
We trained a recurrent neural network model to classify microblogging posts related to urban waterlogging and establish an online monitoring system of urban waterlogging caused by flood disasters. We manually curated more than 4400 waterlogging posts to train the RNN model so that it can precisely identify waterlogging-related posts of Sina Weibo to timely determine urban waterlogging.
Roope Tervo, Ilona Láng, Alexander Jung, and Antti Mäkelä
Nat. Hazards Earth Syst. Sci., 21, 607–627, https://doi.org/10.5194/nhess-21-607-2021, https://doi.org/10.5194/nhess-21-607-2021, 2021
Short summary
Short summary
Predicting the number of power outages caused by extratropical storms is a key challenge for power grid operators. We introduce a novel method to predict the storm severity for the power grid employing ERA5 reanalysis data combined with a forest inventory. The storms are first identified from the data and then classified using several machine-learning methods. While there is plenty of room to improve, the results are already usable, with support vector classifier providing the best performance.
Michaela Wenner, Clément Hibert, Alec van Herwijnen, Lorenz Meier, and Fabian Walter
Nat. Hazards Earth Syst. Sci., 21, 339–361, https://doi.org/10.5194/nhess-21-339-2021, https://doi.org/10.5194/nhess-21-339-2021, 2021
Short summary
Short summary
Mass movements constitute a risk to property and human life. In this study we use machine learning to automatically detect and classify slope failure events using ground vibrations. We explore the influence of non-ideal though commonly encountered conditions: poor network coverage, small number of events, and low signal-to-noise ratios. Our approach enables us to detect the occurrence of rare events of high interest in a large data set of more than a million windowed seismic signals.
Luiz Felipe Galizia, Thomas Curt, Renaud Barbero, and Marcos Rodrigues
Nat. Hazards Earth Syst. Sci., 21, 73–86, https://doi.org/10.5194/nhess-21-73-2021, https://doi.org/10.5194/nhess-21-73-2021, 2021
Short summary
Short summary
This paper aims to provide a quantitative evaluation of three remotely sensed fire datasets which have recently emerged as an important resource to improve our understanding of fire regimes. Our findings suggest that remotely sensed fire datasets can be used to proxy variations in fire activity on monthly and annual timescales; however, caution is advised when drawing information from smaller fires (< 100 ha) across the Mediterranean region.
Philippe Weyrich, Anna Scolobig, Florian Walther, and Anthony Patt
Nat. Hazards Earth Syst. Sci., 20, 2811–2821, https://doi.org/10.5194/nhess-20-2811-2020, https://doi.org/10.5194/nhess-20-2811-2020, 2020
Patric Kellermann, Kai Schröter, Annegret H. Thieken, Sören-Nils Haubrock, and Heidi Kreibich
Nat. Hazards Earth Syst. Sci., 20, 2503–2519, https://doi.org/10.5194/nhess-20-2503-2020, https://doi.org/10.5194/nhess-20-2503-2020, 2020
Short summary
Short summary
The flood damage database HOWAS 21 contains object-specific flood damage data resulting from fluvial, pluvial and groundwater flooding. The datasets incorporate various variables of flood hazard, exposure, vulnerability and direct tangible damage at properties from several economic sectors. This paper presents HOWAS 21 and highlights exemplary analyses to demonstrate the use of HOWAS 21 flood damage data.
Giuseppe Esposito, Ivan Marchesini, Alessandro Cesare Mondini, Paola Reichenbach, Mauro Rossi, and Simone Sterlacchini
Nat. Hazards Earth Syst. Sci., 20, 2379–2395, https://doi.org/10.5194/nhess-20-2379-2020, https://doi.org/10.5194/nhess-20-2379-2020, 2020
Short summary
Short summary
In this article, we present an automatic processing chain aimed to support the detection of landslides that induce sharp land cover changes. The chain exploits free software and spaceborne SAR data, allowing the systematic monitoring of wide mountainous regions exposed to mass movements. In the test site, we verified a general accordance between the spatial distribution of seismically induced landslides and the detected land cover changes, demonstrating its potential use in emergency management.
Cited articles
Alcantara, A. L. and Ahn, K. H.: Probability distribution and characterization of daily precipitation related to tropical cyclones over the Korean Peninsula, Water, 12, 1214, https://doi.org/10.3390/w12041214, 2020.
Alcántara-Ayala, I. and Sassa, K.: Landslide risk management: from hazard to disaster risk reduction, Landslides, 20, 2031–2037, https://doi.org/10.1007/s10346-023-02140-5, 2023.
Amesoeder, C., Hartig, F., and Pichler, M.: cito: An R package for training neural networks using torch, Ecography, 2024, e07143, https://doi.org/10.1111/ecog.07143, 2024.
Armstrong, J. S.: Combining forecasts, Springer US, 417–439, https://doi.org/10.1007/978-0-306-47630-3_19, 2001.
Asada, H. and Minagawa, T.: Impact of vegetation differences on shallow landslides: a case study in Aso, Japan, Water, 15, 3193, https://doi.org/10.3390/w15183193, 2023.
Bernardie, S., Desramaut, N., Malet, J.-P., Gourlay, M., and Grandjean, G.: Prediction of changes in landslide rates induced by rainfall, Landslides, 12, 481–494, https://doi.org/10.1007/s10346-014-0495-8, 2014.
Bonamutial, M. and Prasetyo, S. Y.: Exploring the Impact of Feature Data Normalization and Standardization on Regression Models for Smartphone Price Prediction, in: 2023 International Conference on Information Management and Technology (ICIMTech), 24–25 August 2023, Malang, Indonesia, IEEE, 294–298, https://doi.org/10.1109/ICIMTech59029.2023.10277860, 2023.
Borup, D., Christensen, B. J., Mühlbach, N. S., and Nielsen, M. S.: Targeting predictors in random forest regression, Int. J. Forecast., 39, 841–868, https://doi.org/10.1016/j.ijforecast.2022.02.010, 2023.
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001.
Breiman, L.: Classification and regression trees, Routledge, https://doi.org/10.1201/9781315139470, 2017.
Caine, N.: The rainfall intensity-duration control of shallow landslides and debris flows, Geogr. Ann. A, 62, 23–27, https://doi.org/10.1080/04353676.1980.11879996, 1980.
Cellek, S.: The effect of aspect on landslide and its relationship with other parameters, Landslides, IntechOpen, https://doi.org/10.5772/intechopen.99389, 2021.
Chang, K. T. and Chiang, S. H.: An integrated model for predicting rainfall-induced landslides, Geomorphology, 105, 366–373, https://doi.org/10.1016/j.geomorph.2008.10.012, 2009.
Chang, K. T., Chiang, S. H., and Lei, F.: Analysing the relationship between typhoon-triggered landslides and critical rainfall conditions, Earth Surf. Process. Landf., 33, 1261–1271, https://doi.org/10.1002/esp.1611, 2008.
Chatra, A. S., Dodagoudar, G. R., and Maji, V. B.: Numerical modelling of rainfall effects on the stability of soil slopes, Int. J. Geotech. Eng., 13, 425–437, https://doi.org/10.1080/19386362.2017.1359912, 2019.
Chen, C. W., Oguchi, T., Hayakawa, Y. S., Saito, H., and Chen, H.: Relationship between landslide size and rainfall conditions in Taiwan, Landslides, 14, 1235–1240, https://doi.org/10.1007/s10346-016-0790-7, 2017.
Chen, L., Guo, Z., Yin, K., Shrestha, D. P., and Jin, S.: The influence of land use and land cover change on landslide susceptibility: a case study in Zhushan Town, Xuan'en County (Hubei, China), Nat. Hazards Earth Syst. Sci., 19, 2207–2228, https://doi.org/10.5194/nhess-19-2207-2019, 2019.
Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., Li, M., Xie, J., Lin, M., Geng, Y., Li, Y., and Yuan, J.: xgboost: Extreme Gradient Boosting, R package version 1.6.0.1, https://CRAN.R-project.org/package=xgboost (last access: 25 January 2025), 2022.
Chen, X., Zhang, L., Zhang, L., Zhou, Y., Ye, G., and Guo, N.: Modelling rainfall-induced landslides from initiation of instability to post-failure, Comput. Geotech., 129, 103877, https://doi.org/10.1016/j.compgeo.2020.103877, 2021.
Chen, Z., Luo, R., Huang, Z., Tu, W., Chen, J., Li, W., Chen, S., Xiao, J. and Ai, Y.: Effects of different backfill soils on artificial soil quality for cut slope revegetation: Soil structure, soil erosion, moisture retention and soil C stock, Ecol. Eng., 83, 5–12, https://doi.org/10.1016/j.ecoleng.2015.05.048, 2015.
Cheung, R. W.: Landslide risk management in Hong Kong, Landslides, 18, 3457–3473, https://doi.org/10.1007/s10346-020-01587-0, 2021.
Chicco, D., Warrens, M. J., and Jurman, G.: The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation, PeerJ Comput. Sci., 7, e623, https://doi.org/10.7717/peerj-cs.623, 2021.
Chowdhury, M. Z. I., Leung, A. A., Walker, R. L., Sikdar, K. C., O'Beirne, M., Quan, H., and Turin, T. C.: A comparison of machine learning algorithms and traditional regression-based statistical modeling for predicting hypertension incidence in a Canadian population, Sci. Rep., 13, 13, https://doi.org/10.1038/s41598-022-27264-x, 2023.
Chung, C. J. F. and Fabbri, A. G.: Validation of spatial prediction models for landslide hazard mapping, Nat. Hazards, 30, 451–472, https://doi.org/10.1023/B:NHAZ.0000007172.62651.2b, 2003.
Cohen, D. and Schwarz, M.: Tree-root control of shallow landslides, Earth Surf. Dynam., 5, 451–477, https://doi.org/10.5194/esurf-5-451-2017, 2017.
Culler, E. S., Livneh, B., Rajagopalan, B., and Tiampo, K. F.: A data-driven evaluation of post-fire landslide susceptibility, Nat. Hazards Earth Syst. Sci., 23, 1631–1652, https://doi.org/10.5194/nhess-23-1631-2023, 2023.
Dahal, B. K. and Dahal, R. K.: Landslide hazard map: tool for optimization of low-cost mitigation, Geoenvironmental Disasters, 4, 1–9, https://doi.org/10.1186/s40677-017-0071-3, 2017.
Dai, F. C. and Lee, C. F.: Frequency–volume relation and prediction of rainfall-induced landslides, Eng. Geol., 59, 253–266, https://doi.org/10.1016/S0013-7952(00)00077-6, 2001.
Darlington, R. B.: Regression and linear models, Mcgraw-Hill, New York, USA, ISBN 9780070153721, 1990.
Dinno, A.: Nonparametric pairwise multiple comparisons in independent groups using Dunn's test, Stata J., 15, 292–300, https://doi.org/10.1177/1536867X1501500117, 2015.
Dobson, A. J. and Barnett, A. G.: An introduction to generalized linear models, CRC press, New York, USA, ISBN 9781315182780, https://doi.org/10.1201/9781315182780, 2018.
Donnarumma, A., Revellino, P., Grelle, G., and Guadagno, F. M.: Slope angle as indicator parameter of landslide susceptibility in a geologically complex area, Landslide Science and Practice: Volume 1: Landslide Inventory and Susceptibility and Hazard Zoning, Springer, Berlin, 425–433, https://doi.org/10.1007/978-3-642-31325-7_56, 2013.
Duc, D. M.: Rainfall-triggered large landslides on 15 December 2005 in Van Canh district, Binh Dinh province, Vietnam, Landslides, 10, 219–230, https://doi.org/10.1007/s10346-012-0362-4, 2013.
Dudley, R.: The Shapiro–Wilk test for normality, MIT Mathematics, https://math.mit.edu/~rmd/46512/shapiro.pdf (last access: 25 January 2025), 2023.
Evans, S., Mugnozza, G. S., Strom, A., Hermanns, R., Ischuk, A., and Vinnichenko, S.: Landslides From Massive Rock Slope Failure And Associated Phenomena, in: Landslides from Massive Rock Slope Failure, NATO Science Series, vol. 49, Springer, Dordrecht, https://doi.org/10.1007/978-1-4020-4037-5_1, 2006.
Friedman, J. H., Hastie, T., and Tibshirani, R.: Regularization paths for generalized linear models via coordinate descent, J. Stat. Softw., 33, 1–22, https://pmc.ncbi.nlm.nih.gov/articles/PMC2929880/ (last access: 25 January 2025), 2010.
Gariano, S. L., Rianna, G., Petrucci, O., and Guzzetti, F.: Assessing future changes in the occurrence of rainfall-induced landslides at a regional scale, Sci. Total Environ., 596, 417–426, https://doi.org/10.1016/j.scitotenv.2017.03.103, 2017.
Gelman, A. and Hill, J.: Data analysis using regression and multilevel/hierarchical models, Cambridge University Press, New York, ISBN 0-521-86706-1, 2007.
Glade, T., Anderson, M. G., and Crozier, M. J.: Landslide hazard and risk, Vol. 807, John Wiley & Sons, ISBN 9780470012659, https://doi.org/10.1002/9780470012659, 2005.
Gong, Q., Wang, J., Zhou, P., and Guo, M.: A regional landslide stability analysis method under the combined impact of rainfall and vegetation roots in south China, Adv. Civ. Eng., 2021, 5512281, https://doi.org/10.1155/2021/5512281, 2021.
Gonzalez-Ollauri, A. and Mickovski, S. B.: Hydrological effect of vegetation against rainfall-induced landslides, J. Hydrol., 549, 374–387, https://doi.org/10.1016/j.jhydrol.2017.04.014, 2017.
Greenwood, J. R., Norris, J. E., and Wint, J.: Assessing the contribution of vegetation to slope stability, Proc. Inst. Civil Eng. Geotech. Eng., 157, 199–207, https://doi.org/10.1680/geng.2004.157.4.199, 2004.
Gutierrez-Martin, A.: A GIS-physically-based emergency methodology for predicting rainfall-induced shallow landslide zonation, Geomorphology, 359, 107121, https://doi.org/10.1016/j.geomorph.2020.107121, 2020.
Guzzetti, F., Peruccacci, S., Rossi, M., and Stark, C. P.: The rainfall intensity–duration control of shallow landslides and debris flows: an update, Landslides, 5, 3–17, https://doi.org/10.1007/s10346-007-0112-1, 2008.
Ha, K. M.: Predicting typhoon tracks around Korea, Nat. Hazards, 113, 1385–1390, https://doi.org/10.1007/s11069-022-05335-6, 2022.
Hastie, T.: The elements of statistical learning: data mining, inference, and prediction, 2nd edn., Springer, New York, ISBN 9780387848570, https://doi.org/10.1111/j.1541-0420.2010.01516.x, 2009.
Highland, L. and Bobrowsky, P.: The Landslide Handbook: A Guide to Understanding Landslides, USGS, Reston, VA, Circular 1325, https://pubs.usgs.gov/circ/1325/ (last access: 25 January 2025), 2008.
Holcombe, E. A., Beesley, M. E., Vardanega, P. J., and Sorbie, R.: Urbanisation and landslides: hazard drivers and better practices, Proc. Inst. Civ. Eng. Civ. Eng., Thomas Telford Ltd, 169, 137–144, https://doi.org/10.1680/jcien.15.00044, 2016.
Hovius, N., Stark, C. P., and Allen, P. A.: Sediment flux from a mountain belt derived by landslide mapping, Geology, 25, 231–234, https://doi.org/10.1130/0091-7613(1997)025<0231:SFFAMB>2.3.CO;2, 1997.
Huang, J., Hales, T. C., Huang, R., Ju, N., Li, Q., and Huang, Y.: A hybrid machine-learning model to estimate potential debris-flow volumes, Geomorphology, 367, 107333, https://doi.org/10.1016/j.geomorph.2020.107333, 2020.
Hyde, K. D., Riley, K., and Stoof, C.: Uncertainties in predicting debris flow hazards following wildfire, in: Natural hazard uncertainty assessment: Modeling and decision support, edited by: Riley, K., Webley, P., and Thompson, M., Geophysical Monograph 223, John Wiley and Sons, Inc., 287–299, https://doi.org/10.1002/9781119028116.ch19, 2016.
Hyndman, R. J. and Koehler, A. B.: Another look at measures of forecast accuracy, Int. J. Forecast., 22, 679–688, https://doi.org/10.1016/j.ijforecast.2006.03.001, 2006.
Hyun, Y. K., Kar, S. K., Ha, K. J., and Lee, J. H.: Diurnal and spatial variabilities of monsoonal CG lightning and precipitation and their association with the synoptic weather conditions over South Korea, Theor. Appl. Climatol., 102, 43–60, https://doi.org/10.1007/s00704-009-0235-5, 2010.
Intrieri, E., Carlà, T., and Gigli, G.: Forecasting the time of failure of landslides at slope-scale: A literature review, Earth-Sci. Rev., 193, 333–349, https://doi.org/10.1016/j.earscirev.2019.03.019, 2019.
Jaboyedoff, M., Choffet, M., Derron, M. H., Horton, P., Loye, A., Longchamp, C., Mazotti, B., Michoud, C., and Pedrazzini, A.: Preliminary slope mass movement susceptibility mapping using DEM and LiDAR DEM, in: Terrigenous mass movements: Detection, modelling, early warning and mitigation using geoinformation technology, Springer, Berlin Heidelberg, 109–170, https://doi.org/10.1007/978-3-642-25495-6_5, 2012.
Jin, H. G., Lee, H., and Baik, J. J.: Characteristics and possible mechanisms of diurnal variation of summertime precipitation in South Korea, Theor. Appl. Climatol., 148, 551–568, https://doi.org/10.1007/s00704-022-03965-1, 2022.
Ju, L. Y., Zhang, L. M., and Xiao, T.: Power laws for accurate determination of landslide volume based on high-resolution LiDAR data, Eng. Geol., 312, 106935, https://doi.org/10.1016/j.enggeo.2022.106935, 2023.
Jung, M. J., Jeong, Y. J., Shin, W. J., and Cheong, A. C. S.: Isotopic distribution of bioavailable Sr, Nd, and Pb in Chungcheongbuk-do Province, Korea, J. Anal. Sci. Technol., 15, 46, https://doi.org/10.1186/s40543-024-00460-2, 2024.
Jung, Y., Shin, J. Y., Ahn, H., and Heo, J. H.: The spatial and temporal structure of extreme rainfall trends in South Korea, Water, 9, 809, https://doi.org/10.3390/w9100809, 2017.
Kafle, L., Xu, W. J., Zeng, S. Y., and Nagel, T.: A numerical investigation of slope stability influenced by the combined effects of reservoir water level fluctuations and precipitation: A case study of the Bianjiazhai landslide in China, Eng. Geol., 297, 106508, https://doi.org/10.1016/j.enggeo.2021.106508, 2022.
Kang, M. W., Yibeltal, M., Kim, Y. H., Oh, S. J., Lee, J. C., Kwon, E. E., and Lee, S. S.: Enhancement of soil physical properties and soil water retention with biochar-based soil amendments, Sci. Total Environ., 836, 155746, https://doi.org/10.1016/j.scitotenv.2022.155746, 2022.
Keefer, R. F.: Handbook of soils for landscape architects, Oxford University Press, ISBN 0-19-51202-3, 2000.
Khan, M. A., Basharat, M., Riaz, M. T., Sarfraz, Y., Farooq, M., Khan, A. Y., Pham, Q. B., Ahmed, K. S., and Shahzad, A.: An integrated geotechnical and geophysical investigation of a catastrophic landslide in the Northeast Himalayas of Pakistan, Geol. J., 56, 4760–4778, https://doi.org/10.1002/gj.4209, 2021.
Khan, Y. A., Lateh, H., Baten, M. A., and Kamil, A. A.: Critical antecedent rainfall conditions for shallow landslides in Chittagong City of Bangladesh, Environ. Earth Sci., 67, 97–106, https://doi.org/10.1007/s12665-011-1483-0, 2012.
Kim, D. E., Seong, Y. B., Weber, J., and Yu, B. Y.: Unsteady migration of Taebaek Mountain drainage divide, Cenozoic extensional basin margin, Korean Peninsula, Geomorphology, 352, 107012, https://doi.org/10.1016/j.geomorph.2019.107012, 2020.
Kim, H. G. and Park, C. Y.: Landslide susceptibility analysis of photovoltaic power stations in Gangwon-do, Republic of Korea, Geomat. Nat. Hazards Risk., 12, 2328–2351, https://doi.org/10.1080/19475705.2021.1950219, 2021.
Kim, J., Lee, K., Jeong, S., and Kim, G.: GIS-based prediction method of landslide susceptibility using a rainfall infiltration-groundwater flow model, Eng. Geol., 182, 63–78, https://doi.org/10.1016/j.enggeo.2014.09.001, 2014.
Kim, M. S., Onda, Y., Kim, J. K., and Kim, S. W.: Effect of topography and soil parameterisation representing soil thicknesses on shallow landslide modelling, Quat. Int., 384, 91–106, https://doi.org/10.1016/j.quaint.2015.03.057, 2015.
Kim, S. W., Chun, K. W., Kim, M., Catani, F., Choi, B., and Seo, J. I.: Effect of antecedent rainfall conditions and their variations on shallow landslide-triggering rainfall thresholds in South Korea, Landslides, 18, 569–582, https://doi.org/10.1007/s10346-020-01505-4, 2021.
Kitutu, M. G., Muwanga, A., Poesen, J., and Deckers, J. A.: Influence of soil properties on landslide occurrences in Bududa district, Eastern Uganda, Afr. J. Agric. Res., 4, 611–620, https://lirias.kuleuven.be/retrieve/78489 (last access: 25 January 2025), 2009.
Korup, O.: Geomorphometric characteristics of New Zealand landslide dams, Eng. Geol., 73, 13–35, https://doi.org/10.1016/j.enggeo.2003.11.003, 2004.
Korup, O., Clague, J. J., Hermanns, R. L., Hewitt, K., Strom, A. L., and Weidinger, J. T.: Giant landslides, topography, and erosion, Earth Planet. Sci. Lett., 261, 578–589, https://doi.org/10.1016/j.epsl.2007.07.025, 2007.
Kotsakis, C.: Ordinary Least Squares, in: Encyclopedia of Mathematical Geosciences, Springer, Cham, 1032–1038, https://doi.org/10.1007/978-3-030-85040-1_237, 2023.
Kramer, O. and Kramer, O.: K-nearest neighbors, Dimensionality reduction with unsupervised nearest neighbors, Intelligent Systems Reference Library, Springer, Berlin, Heidelberg, 51, 13–23, https://doi.org/10.1007/978-3-642-38652-7_2, 2013.
Krizhevsky, A., Sutskever, I., and Hinton, G. E.: Imagenet classification with deep convolutional neural networks, Adv. Neural Inf. Process. Syst., 25, 1097–1105, https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf (last access: 25 January 2025), 2012.
Kuhn, M.: caret: Classification and Regression Training, R package, version 6.0-92, Comprehensive R Archive Network (CRAN), https://CRAN.R-project.org/package=caret (last access: 25 January 2025), 2022.
Kunz, M. and Kottmeier, C.: Orographic enhancement of precipitation over low mountain ranges, Part II: Simulations of heavy precipitation events over southwest Germany, J. Appl. Meteorol. Clim., 45, 1041–1055, https://doi.org/10.1175/JAM2390.1, 2006.
Lacerda, W. A., Palmeira, E. M., Netto, A. L. C., and Ehrlich, M. (Eds.): Extreme rainfall induced landslides: an international perspective, Oficina de Textos, ISBN 978-85-7975-150-9, 2014.
Lann, T., Bao, H., Lan, H., Zheng, H., and Yan, C.: Hydro-mechanical effects of vegetation on slope stability: A review, Sci. Total Environ., 926, 171691, https://doi.org/10.1016/j.scitotenv.2024.171691, 2024.
LeCun, Y., Bengio, Y., and Hinton, G.: Deep learning, Nature, 521, 436–444, https://doi.org/10.1038/nature14539, 2015.
Lee, D. B., Kim, Y. N., Sonn, Y. K., and Kim, K. H.: Comparison of Soil Taxonomy (2022) and WRB (2022) Systems for classifying Paddy Soils with different drainage grades in South Korea, Land, 12, 1204, https://doi.org/10.3390/land12061204, 2023.
Lee, D. H., Kim, Y. T., and Lee, S. R.: Shallow landslide susceptibility models based on artificial neural networks considering the factor selection method and various non-linear activation functions, J. Remote Sens., 12, 1194, https://doi.org/10.3390/rs12071194, 2020.
Lee, D. H., Cheon, E., Lim, H. H., Choi, S. K., Kim, Y. T., and Lee, S. R.: An artificial neural network model to predict debris-flow volumes caused by extreme rainfall in the central region of South Korea, Eng. Geol., 281, 105979, https://doi.org/10.1016/j.enggeo.2020.105979, 2021.
Lee, J. U., Cho, Y. C., Kim, M., Jang, S. J., Lee, J., and Kim, S.: The effects of different geological conditions on landslide-triggering rainfall conditions in South Korea, Water, 14, 2051, https://doi.org/10.3390/w14132051, 2022.
Lee, M. J.: Rainfall and landslide correlation analysis and prediction of future rainfall base on climate change, in: Geohazards Caused by Human Activity, IntechOpen, https://doi.org/10.5772/64694, 2016.
Lee, S. W., Kim, G., Yune, C. Y., and Ryu, H. J.: Development of landslide-risk assessment model for mountainous regions in eastern Korea, Disaster Adv., 6, 70–79, 2013.
Li, C. J., Guo, C. X., Yang, X. G., Li, H. B., and Zhou, J. W.: A GIS-based probabilistic analysis model for rainfall-induced shallow landslides in mountainous areas, Environ. Earth Sci., 81, 432, https://doi.org/10.1007/s12665-022-10562-y, 2022.
Liaw, A. and Wiener, M.: Classification and regression by randomForest, R News 2, 18–22, https://journal.r-project.org/articles/RN-2002-022/RN-2002-022.pdf (last access: 24 January 2025), 2002.
Liu, Y., Deng, Z., and Wang, X.: The effects of rainfall, soil type and slope on the processes and mechanisms of rainfall-induced shallow landslides, Appl. Sci., 11, 11652, https://doi.org/10.3390/app112411652, 2021a.
Liu, Z., Gilbert, G., Cepeda, J. M., Lysdahl, A. O. K., Piciullo, L., Hefre, H., and Lacasse, S.: Modelling of shallow landslides with machine learning algorithms, Geosci. Front., 12, 385–393, https://doi.org/10.1016/j.gsf.2020.04.014, 2021b.
Luino, F., De Graff, J., Biddoccu, M., Faccini, F., Freppaz, M., Roccati, A., Ungaro, F., D'Amico, M., and Turconi, L.: The Role of soil type in triggering shallow landslides in the alps (Lombardy, Northern Italy), Land, 11, 1125, https://doi.org/10.3390/land11081125, 2022.
Martinović, K., Gavin, K., Reale, C., and Mangan, C.: Rainfall thresholds as a landslide indicator for engineered slopes on the Irish Rail network, Geomorphology, 306, 40–50, https://doi.org/10.1016/j.geomorph.2018.01.006, 2018.
McKenna, J. P., Santi, P. M., Amblard, X., and Negri, J.: Effects of soil-engineering properties on the failure mode of shallow landslides, Landslides, 9, 215–228, https://doi.org/10.1007/s10346-011-0295-3, 2012.
McKight, P. E. and Najab, J.: Kruskal-wallis test, The corsini encyclopedia of psychology, 1, 1–10, https://doi.org/10.1002/9780470479216.corpsy0491, 2010.
Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., and Leisch, F.: e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien, R package version 1.7-9, https://doi.org/10.32614/CRAN.package.e1071, 2021.
Miao, F., Wu, Y., Xie, Y., and Li, Y.: Prediction of landslide displacement with step-like behavior based on multialgorithm optimization and a support vector regression model, Landslides, 15, 475–488, https://doi.org/10.1007/s10346-017-0883-y, 2018.
Montgomery, D. R., Schmidt, K. M., Dietrich, W. E., and McKean, J.: Instrumental record of debris flow initiation during natural rainfall: Implications for modeling slope stability, J. Geophys. Res.-Earth Surf., 114, F01031, https://doi.org/10.1029/2008JF001078, 2009.
Nguyen, Q. H., Ly, H. B., Ho, L. S., Al-Ansari, N., Le, H. V., Tran, V. Q., Prakash, I., and Pham, B. T.: Influence of data splitting on performance of machine learning models in prediction of shear strength of soil, Math. Probl. Eng., 2021, 4832864, https://doi.org/10.1155/2021/4832864, 2021.
O'Brien, R. M.: A caution regarding rules of thumb for variance inflation factors, Qual. Quant., 41, 673–690, https://doi.org/10.1007/s11135-006-9018-6, 2007.
Omwega, A. K.: Crop cover, rainfall energy and soil erosion in Githunguri (Kiambu District), Kenya, The University of Manchester (United Kingdom), https://www.proquest.com/openview/dd7c169f804775d18041ec262d03e4c1/1?cbl=2026366&diss=y&pq-origsite=gscholar (last access: 24 Janurary 2025), 1989.
Panday, S. and Dong, J. J.: Topographical features of rainfall-triggered landslides in Mon State, Myanmar, August 2019: Spatial distribution heterogeneity and uncommon large relative heights, Landslides, 18, 3875–3889, https://doi.org/10.1007/s10346-021-01758-7, 2021.
Park, C. Y.: The classification of extreme climate events in the Republic of Korea, J. Korean Assoc. Regional Geograp., 21, 394–410, https://koreascience.kr/article/JAKO201507740043627.page (last access: 25 January 2025), 2015.
Park, S. J. and Lee, D. K.: Predicting susceptibility to landslides under climate change impacts in metropolitan areas of South Korea using machine learning, Geomat. Nat. Hazards Risk. Risk, 12, 2462–2476, https://doi.org/10.1080/19475705.2021.1963328, 2021.
Pham, B. T., Tien Bui, D., and Prakash, I.: Bagging based support vector machines for spatial prediction of landslides, Environ. Earth Sci., 77, 1–17, https://doi.org/10.1007/s12665-018-7268-y, 2018.
Phillips, C., Hales, T., Smith, H., and Basher, L.: Shallow landslides and vegetation at the catchment scale: A perspective, Ecol. Eng., 173, 106436, https://doi.org/10.1016/j.ecoleng.2021.106436, 2021.
Pisner, D. A. and Schnyer, D. M.: Support vector machine, in: Machine learning, Academic Press, 101–121, https://doi.org/10.1016/B978-0-12-815739-8.00006-7, 2020.
Pourghasemi, H. R. and Rahmati, O.: Prediction of the landslide susceptibility: Which algorithm, which precision?, Catena, 162, 177–192, https://doi.org/10.1016/j.catena.2017.11.022, 2018.
Qiu, H., Regmi, A. D., Cui, P., Cao, M., Lee, J., and Zhu, X.: Size distribution of loess slides in relation to local slope height within different slope morphologies, Catena, 145, 155–163, https://doi.org/10.1016/j.catena.2016.06.005, 2016.
Rahman, M. S., Ahmed, B., and Di, L.: Landslide initiation and runout susceptibility modeling in the context of hill cutting and rapid urbanization: a combined approach of weights of evidence and spatial multi-criteria, J. Mt. Sci., 14, 1919–1937, https://doi.org/10.1007/s11629-016-4220-z, 2017.
Ran, Q., Wang, J., Chen, X., Liu, L., Li, J., and Ye, S.: The relative importance of antecedent soil moisture and precipitation in flood generation in the middle and lower Yangtze River basin, Hydrol. Earth Syst. Sci., 26, 4919–4931, https://doi.org/10.5194/hess-26-4919-2022, 2022.
Rathore, S. S. and Kumar, S.: A decision tree regression-based approach for the number of software faults prediction, ACM SIGSOFT, 41, 1–6, https://doi.org/10.1145/2853073.2853083, 2016.
Razakova, M., Kuzmin, A., Fedorov, I., Yergaliev, R., and Ainakulov, Z.: Methods of calculating landslide volume using remote sensing data, in: E3S Web of Conferences, EDP Sciences, 149, 02009, https://doi.org/10.1051/e3sconf/202014902009, 2020.
R Core Team: R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/ (last access: 24 January 2025), 2022.
Rosi, A., Peternel, T., Jemec-Auflič, M., Komac, M., Segoni, S., and Casagli, N.: Rainfall thresholds for rainfall-induced landslides in Slovenia, Landslides, 13, 1571–1577, https://doi.org/10.1007/s10346-016-0733-3, 2016.
Rotaru, A., Oajdea, D., and Răileanu, P.: Analysis of the landslide movements, Int. J. Coal Geol., 1, 70–79, https://naun.org/multimedia/NAUN/geology/ijgeo-10.pdf (last access: 24 January 2025), 2007.
Saito, H., Korup, O., Uchida, T., Hayashi, S., and Oguchi, T.: Rainfall conditions, typhoon frequency, and contemporary landslide erosion in Japan, Geology, 42, 999–1002, https://doi.org/10.1130/G35680.1, 2014.
Saleh, A. M. E., Arashi, M., and Kibria, B. G.: Theory of ridge regression estimation with applications, John Wiley and Sons, ISBN 9781118644614, 2019.
Sato, T., Katsuki, Y., and Shuin, Y.: Evaluation of influences of forest cover change on landslides by comparing rainfall-induced landslides in Japanese artificial forests with different ages, Sci. Rep., 13, 14258, https://doi.org/10.1038/s41598-023-41539-x, 2023.
Scheidl, C., Heiser, M., Kamper, S., Thaler, T., Klebinder, K., Nagl, F., Lechner, L., Markart, G., Rammer, W., and Seidl, R.: The influence of climate change and canopy disturbances on landslide susceptibility in headwater catchments, Sci. Total Environ., 742, 140588, https://doi.org/10.1016/j.scitotenv.2020.140588, 2020.
Seger, C.: An investigation of categorical variable encoding techniques in machine learning: binary versus one-hot and feature hashing, Kth Royal Institute of Technology, Sweden, https://www.diva-portal.org/smash/get/diva2:1259073/FULLTEXT01.pdf (last access: 24 January 2025), 2018.
Shirzadi, A., Shahabi, H., Chapi, K., Bui, D. T., Pham, B. T., Shahedi, K., and Ahmad, B. B.: A comparative study between popular statistical and machine learning methods for simulating volume of landslides, Catena, 157, 213–226, https://doi.org/10.1016/j.catena.2017.05.016, 2017.
Singh, D. and Singh, B.: Feature wise normalization: An effective way of normalizing data, Pattern Recogn., 122, 108307, https://doi.org/10.1016/j.patcog.2021.108307, 2022.
Smith, H. G., Neverman, A. J., Betts, H., and Spiekermann, R.: The influence of spatial patterns in rainfall on shallow landslides, Geomorphology, 437, 108795, https://doi.org/10.1016/j.geomorph.2023.108795, 2023.
Stoof, C. R., Vervoort, R. W., Iwema, J., van den Elsen, E., Ferreira, A. J. D., and Ritsema, C. J.: Hydrological response of a small catchment burned by experimental fire, Hydrol. Earth Syst. Sci., 16, 267–285, https://doi.org/10.5194/hess-16-267-2012, 2012.
Sun, H. Y., Wong, L. N. Y., Shang, Y. Q., Shen, Y. J., and Lü, Q.: Evaluation of drainage tunnel effectiveness in landslide control, Landslides, 7, 445–454, https://doi.org/10.1007/s10346-010-0210-3, 2010.
Székely, G. J., Rizzo, M. L., and Bakirov, N. K.: Measuring and testing dependence by correlation of distances, Ann. Statist., 35, 2769–2794, https://doi.org/10.1214/009053607000000505, 2007.
Tacconi Stefanelli, C., Casagli, N., and Catani, F.: Landslide damming hazard susceptibility maps: a new GIS-based procedure for risk management, Landslides, 17, 1635–1648, https://doi.org/10.1007/s10346-020-01395-6, 2020.
Tsai, T. L. and Chen, H. F.: Effects of degree of saturation on shallow landslides triggered by rainfall, Environ. Earth Sci., 59, 1285–1295, https://doi.org/10.1007/s12665-009-0116-3, 2010.
Turner, T. R., Duke, S. D., Fransen, B. R., Reiter, M. L., Kroll, A. J., Ward, J. W., Bach, J. L., Justice, T. E., and Bilby, R. E.: Landslide densities associated with rainfall, stand age, and topography on forested landscapes, southwestern Washington, USA, For. Ecol. Manag., 259, 2233–2247, https://doi.org/10.1016/j.foreco.2010.01.051, 2010.
Um, M. J., Yun, H., Cho, W., and Heo, J. H.: Analysis of orographic precipitation on Jeju-Island using regional frequency analysis and regression, Water Resour. Manag., 24, 1461–1487, https://doi.org/10.1007/s11269-009-9509-z, 2010.
Van Westen, C. J.: The modelling of landslide hazards using GIS, Surv. Geophys., 21, 241–255, https://doi.org/10.1023/A:1006794127521, 2000.
Wang, D., Hollaus, M., Schmaltz, E., Wieser, M., Reifeltshammer, D., and Pfeifer, N.: Tree stem shapes derived from TLS data as an indicator for shallow landslides, Proced. Earth Plan. Sc., 16, 185–194, https://doi.org/10.1016/j.proeps.2016.10.020, 2016.
Wei, Z. L., Shang, Y. Q., Sun, H. Y., Xu, H. D., and Wang, D. F.: The effectiveness of a drainage tunnel in increasing the rainfall threshold of a deep-seated landslide, Landslides, 16, 1731–1744, https://doi.org/10.1007/s10346-019-01241-4, 2019.
Wieczorek, G.: Debris flows/avalanches: process, recognition, and mitigation, Volume VII, GSA, Boulder, Colorado, ISBN 0-8137-4107-6, 1987.
Willmott, C. J. and Matsuura, K.: Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance, Climate Res., 30, 79–82, https://doi.org/10.3354/cr030079, 2005.
Yan, L., Xu, W., Wang, H., Wang, R., Meng, Q., Yu, J., and Xie, W. C.: Drainage controls on the Donglingxing landslide (China) induced by rainfall and fluctuation in reservoir water levels, Landslides, 16, 1583–1593, https://doi.org/10.1007/s10346-019-01202-x, 2019.
Yoon, S. S. and Bae, D. H.: Optimal rainfall estimation by considering elevation in the Han River Basin, South Korea, J. Appl. Meteorol. Clim., 52, 802–818, https://doi.org/10.1175/JAMC-D-11-0147.1, 2013.
Yun, H. S., Um, M. J., Cho, W. C., and Heo, J. H.: Orographic precipitation analysis with regional frequency analysis and multiple linear regression, Korea Water Resour. Assoc., 42, 465–480, https://doi.org/10.3741/JKWRA.2009.42.6.465, 2009.
Yune, C. Y., Jun, K. J., Kim, K. S., Kim, G. H., and Lee, S. W.: Analysis of slope hazard-triggering rainfall characteristics in Gangwon Province by database construction, J. Korean Geotech. Soc., 26, 27–38, https://doi.org/10.7843/kgs.2010.26.10.27, 2010.
Zaruba, Q. and Mencl, V.: Landslides and their control, Elsevier, ISBN 9780444600769, 2014.
Zhang, K., Wang, S., Bao, H., and Zhao, X.: Characteristics and influencing factors of rainfall-induced landslide and debris flow hazards in Shaanxi Province, China, Nat. Hazards Earth Syst. Sci., 19, 93–105, https://doi.org/10.5194/nhess-19-93-2019, 2019.
Short summary
To reduce the consequences of landslides due to rainfall, such as loss of life, economic losses, and disruption to daily living, this study describes the process of building a machine learning model which can help to estimate the volume of landslide material that can occur in a particular region, taking into account antecedent rainfall, soil characteristics, type of vegetation, etc. The findings can be useful for land use management, infrastructure design, and rainfall disaster management.
To reduce the consequences of landslides due to rainfall, such as loss of life, economic losses,...
Altmetrics
Final-revised paper
Preprint