Articles | Volume 25, issue 1
https://doi.org/10.5194/nhess-25-183-2025
© 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-25-183-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Jan 2025
Research article |
| 07 Jan 2025
Comparison of conditioning factor classification criteria in large-scale statistically based landslide susceptibility models
Faculty of Mining, Geology and Petroleum Engineering, University of Zagreb, Pierottijeva 6, 10000 Zagreb, Croatia
Sanja Bernat Gazibara
Faculty of Mining, Geology and Petroleum Engineering, University of Zagreb, Pierottijeva 6, 10000 Zagreb, Croatia
Mauro Rossi
Consiglio Nazionale delle Ricerche, Istituto di Ricerca per la Protezione Idrogeologica, via Madonna Alta 126, 06128 Perugia, Italy
Snježana Mihalić Arbanas
Faculty of Mining, Geology and Petroleum Engineering, University of Zagreb, Pierottijeva 6, 10000 Zagreb, Croatia
Related authors
No articles found.
Roberto Sarro, Mauro Rossi, Paola Reichenbach, and Rosa María Mateos
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-85, https://doi.org/10.5194/nhess-2024-85, 2024
Preprint under review for NHESS
Short summary
Short summary
This study proposes a novel workflow to precisely model rockfalls. It compares three methods for defining source areas to enhance model accuracy. Identified areas are inputted into a runout model to identify vulnerable zones. A new approach generates probabilistic susceptibility maps using ECDFs. Validation strategies employing various inventory types are included. Comparing six susceptibility maps highlights the impact of source area definition on model precision.
Sandra Melzner, Marco Conedera, Johannes Hübl, and Mauro Rossi
Nat. Hazards Earth Syst. Sci., 23, 3079–3093, https://doi.org/10.5194/nhess-23-3079-2023, https://doi.org/10.5194/nhess-23-3079-2023, 2023
Short summary
Short summary
The estimation of the temporal frequency of the involved rockfall processes is an important part in hazard and risk assessments. Different methods can be used to collect and analyse rockfall data. From a statistical point of view, rockfall datasets are nearly always incomplete. Accurate data collection approaches and the application of statistical methods on existing rockfall data series as reported in this study should be better considered in rockfall hazard and risk assessments in the future.
Luca Schilirò, Mauro Rossi, Federica Polpetta, Federica Fiorucci, Carolina Fortunato, and Paola Reichenbach
Nat. Hazards Earth Syst. Sci., 23, 1789–1804, https://doi.org/10.5194/nhess-23-1789-2023, https://doi.org/10.5194/nhess-23-1789-2023, 2023
Short summary
Short summary
We present a database of the main scientific articles published on earthquake-triggered landslides in the last 4 decades. To enhance data viewing, the articles were catalogued into a web-based GIS, which was specifically designed to show different types of information, such as bibliometric information, the relevant topic and sub-topic category (or categories), and earthquake(s) addressed. Such information can be useful to obtain a general overview of the topic, especially for a broad readership.
Mauro Rossi, Txomin Bornaetxea, and Paola Reichenbach
Geosci. Model Dev., 15, 5651–5666, https://doi.org/10.5194/gmd-15-5651-2022, https://doi.org/10.5194/gmd-15-5651-2022, 2022
Short summary
Short summary
LAND-SUITE is a software package designed to support landslide susceptibility zonation. The software integrates, extends, and completes LAND-SE (Rossi et al., 2010; Rossi and Reichenbach, 2016). The software is implemented in R, a free software environment for statistical computing and graphics, and gives expert users the possibility to perform easier, more flexible, and more informed statistically based landslide susceptibility applications and zonations.
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.
Txomin Bornaetxea, Mauro Rossi, Ivan Marchesini, and Massimiliano Alvioli
Nat. Hazards Earth Syst. Sci., 18, 2455–2469, https://doi.org/10.5194/nhess-18-2455-2018, https://doi.org/10.5194/nhess-18-2455-2018, 2018
Short summary
Short summary
While producing a landslide susceptibility map using a fieldwork-based landslide inventory and a logistic regression model, two crucial questions came to our minds. (i) Shall we consider unsurveyed regions of the study area, for which landslide absence is typically assumed? (ii) Which reference mapping unit should be used in our model? So we compared four maps and found that rejecting unsurveyed regions together with slope units as reference mapping unit should be the best option.
Federica Fiorucci, Daniele Giordan, Michele Santangelo, Furio Dutto, Mauro Rossi, and Fausto Guzzetti
Nat. Hazards Earth Syst. Sci., 18, 405–417, https://doi.org/10.5194/nhess-18-405-2018, https://doi.org/10.5194/nhess-18-405-2018, 2018
Short summary
Short summary
This paper describes the criteria for the optimal selection of remote sensing images to map event landslides, discussing the ability of monoscopic and stereoscopic VHR satellite images and ultra-high-resolution UAV images to resolve the landslide photographical and morphological signatures. The findings can be useful to decide on the optimal imagery and technique to be used when planning the production of a landslide inventory map.
Liesbet Jacobs, Olivier Dewitte, Jean Poesen, John Sekajugo, Adriano Nobile, Mauro Rossi, Wim Thiery, and Matthieu Kervyn
Nat. Hazards Earth Syst. Sci., 18, 105–124, https://doi.org/10.5194/nhess-18-105-2018, https://doi.org/10.5194/nhess-18-105-2018, 2018
Short summary
Short summary
While country-specific, continental and global susceptibility maps are increasingly available, local and regional susceptibility studies remain rare in remote and data-poor settings. Here, we provide a landslide susceptibility assessment for the inhabited region of the Rwenzori Mountains. We find that higher spatial resolutions do not necessarily lead to better models and that models built for local case studies perform better than aggregated susceptibility assessments on the regional scale.
Maria Elena Martinotti, Luca Pisano, Ivan Marchesini, Mauro Rossi, Silvia Peruccacci, Maria Teresa Brunetti, Massimo Melillo, Giuseppe Amoruso, Pierluigi Loiacono, Carmela Vennari, Giovanna Vessia, Maria Trabace, Mario Parise, and Fausto Guzzetti
Nat. Hazards Earth Syst. Sci., 17, 467–480, https://doi.org/10.5194/nhess-17-467-2017, https://doi.org/10.5194/nhess-17-467-2017, 2017
Short summary
Short summary
We studied a period of torrential rain between 1 and 6 September 2014 in the Gargano Promontory, Puglia, southern Italy, which caused a variety of geohydrological hazards, including landslides, flash floods, inundations and sinkholes. We used the rainfall and the landslide information available to us to design and test the new ensemble – non-exceedance probability (E-NEP) algorithm for the quantitative evaluation of the probability of the occurrence of rainfall-induced landslides.
Massimiliano Alvioli, Ivan Marchesini, Paola Reichenbach, Mauro Rossi, Francesca Ardizzone, Federica Fiorucci, and Fausto Guzzetti
Geosci. Model Dev., 9, 3975–3991, https://doi.org/10.5194/gmd-9-3975-2016, https://doi.org/10.5194/gmd-9-3975-2016, 2016
Short summary
Short summary
Slope units are morphological mapping units bounded by drainage and divide lines that maximize within-unit homogeneity and between-unit heterogeneity. We use r.slopeunits, a software for the automatic delination of slope units. We outline an objective procedure to optimize the software input parameters for landslide susceptibility (LS) zonation. Optimization is achieved by maximizing an objective function that simultaneously evaluates terrain aspect segmentation quality and LS model performance.
Mauro Rossi and Paola Reichenbach
Geosci. Model Dev., 9, 3533–3543, https://doi.org/10.5194/gmd-9-3533-2016, https://doi.org/10.5194/gmd-9-3533-2016, 2016
Short summary
Short summary
Landslide susceptibility maps show places where landslides may occur in the future. These maps are prepared using different approaches, information on past landslides distribution and a variety of geo-environmental data. The paper describes LAND-SE (LANDslide Susceptibility Evaluation), an open-source software coded in R for statistically based susceptibility zonation that provides estimates of model performances and uncertainty. A user guide and example data are distributed with the software.
M. Mergili, I. Marchesini, M. Alvioli, M. Metz, B. Schneider-Muntau, M. Rossi, and F. Guzzetti
Geosci. Model Dev., 7, 2969–2982, https://doi.org/10.5194/gmd-7-2969-2014, https://doi.org/10.5194/gmd-7-2969-2014, 2014
Short summary
Short summary
The article deals with strategies to (i) reduce computation time and to (ii) appropriately account for uncertain input parameters when applying an open source GIS sliding surface model to estimate landslide susceptibility for a 90km² study area in central Italy. For (i), the area is split into a large number of tiles, enabling the exploitation of multi-processor computing environments. For (ii), the model is run with various parameter combinations to compute the slope failure probability.
G. Vessia, M. Parise, M. T. Brunetti, S. Peruccacci, M. Rossi, C. Vennari, and F. Guzzetti
Nat. Hazards Earth Syst. Sci., 14, 2399–2408, https://doi.org/10.5194/nhess-14-2399-2014, https://doi.org/10.5194/nhess-14-2399-2014, 2014
I. Marchesini, F. Ardizzone, M. Alvioli, M. Rossi, and F. Guzzetti
Nat. Hazards Earth Syst. Sci., 14, 2215–2231, https://doi.org/10.5194/nhess-14-2215-2014, https://doi.org/10.5194/nhess-14-2215-2014, 2014
S. Raia, M. Alvioli, M. Rossi, R. L. Baum, J. W. Godt, and F. Guzzetti
Geosci. Model Dev., 7, 495–514, https://doi.org/10.5194/gmd-7-495-2014, https://doi.org/10.5194/gmd-7-495-2014, 2014
Related subject area
Landslides and Debris Flows Hazards
Invited perspectives: Integrating hydrologic information into the next generation of landslide early warning systems
Predicting deep-seated landslide displacement on Taiwan's Lushan through the integration of convolutional neural networks and the Age of Exploration-Inspired Optimizer
Limit analysis of earthquake-induced landslides considering two strength envelopes
The vulnerability of buildings to a large-scale debris flow and outburst flood hazard cascade that occurred on 30 August 2020 in Ganluo, southwest China
Optimizing rainfall-triggered landslide thresholds for daily landslide hazard warning in the Three Gorges Reservoir area
Brief communication: Monitoring impending slope failure with very high-resolution spaceborne synthetic aperture radar
Size scaling of large landslides from incomplete inventories
InSAR-informed in situ monitoring for deep-seated landslides: insights from El Forn (Andorra)
A coupled hydrological and hydrodynamic modeling approach for estimating rainfall thresholds of debris-flow occurrence
More than one landslide per road kilometer – surveying and modeling mass movements along the Rishikesh–Joshimath (NH-7) highway, Uttarakhand, India
An integrated method for assessing vulnerability of buildings caused by debris flows in mountainous areas
Temporal clustering of precipitation for detection of potential landslides
Shallow-landslide stability evaluation in loess areas according to the Revised Infinite Slope Model: a case study of the 7.25 Tianshui sliding-flow landslide events of 2013 in the southwest of the Loess Plateau, China
Probabilistic assessment of postfire debris-flow inundation in response to forecast rainfall
Evaluating post-wildfire debris-flow rainfall thresholds and volume models at the 2020 Grizzly Creek Fire in Glenwood Canyon, Colorado, USA
Unravelling Landslide Failure Mechanisms with Seismic Signal Analysis for Enhanced Pre-Survey Understanding
Addressing class imbalance in soil movement predictions
Assessing the impact of climate change on landslides near Vejle, Denmark, using public data
Predicting the thickness of shallow landslides in Switzerland using machine learning
Analysis of three-dimensional slope stability combined with rainfall and earthquake
Assessing landslide damming susceptibility in Central Asia
Assessing locations susceptible to shallow landslide initiation during prolonged intense rainfall in the Lares, Utuado, and Naranjito municipalities of Puerto Rico
Evaluation of debris-flow building damage forecasts
Characteristics of debris-flow-prone watersheds and debris-flow-triggering rainstorms following the Tadpole Fire, New Mexico, USA
Morphological characteristics and conditions of drainage basins contributing to the formation of debris flow fans: an examination of regions with different rock strength using decision tree analysis
Characterizing the scale of regional landslide triggering from storm hydrometeorology
Comparison of debris flow observations, including fine-sediment grain size and composition and runout model results, at Illgraben, Swiss Alps
Simulation analysis of 3D stability of a landslide with a locking segment: a case study of the Tizicao landslide in Maoxian County, southwest China
Space–time landslide hazard modeling via Ensemble Neural Networks
Optimization strategy for flexible barrier structures: investigation and back analysis of a rockfall disaster case in southwestern China
Numerical-model-derived intensity–duration thresholds for early warning of rainfall-induced debris flows in a Himalayan catchment
Slope Unit Maker (SUMak): an efficient and parameter-free algorithm for delineating slope units to improve landslide modeling
Probabilistic Hydrological Estimation of LandSlides (PHELS): global ensemble landslide hazard modelling
Exploratory analysis of the annual risk to life from debris flows
A new analytical method for stability analysis of rock blocks with basal erosion in sub-horizontal strata by considering the eccentricity effect
Rockfall monitoring with a Doppler radar on an active rockslide complex in Brienz/Brinzauls (Switzerland)
Landslide initiation thresholds in data-sparse regions: application to landslide early warning criteria in Sitka, Alaska, USA
Lessons learnt from a rockfall time series analysis: data collection, statistical analysis, and applications
The concept of event-size-dependent exhaustion and its application to paraglacial rockslides
Coastal earthquake-induced landslide susceptibility during the 2016 Mw 7.8 Kaikōura earthquake, New Zealand
Characteristics of debris flows recorded in the Shenmu area of central Taiwan between 2004 and 2021
Semi-automatic mapping of shallow landslides using free Sentinel-2 images and Google Earth Engine
The role of thermokarst evolution in debris flow initiation (Hüttekar Rock Glacier, Austrian Alps)
Accounting for the effect of forest and fragmentation in probabilistic rockfall hazard
Comprehensive landslide susceptibility map of Central Asia
The influence of large woody debris on post-wildfire debris flow sediment storage
Statistical modeling of sediment supply in torrent catchments of the northern French Alps
A data-driven evaluation of post-fire landslide susceptibility
Deciphering seasonal effects of triggering and preparatory precipitation for improved shallow landslide prediction using generalized additive mixed models
Brief communication: The northwest Himalaya towns slipping towards potential disaster
Benjamin B. Mirus, Thom Bogaard, Roberto Greco, and Manfred Stähli
Nat. Hazards Earth Syst. Sci., 25, 169–182, https://doi.org/10.5194/nhess-25-169-2025, https://doi.org/10.5194/nhess-25-169-2025, 2025
Short summary
Short summary
Early warning of increased landslide potential provides situational awareness to reduce landslide-related losses from major storm events. For decades, landslide forecasts relied on rainfall data alone, but recent research points to the value of hydrologic information for improving predictions. In this paper, we provide our perspectives on the value and limitations of integrating subsurface hillslope hydrologic monitoring data and mathematical modeling for more accurate landslide forecasts.
Jui-Sheng Chou, Hoang-Minh Nguyen, Huy-Phuong Phan, and Kuo-Lung Wang
Nat. Hazards Earth Syst. Sci., 25, 119–146, https://doi.org/10.5194/nhess-25-119-2025, https://doi.org/10.5194/nhess-25-119-2025, 2025
Short summary
Short summary
This study enhances landslide prediction using advanced machine learning, including new algorithms inspired by historical explorations. The research accurately forecasts landslide movements by analyzing 8 years of data from Taiwan's Lushan, improving early warning and potentially saving lives and infrastructure. This integration marks a significant advancement in environmental risk management.
Di Wu, Yuke Wang, and Xin Chen
Nat. Hazards Earth Syst. Sci., 24, 4617–4630, https://doi.org/10.5194/nhess-24-4617-2024, https://doi.org/10.5194/nhess-24-4617-2024, 2024
Short summary
Short summary
This paper proposes a 3D limit analysis for seismic stability of soil slopes to address the influence of earthquakes on slope stabilities with nonlinear and linear criteria. Comparison results illustrate that the use of a linear envelope leads to the non-negligible overestimation of steep-slope stability, and this overestimation will be significant with increasing earthquakes. Earthquakes have a smaller influence on slope slip surfaces with a nonlinear envelope than those with a linear envelope.
Li Wei, Kaiheng Hu, Shuang Liu, Lan Ning, Xiaopeng Zhang, Qiyuan Zhang, and Md. Abdur Rahim
Nat. Hazards Earth Syst. Sci., 24, 4179–4197, https://doi.org/10.5194/nhess-24-4179-2024, https://doi.org/10.5194/nhess-24-4179-2024, 2024
Short summary
Short summary
The damage patterns of the buildings were classified into three types: (I) buried by primary debris flow, (II) inundated by secondary dam-burst flood, and (III) sequentially buried by debris flow and inundated by dam-burst flood. The threshold of the impact pressures in Zones (II) and (III) where vulnerability is equal to 1 is 84 kPa and 116 kPa, respectively. Heavy damage occurs at an impact pressure greater than 50 kPa, while slight damage occurs below 30 kPa.
Bo Peng and Xueling Wu
Nat. Hazards Earth Syst. Sci., 24, 3991–4013, https://doi.org/10.5194/nhess-24-3991-2024, https://doi.org/10.5194/nhess-24-3991-2024, 2024
Short summary
Short summary
Our research enhances landslide prevention using advanced machine learning to forecast heavy-rainfall-triggered landslides. By analyzing regions and employing various models, we identified optimal ways to predict high-risk rainfall events. Integrating multiple factors and models, including a neural network, significantly improves landslide predictions. Real data validation confirms our approach's reliability, aiding communities in mitigating landslide impacts and safeguarding lives and property.
Andrea Manconi, Yves Bühler, Andreas Stoffel, Johan Gaume, Qiaoping Zhang, and Valentyn Tolpekin
Nat. Hazards Earth Syst. Sci., 24, 3833–3839, https://doi.org/10.5194/nhess-24-3833-2024, https://doi.org/10.5194/nhess-24-3833-2024, 2024
Short summary
Short summary
Our research reveals the power of high-resolution satellite synthetic-aperture radar (SAR) imagery for slope deformation monitoring. Using ICEYE data over the Brienz/Brinzauls instability, we measured surface velocity and mapped the landslide event with unprecedented precision. This underscores the potential of satellite SAR for timely hazard assessment in remote regions and aiding disaster mitigation efforts effectively.
Oliver Korup, Lisa V. Luna, and Joaquin V. Ferrer
Nat. Hazards Earth Syst. Sci., 24, 3815–3832, https://doi.org/10.5194/nhess-24-3815-2024, https://doi.org/10.5194/nhess-24-3815-2024, 2024
Short summary
Short summary
Catalogues of mapped landslides are useful for learning and forecasting how frequently they occur in relation to their size. Yet, rare and large landslides remain mostly uncertain in statistical summaries of these catalogues. We propose a single, consistent method of comparing across different data sources and find that landslide statistics disclose more about subjective mapping choices than trigger types or environmental settings.
Rachael Lau, Carolina Seguí, Tyler Waterman, Nathaniel Chaney, and Manolis Veveakis
Nat. Hazards Earth Syst. Sci., 24, 3651–3661, https://doi.org/10.5194/nhess-24-3651-2024, https://doi.org/10.5194/nhess-24-3651-2024, 2024
Short summary
Short summary
This work examines the use of interferometric synthetic-aperture radar (InSAR) alongside in situ borehole measurements to assess the stability of deep-seated landslides for the case study of El Forn (Andorra). Comparing InSAR with borehole data suggests a key trade-off between accuracy and precision for various InSAR resolutions. Spatial interpolation with InSAR informed how many remote observations are necessary to lower error in a remote sensing re-creation of ground motion over the landslide.
Zhen Lei Wei, Yue Quan Shang, Qiu Hua Liang, and Xi Lin Xia
Nat. Hazards Earth Syst. Sci., 24, 3357–3379, https://doi.org/10.5194/nhess-24-3357-2024, https://doi.org/10.5194/nhess-24-3357-2024, 2024
Short summary
Short summary
The initiation of debris flows is significantly influenced by rainfall-induced hydrological processes. We propose a novel framework based on an integrated hydrological and hydrodynamic model and aimed at estimating intensity–duration (ID) rainfall thresholds responsible for triggering debris flows. In comparison to traditional statistical approaches, this physically based framework is particularly suitable for application in ungauged catchments where historical debris flow data are scarce.
Jürgen Mey, Ravi Kumar Guntu, Alexander Plakias, Igo Silva de Almeida, and Wolfgang Schwanghart
Nat. Hazards Earth Syst. Sci., 24, 3207–3223, https://doi.org/10.5194/nhess-24-3207-2024, https://doi.org/10.5194/nhess-24-3207-2024, 2024
Short summary
Short summary
The Himalayan road network links remote areas, but fragile terrain and poor construction lead to frequent landslides. This study on the NH-7 in India's Uttarakhand region analyzed 300 landslides after heavy rainfall in 2022 . Factors like slope, rainfall, rock type and road work influence landslides. The study's model predicts landslide locations for better road maintenance planning, highlighting the risk from climate change and increased road use.
Chenchen Qiu and Xueyu Geng
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-156, https://doi.org/10.5194/nhess-2024-156, 2024
Revised manuscript accepted for NHESS
Short summary
Short summary
We proposed an interated method with the combination of a physical vulnerability matric and a machine learning model to estimate the potential physical damage and associated economic loss caused by future debris flows based on the collected historical data on the Qinghai-Tibet Plateau regions.
Fabiola Banfi, Emanuele Bevacqua, Pauline Rivoire, Sérgio C. Oliveira, Joaquim G. Pinto, Alexandre M. Ramos, and Carlo De Michele
Nat. Hazards Earth Syst. Sci., 24, 2689–2704, https://doi.org/10.5194/nhess-24-2689-2024, https://doi.org/10.5194/nhess-24-2689-2024, 2024
Short summary
Short summary
Landslides are complex phenomena causing important impacts in vulnerable areas, and they are often triggered by rainfall. Here, we develop a new approach that uses information on the temporal clustering of rainfall, i.e. multiple events close in time, to detect landslide events and compare it with the use of classical empirical rainfall thresholds, considering as a case study the region of Lisbon, Portugal. The results could help to improve the prediction of rainfall-triggered landslides.
Jianqi Zhuang, Jianbing Peng, Chenhui Du, Yi Zhu, and Jiaxu Kong
Nat. Hazards Earth Syst. Sci., 24, 2615–2631, https://doi.org/10.5194/nhess-24-2615-2024, https://doi.org/10.5194/nhess-24-2615-2024, 2024
Short summary
Short summary
The Revised Infinite Slope Model (RISM) is proposed using the equal differential unit method and correcting the deficiency of the safety factor increasing with the slope increasing when the slope is larger than 40°, as calculated using the Taylor slope infinite model. The intensity–duration (I–D) prediction curve of the rainfall-induced shallow loess landslides with different slopes was constructed and can be used in forecasting regional shallow loess landslides.
Alexander B. Prescott, Luke A. McGuire, Kwang-Sung Jun, Katherine R. Barnhart, and Nina S. Oakley
Nat. Hazards Earth Syst. Sci., 24, 2359–2374, https://doi.org/10.5194/nhess-24-2359-2024, https://doi.org/10.5194/nhess-24-2359-2024, 2024
Short summary
Short summary
Fire can dramatically increase the risk of debris flows to downstream communities with little warning, but hazard assessments have not traditionally included estimates of inundation. We unify models developed by the scientific community to create probabilistic estimates of inundation area in response to rainfall at forecast lead times (≥ 24 h) needed for decision-making. This work takes an initial step toward a near-real-time postfire debris-flow inundation hazard assessment product.
Francis K. Rengers, Samuel Bower, Andrew Knapp, Jason W. Kean, Danielle W. vonLembke, Matthew A. Thomas, Jaime Kostelnik, Katherine R. Barnhart, Matthew Bethel, Joseph E. Gartner, Madeline Hille, Dennis M. Staley, Justin K. Anderson, Elizabeth K. Roberts, Stephen B. DeLong, Belize Lane, Paxton Ridgway, and Brendan P. Murphy
Nat. Hazards Earth Syst. Sci., 24, 2093–2114, https://doi.org/10.5194/nhess-24-2093-2024, https://doi.org/10.5194/nhess-24-2093-2024, 2024
Short summary
Short summary
Every year the U.S. Geological Survey produces 50–100 postfire debris-flow hazard assessments using models for debris-flow likelihood and volume. To refine these models they must be tested with datasets that clearly document rainfall, debris-flow response, and debris-flow volume. These datasets are difficult to obtain, but this study developed and analyzed a postfire dataset with more than 100 postfire storm responses over a 2-year period. We also proposed ways to improve these models.
Jui-Ming Chang, Che-Ming Yang, Wei-An Chao, Chin-Shang Ku, Ming-Wan Huang, Tung-Chou Hsieh, and Chi-Yao Hung
EGUsphere, https://doi.org/10.5194/egusphere-2024-1267, https://doi.org/10.5194/egusphere-2024-1267, 2024
Short summary
Short summary
The study on the Cilan Landslide (CL) demonstrates the utilization of seismic analysis results as preliminary data for geologists during field surveys. Spectrograms revealed that the 1st event of CL consisted of 4 sliding failures, accompanied by a gradual reduction in landslide volume. The 2nd and 3rd events were minor topplings and rockfalls. Then combining the seismological-based knowledge and field survey results, the temporal-spatial variation of landslide evolution is proposed.
Praveen Kumar, Priyanka Priyanka, Kala Venkata Uday, and Varun Dutt
Nat. Hazards Earth Syst. Sci., 24, 1913–1928, https://doi.org/10.5194/nhess-24-1913-2024, https://doi.org/10.5194/nhess-24-1913-2024, 2024
Short summary
Short summary
Our study focuses on predicting soil movement to mitigate landslide risks. We develop machine learning models with oversampling techniques to address the class imbalance in monitoring data. The dynamic ensemble model with K-means SMOTE (synthetic minority oversampling technique) achieves high precision, high recall, and a high F1 score. Our findings highlight the potential of these models with oversampling techniques to improve soil movement predictions in landslide-prone areas.
Kristian Svennevig, Julian Koch, Marie Keiding, and Gregor Luetzenburg
Nat. Hazards Earth Syst. Sci., 24, 1897–1911, https://doi.org/10.5194/nhess-24-1897-2024, https://doi.org/10.5194/nhess-24-1897-2024, 2024
Short summary
Short summary
In our study, we analysed publicly available data in order to investigate the impact of climate change on landslides in Denmark. Our research indicates that the rising groundwater table due to climate change will result in an increase in landslide activity. Previous incidents of extremely wet winters have caused damage to infrastructure and buildings due to landslides. This study is the first of its kind to exclusively rely on public data and examine landslides in Denmark.
Christoph Schaller, Luuk Dorren, Massimiliano Schwarz, Christine Moos, Arie C. Seijmonsbergen, and E. Emiel van Loon
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-76, https://doi.org/10.5194/nhess-2024-76, 2024
Revised manuscript accepted for NHESS
Short summary
Short summary
We developed a machine learning-based approach to predict the potential thickness of shallow landslides to generate improved inputs for slope stability models. We selected 21 explanatory variables including metrics on terrain, geomorphology, vegetation height, and lithology and used data from two Swiss field inventories to calibrate and test the models. The best performing machine learning model consistently reduced the mean average error by least 17 % compared to previously existing models.
Jiao Wang, Zhangxing Wang, Guanhua Sun, and Hongming Luo
Nat. Hazards Earth Syst. Sci., 24, 1741–1756, https://doi.org/10.5194/nhess-24-1741-2024, https://doi.org/10.5194/nhess-24-1741-2024, 2024
Short summary
Short summary
With a simplified formula linking rainfall and groundwater level, the rise of the phreatic surface within the slope can be obtained. Then, a global analysis method that considers both seepage and seismic forces is proposed to determine the safety factor of slopes subjected to the combined effect of rainfall and earthquakes. By taking a slope in the Three Gorges Reservoir area as an example, the safety evolution of the slope combined with both rainfall and earthquake is also examined.
Carlo Tacconi Stefanelli, William Frodella, Francesco Caleca, Zhanar Raimbekova, Ruslan Umaraliev, and Veronica Tofani
Nat. Hazards Earth Syst. Sci., 24, 1697–1720, https://doi.org/10.5194/nhess-24-1697-2024, https://doi.org/10.5194/nhess-24-1697-2024, 2024
Short summary
Short summary
Central Asia regions are marked by active tectonics, high mountains with glaciers, and strong rainfall. These predisposing factors make large landslides a serious threat in the area and a source of possible damming scenarios, which endanger the population. To prevent this, a semi-automated geographic information system (GIS-)based mapping method, centered on a bivariate correlation of morphometric parameters, was applied to give preliminary information on damming susceptibility in Central Asia.
Rex L. Baum, Dianne L. Brien, Mark E. Reid, William H. Schulz, and Matthew J. Tello
Nat. Hazards Earth Syst. Sci., 24, 1579–1605, https://doi.org/10.5194/nhess-24-1579-2024, https://doi.org/10.5194/nhess-24-1579-2024, 2024
Short summary
Short summary
We mapped potential for heavy rainfall to cause landslides in part of the central mountains of Puerto Rico using new tools for estimating soil depth and quasi-3D slope stability. Potential ground-failure locations correlate well with the spatial density of landslides from Hurricane Maria. The smooth boundaries of the very high and high ground-failure susceptibility zones enclose 75 % and 90 %, respectively, of observed landslides. The maps can help mitigate ground-failure hazards.
Katherine R. Barnhart, Christopher R. Miller, Francis K. Rengers, and Jason W. Kean
Nat. Hazards Earth Syst. Sci., 24, 1459–1483, https://doi.org/10.5194/nhess-24-1459-2024, https://doi.org/10.5194/nhess-24-1459-2024, 2024
Short summary
Short summary
Debris flows are a type of fast-moving landslide that start from shallow landslides or during intense rain. Infrastructure located downstream of watersheds susceptible to debris flows may be damaged should a debris flow reach them. We present and evaluate an approach to forecast building damage caused by debris flows. We test three alternative models for simulating the motion of debris flows and find that only one can forecast the correct number and spatial pattern of damaged buildings.
Luke A. McGuire, Francis K. Rengers, Ann M. Youberg, Alexander N. Gorr, Olivia J. Hoch, Rebecca Beers, and Ryan Porter
Nat. Hazards Earth Syst. Sci., 24, 1357–1379, https://doi.org/10.5194/nhess-24-1357-2024, https://doi.org/10.5194/nhess-24-1357-2024, 2024
Short summary
Short summary
Runoff and erosion increase after fire, leading to a greater likelihood of floods and debris flows. We monitored debris flow activity following a fire in western New Mexico, USA, and observed 16 debris flows over a <2-year monitoring period. Rainstorms with recurrence intervals of approximately 1 year were sufficient to initiate debris flows. All debris flows initiated during the first several months following the fire, indicating a rapid decrease in debris flow susceptibility over time.
Ken'ichi Koshimizu, Satoshi Ishimaru, Fumitoshi Imaizumi, and Gentaro Kawakami
Nat. Hazards Earth Syst. Sci., 24, 1287–1301, https://doi.org/10.5194/nhess-24-1287-2024, https://doi.org/10.5194/nhess-24-1287-2024, 2024
Short summary
Short summary
Morphological conditions of drainage basins that classify the presence or absence of debris flow fans were analyzed in areas with different rock strength using decision tree analysis. The relief ratio is the most important morphological factor regardless of the geology. However, the thresholds of morphological parameters needed for forming debris flow fans differ depending on the geology. Decision tree analysis is an effective tool for evaluating the debris flow risk for each geology.
Jonathan P. Perkins, Nina S. Oakley, Brian D. Collins, Skye C. Corbett, and W. Paul Burgess
EGUsphere, https://doi.org/10.5194/egusphere-2024-873, https://doi.org/10.5194/egusphere-2024-873, 2024
Short summary
Short summary
Landslides are a global issue that results in deaths and economic losses annually. However, it is not clear how storm severity relates to landslide severity across large regions. Here we develop a method to estimate the footprint of landslide area and compare this to meteorologic estimates of storm severity. We find that total storm strength does not clearly relate to landslide area. Rather, landslide area depends on soil wetness and smaller storm structures that can produce intense rainfall.
Daniel Bolliger, Fritz Schlunegger, and Brian W. McArdell
Nat. Hazards Earth Syst. Sci., 24, 1035–1049, https://doi.org/10.5194/nhess-24-1035-2024, https://doi.org/10.5194/nhess-24-1035-2024, 2024
Short summary
Short summary
We analysed data from the Illgraben debris flow monitoring station, Switzerland, and we modelled these flows with a debris flow runout model. We found that no correlation exists between the grain size distribution, the mineralogical composition of the matrix, and the debris flow properties. The flow properties rather appear to be determined by the flow volume, from which most other parameters can be derived.
Yuntao Zhou, Xiaoyan Zhao, Guangze Zhang, Bernd Wünnemann, Jiajia Zhang, and Minghui Meng
Nat. Hazards Earth Syst. Sci., 24, 891–906, https://doi.org/10.5194/nhess-24-891-2024, https://doi.org/10.5194/nhess-24-891-2024, 2024
Short summary
Short summary
We developed three rock bridge models to analyze 3D stability and deformation behaviors of the Tizicao landslide and found that the contact surface model with high strength parameters combines advantages of the intact rock mass model in simulating the deformation of slopes with rock bridges and the modeling advantage of the Jennings model. The results help in choosing a rock bridge model to simulate landslide stability and reveal the influence laws of rock bridges on the stability of landslides.
Ashok Dahal, Hakan Tanyas, Cees van Westen, Mark van der Meijde, Paul Martin Mai, Raphaël Huser, and Luigi Lombardo
Nat. Hazards Earth Syst. Sci., 24, 823–845, https://doi.org/10.5194/nhess-24-823-2024, https://doi.org/10.5194/nhess-24-823-2024, 2024
Short summary
Short summary
We propose a modeling approach capable of recognizing slopes that may generate landslides, as well as how large these mass movements may be. This protocol is implemented, tested, and validated with data that change in both space and time via an Ensemble Neural Network architecture.
Li-Ru Luo, Zhi-Xiang Yu, Li-Jun Zhang, Qi Wang, Lin-Xu Liao, and Li Peng
Nat. Hazards Earth Syst. Sci., 24, 631–649, https://doi.org/10.5194/nhess-24-631-2024, https://doi.org/10.5194/nhess-24-631-2024, 2024
Short summary
Short summary
We performed field investigations on a rockfall near Jiguanshan National Forest Park, Chengdu. Vital information was obtained from an unmanned aerial vehicle survey. A finite element model was created to reproduce the damage evolution. We found that the impact kinetic energy was below the design protection energy. Improper member connections prevent the barrier from producing significant deformation to absorb energy. Damage is avoided by improving the ability of the nets and ropes to slide.
Sudhanshu Dixit, Srikrishnan Siva Subramanian, Piyush Srivastava, Ali P. Yunus, Tapas Ranjan Martha, and Sumit Sen
Nat. Hazards Earth Syst. Sci., 24, 465–480, https://doi.org/10.5194/nhess-24-465-2024, https://doi.org/10.5194/nhess-24-465-2024, 2024
Short summary
Short summary
Rainfall intensity–duration (ID) thresholds can aid in the prediction of natural hazards. Large-scale sediment disasters like landslides, debris flows, and flash floods happen frequently in the Himalayas because of their propensity for intense precipitation events. We provide a new framework that combines the Weather Research and Forecasting (WRF) model with a regionally distributed numerical model for debris flows to analyse and predict intense rainfall-induced landslides in the Himalayas.
Jacob B. Woodard, Benjamin B. Mirus, Nathan J. Wood, Kate E. Allstadt, Benjamin A. Leshchinsky, and Matthew M. Crawford
Nat. Hazards Earth Syst. Sci., 24, 1–12, https://doi.org/10.5194/nhess-24-1-2024, https://doi.org/10.5194/nhess-24-1-2024, 2024
Short summary
Short summary
Dividing landscapes into hillslopes greatly improves predictions of landslide potential across landscapes, but their scaling is often arbitrarily set and can require significant computing power to delineate. Here, we present a new computer program that can efficiently divide landscapes into meaningful slope units scaled to best capture landslide processes. The results of this work will allow an improved understanding of landslide potential and can help reduce the impacts of landslides worldwide.
Anne Felsberg, Zdenko Heyvaert, Jean Poesen, Thomas Stanley, and Gabriëlle J. M. De Lannoy
Nat. Hazards Earth Syst. Sci., 23, 3805–3821, https://doi.org/10.5194/nhess-23-3805-2023, https://doi.org/10.5194/nhess-23-3805-2023, 2023
Short summary
Short summary
The Probabilistic Hydrological Estimation of LandSlides (PHELS) model combines ensembles of landslide susceptibility and of hydrological predictor variables to provide daily, global ensembles of hazard for hydrologically triggered landslides. Testing different hydrological predictors showed that the combination of rainfall and soil moisture performed best, with the lowest number of missed and false alarms. The ensemble approach allowed the estimation of the associated prediction uncertainty.
Mark Bloomberg, Tim Davies, Elena Moltchanova, Tom Robinson, and David Palmer
EGUsphere, https://doi.org/10.5194/egusphere-2023-2695, https://doi.org/10.5194/egusphere-2023-2695, 2023
Short summary
Short summary
Debris flows occur infrequently, with average recurrence intervals (ARIs) ranging from decades to millennia. Consequently, they pose an underappreciated hazard. We describe how to make a preliminary identification of debris flow-susceptible catchments, estimate threshold ARIs for debris flows which pose an unacceptable risk to life, and identify the "window of non-recognition" where debris flows are infrequent enough that their hazard is unrecognised, yet frequent enough to pose a risk to life.
Xushan Shi, Bo Chai, Juan Du, Wei Wang, and Bo Liu
Nat. Hazards Earth Syst. Sci., 23, 3425–3443, https://doi.org/10.5194/nhess-23-3425-2023, https://doi.org/10.5194/nhess-23-3425-2023, 2023
Short summary
Short summary
A 3D stability analysis method is proposed for biased rockfall with external erosion. Four failure modes are considered according to rockfall evolution processes, including partial damage of underlying soft rock and overall failure of hard rock blocks. This method is validated with the biased rockfalls in the Sichuan Basin, China. The critical retreat ratio from low to moderate rockfall susceptibility is 0.33. This method could facilitate rockfall early identification and risk mitigation.
Marius Schneider, Nicolas Oestreicher, Thomas Ehrat, and Simon Loew
Nat. Hazards Earth Syst. Sci., 23, 3337–3354, https://doi.org/10.5194/nhess-23-3337-2023, https://doi.org/10.5194/nhess-23-3337-2023, 2023
Short summary
Short summary
Rockfalls and their hazards are typically treated as statistical events based on rockfall catalogs, but only a few complete rockfall inventories are available today. Here, we present new results from a Doppler radar rockfall alarm system, which has operated since 2018 at a high frequency under all illumination and weather conditions at a site where frequent rockfall events threaten a village and road. The new data set is used to investigate rockfall triggers in an active rockslide complex.
Annette I. Patton, Lisa V. Luna, Joshua J. Roering, Aaron Jacobs, Oliver Korup, and Benjamin B. Mirus
Nat. Hazards Earth Syst. Sci., 23, 3261–3284, https://doi.org/10.5194/nhess-23-3261-2023, https://doi.org/10.5194/nhess-23-3261-2023, 2023
Short summary
Short summary
Landslide warning systems often use statistical models to predict landslides based on rainfall. They are typically trained on large datasets with many landslide occurrences, but in rural areas large datasets may not exist. In this study, we evaluate which statistical model types are best suited to predicting landslides and demonstrate that even a small landslide inventory (five storms) can be used to train useful models for landslide early warning when non-landslide events are also included.
Sandra Melzner, Marco Conedera, Johannes Hübl, and Mauro Rossi
Nat. Hazards Earth Syst. Sci., 23, 3079–3093, https://doi.org/10.5194/nhess-23-3079-2023, https://doi.org/10.5194/nhess-23-3079-2023, 2023
Short summary
Short summary
The estimation of the temporal frequency of the involved rockfall processes is an important part in hazard and risk assessments. Different methods can be used to collect and analyse rockfall data. From a statistical point of view, rockfall datasets are nearly always incomplete. Accurate data collection approaches and the application of statistical methods on existing rockfall data series as reported in this study should be better considered in rockfall hazard and risk assessments in the future.
Stefan Hergarten
Nat. Hazards Earth Syst. Sci., 23, 3051–3063, https://doi.org/10.5194/nhess-23-3051-2023, https://doi.org/10.5194/nhess-23-3051-2023, 2023
Short summary
Short summary
Rockslides are a major hazard in mountainous regions. In formerly glaciated regions, the disposition mainly arises from oversteepened topography and decreases through time. However, little is known about this decrease and thus about the present-day hazard of huge, potentially catastrophic rockslides. This paper presents a new theoretical framework that explains the decrease in maximum rockslide size through time and predicts the present-day frequency of large rockslides for the European Alps.
Colin K. Bloom, Corinne Singeisen, Timothy Stahl, Andrew Howell, Chris Massey, and Dougal Mason
Nat. Hazards Earth Syst. Sci., 23, 2987–3013, https://doi.org/10.5194/nhess-23-2987-2023, https://doi.org/10.5194/nhess-23-2987-2023, 2023
Short summary
Short summary
Landslides are often observed on coastlines following large earthquakes, but few studies have explored this occurrence. Here, statistical modelling of landslides triggered by the 2016 Kaikōura earthquake in New Zealand is used to investigate factors driving coastal earthquake-induced landslides. Geology, steep slopes, and shaking intensity are good predictors of landslides from the Kaikōura event. Steeper slopes close to the coast provide the best explanation for a high landslide density.
Yi-Min Huang
Nat. Hazards Earth Syst. Sci., 23, 2649–2662, https://doi.org/10.5194/nhess-23-2649-2023, https://doi.org/10.5194/nhess-23-2649-2023, 2023
Short summary
Short summary
Debris flows are common hazards in Taiwan, and debris-flow early warning is important for disaster responses. The rainfall thresholds of debris flows are analyzed and determined in terms of rainfall intensity, accumulated rainfall, and rainfall duration, based on case histories in Taiwan. These thresholds are useful for disaster management, and the cases in Taiwan are useful for global debris-flow databases.
Davide Notti, Martina Cignetti, Danilo Godone, and Daniele Giordan
Nat. Hazards Earth Syst. Sci., 23, 2625–2648, https://doi.org/10.5194/nhess-23-2625-2023, https://doi.org/10.5194/nhess-23-2625-2023, 2023
Short summary
Short summary
We developed a cost-effective and user-friendly approach to map shallow landslides using free satellite data. Our methodology involves analysing the pre- and post-event NDVI variation to semi-automatically detect areas potentially affected by shallow landslides (PLs). Additionally, we have created Google Earth Engine scripts to rapidly compute NDVI differences and time series of affected areas. Datasets and codes are stored in an open data repository for improvement by the scientific community.
Simon Seelig, Thomas Wagner, Karl Krainer, Michael Avian, Marc Olefs, Klaus Haslinger, and Gerfried Winkler
Nat. Hazards Earth Syst. Sci., 23, 2547–2568, https://doi.org/10.5194/nhess-23-2547-2023, https://doi.org/10.5194/nhess-23-2547-2023, 2023
Short summary
Short summary
A rapid sequence of cascading events involving thermokarst lake outburst, rock glacier front failure, debris flow development, and river blockage hit an alpine valley in Austria during summer 2019. We analyze the environmental conditions initiating the process chain and identify the rapid evolution of a thermokarst channel network as the main driver. Our results highlight the need to account for permafrost degradation in debris flow hazard assessment studies.
Camilla Lanfranconi, Paolo Frattini, Gianluca Sala, Giuseppe Dattola, Davide Bertolo, Juanjuan Sun, and Giovanni Battista Crosta
Nat. Hazards Earth Syst. Sci., 23, 2349–2363, https://doi.org/10.5194/nhess-23-2349-2023, https://doi.org/10.5194/nhess-23-2349-2023, 2023
Short summary
Short summary
This paper presents a study on rockfall dynamics and hazard, examining the impact of the presence of trees along slope and block fragmentation. We compared rockfall simulations that explicitly model the presence of trees and fragmentation with a classical approach that accounts for these phenomena in model parameters (both the hazard and the kinetic energy change). We also used a non-parametric probabilistic rockfall hazard analysis method for hazard mapping.
Ascanio Rosi, William Frodella, Nicola Nocentini, Francesco Caleca, Hans Balder Havenith, Alexander Strom, Mirzo Saidov, Gany Amirgalievich Bimurzaev, and Veronica Tofani
Nat. Hazards Earth Syst. Sci., 23, 2229–2250, https://doi.org/10.5194/nhess-23-2229-2023, https://doi.org/10.5194/nhess-23-2229-2023, 2023
Short summary
Short summary
This work was carried out within the Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia (SFRARR) project and is focused on the first landslide susceptibility analysis at a regional scale for Central Asia. The most detailed available landslide inventories were implemented in a random forest model. The final aim was to provide a useful tool for reduction strategies to landslide scientists, practitioners, and administrators.
Francis K. Rengers, Luke A. McGuire, Katherine R. Barnhart, Ann M. Youberg, Daniel Cadol, Alexander N. Gorr, Olivia J. Hoch, Rebecca Beers, and Jason W. Kean
Nat. Hazards Earth Syst. Sci., 23, 2075–2088, https://doi.org/10.5194/nhess-23-2075-2023, https://doi.org/10.5194/nhess-23-2075-2023, 2023
Short summary
Short summary
Debris flows often occur after wildfires. These debris flows move water, sediment, and wood. The wood can get stuck in channels, creating a dam that holds boulders, cobbles, sand, and muddy material. We investigated how the channel width and wood length influenced how much sediment is stored. We also used a series of equations to back calculate the debris flow speed using the breaking threshold of wood. These data will help improve models and provide insight into future field investigations.
Maxime Morel, Guillaume Piton, Damien Kuss, Guillaume Evin, and Caroline Le Bouteiller
Nat. Hazards Earth Syst. Sci., 23, 1769–1787, https://doi.org/10.5194/nhess-23-1769-2023, https://doi.org/10.5194/nhess-23-1769-2023, 2023
Short summary
Short summary
In mountain catchments, damage during floods is generally primarily driven by the supply of a massive amount of sediment. Predicting how much sediment can be delivered by frequent and infrequent events is thus important in hazard studies. This paper uses data gathered during the maintenance operation of about 100 debris retention basins to build simple equations aiming at predicting sediment supply from simple parameters describing the upstream catchment.
Elsa S. Culler, Ben Livneh, Balaji Rajagopalan, and Kristy F. Tiampo
Nat. Hazards Earth Syst. Sci., 23, 1631–1652, https://doi.org/10.5194/nhess-23-1631-2023, https://doi.org/10.5194/nhess-23-1631-2023, 2023
Short summary
Short summary
Landslides have often been observed in the aftermath of wildfires. This study explores regional patterns in the rainfall that caused landslides both after fires and in unburned locations. In general, landslides that occur after fires are triggered by less rainfall, confirming that fire helps to set the stage for landslides. However, there are regional differences in the ways in which fire impacts landslides, such as the size and direction of shifts in the seasonality of landslides after fires.
Stefan Steger, Mateo Moreno, Alice Crespi, Peter James Zellner, Stefano Luigi Gariano, Maria Teresa Brunetti, Massimo Melillo, Silvia Peruccacci, Francesco Marra, Robin Kohrs, Jason Goetz, Volkmar Mair, and Massimiliano Pittore
Nat. Hazards Earth Syst. Sci., 23, 1483–1506, https://doi.org/10.5194/nhess-23-1483-2023, https://doi.org/10.5194/nhess-23-1483-2023, 2023
Short summary
Short summary
We present a novel data-driven modelling approach to determine season-specific critical precipitation conditions for landslide occurrence. It is shown that the amount of precipitation required to trigger a landslide in South Tyrol varies from season to season. In summer, a higher amount of preparatory precipitation is required to trigger a landslide, probably due to denser vegetation and higher temperatures. We derive dynamic thresholds that directly relate to hit rates and false-alarm rates.
Yaspal Sundriyal, Vipin Kumar, Neha Chauhan, Sameeksha Kaushik, Rahul Ranjan, and Mohit Kumar Punia
Nat. Hazards Earth Syst. Sci., 23, 1425–1431, https://doi.org/10.5194/nhess-23-1425-2023, https://doi.org/10.5194/nhess-23-1425-2023, 2023
Short summary
Short summary
The NW Himalaya has been one of the most affected terrains of the Himalaya, subject to disastrous landslides. This article focuses on two towns (Joshimath and Bhatwari) of the NW Himalaya, which have been witnessing subsidence for decades. We used a slope stability simulation to determine the response of the hillslopes accommodating these towns under various loading conditions. We found that the maximum displacement in these hillslopes might reach up to 20–25 m.
Cited articles
Ballabio, C. and Sterlacchini, S.: Support Vector Machines for Landslide Susceptibility Mapping: The Staffora River Basin Case Study, Italy, Math. Geosci., 44, 47–70, https://doi.org/10.1007/s11004-011-9379-9, 2012.
Bernat, S., Mihalić Arbanas, S., and Krkač, M.: Landslides triggered in the continental part of Croatia by extreme precipitation in 2013, in: Engineering geology for society and territory, Landslide Process., 2, 1599–1603, 2014a.
Bernat, S., Mihalić Arbanas, S., and Krkač, M.: Inventory of precipitation triggered landslides in the winter of 2013 in Zagreb (Croatia, Europe), in: Proceedings of the 3rd World Landslide Forum, Landslide Science for a Safer Geoenvironment: Volume 2: Methods of Landslide Studies, 3rd World Landslide Forum, Beijing, China, 2–6 June 2014, 829–836, 2014b
Bernat Gazibara, S., Krkač, M., Sečanj, M., and Mihalić Arbanas, S.: Identification and Mapping of Shallow Landslides in the City of Zagreb (Croatia) Using the LiDAR–Based Terrain Model, in: Advancing Culture of Living with Landslides, Springer International Publishing, Cham, 1093–1100, https://doi.org/10.1007/978-3-319-53498-5_124, 2017.
Bernat Gazibara, S., Krkač, M., and Mihalić Arbanas, S.: Verification of historical landslide inventory maps for the Podsljeme area in the City of Zagreb using LiDAR-based landslide inventory, The Mining-Geology-Petroleum Engineering Bulletin, 34, 45–58, https://doi.org/10.17794/rgn.2019.1.5, 2019a.
Bernat Gazibara, S., Krkač, M., and Mihalić Arbanas, S.: Landslide inventory mapping using LiDAR data in the City of Zagreb (Croatia), J. Maps, 15, 773–779, https://doi.org/10.1080/17445647.2019.1671906, 2019b.
Bernat Gazibara, S., Mihalić Arbanas, S., Sinčić, M., Krkač, M., Lukačić, H., Jagodnik, P., and Arbanas, Ž.: LandSlidePlan -Scientific research project on landslide susceptibility assessment in large scale, in: Proceedings of the 5th regional symposium on landslides in Adriatic – Balkan Region, 5th regional symposium on landslides in Adriatic – Balkan Region, Rijeka, Croatia, 23–26 March, 99–106, 2022.
Bernat Gazibara, S., Sinčić, M., Krkač, M., Lukačić, H., and Mihalić Arbanas, S.: Landslide susceptibility assessment on a large scale in the Podsljeme area, City of Zagreb (Croatia), J. Maps, 19, 1–11, https://doi.org/10.1080/17445647.2022.2163197, 2023.
Bishop, Y. M. M., Fienberg, S. E., and Holland, P. W.: Discrete Multivariate Analysis: Theory and Practice, 557 pp., https://doi.org/10.1177/014662167700100218, 1975.
Bonham-Carter, G. F., Agterberg, F. P., and Wright, D. F.: Weights of evidence modelling: a new approach to mapping mineral potential, Geological Survey of Canada, 89-9, https://doi.org/10.4095/128059, 1990.
Bonham-Carter, G. F.: Geographic Information Systems for Geoscientists: Modelling with GIS, Pergamon, Ottawa, ISBN 0080424201, 1994.
Bornaetxea, T., Rossi, M., Marchesini, I., and Alvioli, M.: Effective surveyed area and its role in statistical landslide susceptibility assessments, Nat. Hazards Earth Syst. Sci., 18, 2455–2469, https://doi.org/10.5194/nhess-18-2455-2018, 2018.
Brabb, E. E.: Innovative Approaches to Landslide Hazard Mapping, in: Proceedings of 4th International Symposium on Landslides, 307–324, 1984.
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001.
Catani, F., Lagomarsino, D., Segoni, S., and Tofani, V.: Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues, Nat. Hazards Earth Syst. Sci., 13, 2815–2831, https://doi.org/10.5194/nhess-13-2815-2013, 2013.
Chen, W., Xie, X., Wang, J., Pradhan, B., Hong, H., Bui, D. T., Duan, Z., and Ma, J.: A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility, Catena (Amst), 151, 147–160, https://doi.org/10.1016/j.catena.2016.11.032, 2017.
Chung, C. J. F. and Fabbri, A. G.: Probabilistic Prediction Models for Landslide Hazard Mapping, Photogramm. Eng. Remote Sens., 65, 1389–1399, 1999.
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.
City of Zagreb: Land use map of City of Zagreb, https://registri.nipp.hr/izvori/view.php?id=68 (last access: 10 December 2024), 2011.
Corominas, J., van Westen, C., Frattini, P., Cascini, L., Malet, J.-P., Fotopoulou, S., Catani, F., Van Den Eeckhaut, M., Mavrouli, O., Agliardi, F., Pitilakis, K., Winter, M. G., Pastor, M., Ferlisi, S., Tofani, V., Hervás, J., and Smith, J. T.: Recommendations for the quantitative analysis of landslide risk, B. Eng. Geol. Environ., 73, 209–263, https://doi.org/10.1007/s10064-013-0538-8, 2014.
Cortes, C. and Vapnik, V.: Support-vector networks, Mach. Learn., 20, 273–297, https://doi.org/10.1007/BF00994018, 1995.
Cox, D. R.: The Regression Analysis of Binary Sequences, J. Roy. Stat. Soc., 20, 215–242, 1958.
Cui, K., Lu, D., and Li, W.: Comparison of landslide susceptibility mapping based on statistical index, certainty factors, weights of evidence and evidential belief function models, Geocarto Int., 32, 935–955, https://doi.org/10.1080/10106049.2016.1195886, 2017.
Donati, L. and Turrini, M. C.: An objective method to rank the importance of the factors predisposing to landslides with the GIS methodology: application to an area of the Apennines (Valnerina; Perugia, Italy), Eng. Geol., 63, 277–289, https://doi.org/10.1016/S0013-7952(01)00087-4, 2002.
Dou, J., Tien Bui, D., P. Yunus, A., Jia, K., Song, X., Revhaug, I., Xia, H., and Zhu, Z.: Optimization of Causative Factors for Landslide Susceptibility Evaluation Using Remote Sensing and GIS Data in Parts of Niigata, Japan, PLoS One, 10, e0133262, https://doi.org/10.1371/journal.pone.0133262, 2015.
Evans, J. S., Oakleaf, J., Cushman, S. A., and Theobald, D.: An arc gis toolbox for surface gradient and geo-morphometric modeling, version 2.0-0., https://evansmurphy.wixsite.com/evansspatial/arcgis-gradient-metrics-toolbox (last access: 6 February 2024), 2014.
Farooq, S. and Akram, M. S.: Landslide susceptibility mapping using information value method in Jhelum Valley of the Himalayas, Arab. J. Geosci., 14, 824, https://doi.org/10.1007/s12517-021-07147-7, 2021.
Fawcett, T.: An introduction to ROC analysis, Pattern Recognit. Lett., 27, 861–874, https://doi.org/10.1016/j.patrec.2005.10.010, 2006.
Fell, R., Corominas, J., Bonnard, C., Cascini, L., Leroi, E., and Savage, W. Z.: Guidelines for landslide susceptibility, hazard and risk zoning for land use planning, Eng. Geol., 102, 85–98, https://doi.org/10.1016/j.enggeo.2008.03.022, 2008a.
Fell, R., Corominas, J., Bonnard, C., Cascini, L., Leroi, E., and Savage, W. Z.: Guidelines for landslide susceptibility, hazard and risk zoning for land-use planning, Eng. Geol., 102, 99–111, https://doi.org/10.1016/j.enggeo.2008.03.014, 2008b.
Fressard, M., Thiery, Y., and Maquaire, O.: Which data for quantitative landslide susceptibility mapping at operational scale? Case study of the Pays d'Auge plateau hillslopes (Normandy, France), Nat. Hazards Earth Syst. Sci., 14, 569–588, https://doi.org/10.5194/nhess-14-569-2014, 2014.
Gaidzik, K. and Ramírez-Herrera, M. T.: The importance of input data on landslide susceptibility mapping, Sci. Rep., 11, 19334, https://doi.org/10.1038/s41598-021-98830-y, 2021.
Gorsevski, P. V., Gessler, P. E., Foltz, R. B., and Elliot, W. J.: Spatial Prediction of Landslide Hazard Using Logistic Regression and ROC Analysis, T. GIS, 10, 395–415, https://doi.org/10.1111/j.1467-9671.2006.01004.x, 2006.
Guzzetti, F., Carrara, A., Cardinali, M., and Reichenbach, P.: Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy, Geomorphology, 31, 181–216, https://doi.org/10.1016/S0169-555X(99)00078-1, 1999.
Guzzetti, F., Reichenbach, P., Ardizzone, F., Cardinali, M., and Galli, M.: Estimating the quality of landslide susceptibility models, Geomorphology, 81, 166–184, https://doi.org/10.1016/j.geomorph.2006.04.007, 2006a.
Guzzetti, F., Galli, M., Reichenbach, P., Ardizzone, F., and Cardinali, M.: Landslide hazard assessment in the Collazzone area, Umbria, Central Italy, Nat. Hazards Earth Syst. Sci., 6, 115–131, https://doi.org/10.5194/nhess-6-115-2006, 2006b.
Guzzetti, F., Mondini, A. C., Cardinali, M., Fiorucci, F., Santangelo, M., and Chang, K.-T.: Landslide inventory maps: New tools for an old problem, Earth Sci. Rev., 112, 42–66, https://doi.org/10.1016/j.earscirev.2012.02.001, 2012.
Habumugisha, J. M., Chen, N., Rahman, M., Islam, M. M., Ahmad, H., Elbeltagi, A., Sharma, G., Liza, S. N., and Dewan, A.: Landslide Susceptibility Mapping with Deep Learning Algorithms, Sustainability, 14, 1734, https://doi.org/10.3390/su14031734, 2022.
Hastie, T., Tibshirani, R., and Friendman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, New York, 745, https://doi.org/10.1007/b94608, 2009.
Hemasinghe, H., Rangali, R. S. S., Deshapriya, N. L., and Samarakoon, L.: Landslide susceptibility mapping using logistic regression model (a case study in Badulla District, Sri Lanka), Procedia Eng., 212, 1046–1053, https://doi.org/10.1016/j.proeng.2018.01.135, 2018.
Ho, T. K.: Random decision forests, in: Proceedings of 3rd International Conference on Document Analysis and Recognition, 278–282, https://doi.org/10.1109/ICDAR.1995.598994, 1995.
Huang, F., Zhang, J., Zhou, C., Wang, Y., Huang, J., and Zhu, L.: A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction, Landslides, 17, 217–229, https://doi.org/10.1007/s10346-019-01274-9, 2020a.
Huang, F., Cao, Z., Guo, J., Jiang, S.-H., Li, S., and Guo, Z.: Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping, Catena (Amst), 191, 104580, https://doi.org/10.1016/j.catena.2020.104580, 2020b.
Huang, F., Tao, S., Li, D., Lian, Z., Catani, F., Huang, J., Li, K., and Zhang, C.: Landslide Susceptibility Prediction Considering Neighborhood Characteristics of Landslide Spatial Datasets and Hydrological Slope Units Using Remote Sensing and GIS Technologies, Remote Sens. (Basel), 14, 4436, https://doi.org/10.3390/rs14184436, 2022.
Jacobs, L., Kervyn, M., Reichenbach, P., Rossi, M., Marchesini, I., Alvioli, M., and Dewitte, O.: Regional susceptibility assessments with heterogeneous landslide information: Slope unit- vs. pixel-based approach, Geomorphology, 356, 107084, https://doi.org/10.1016/j.geomorph.2020.107084, 2020.
James, G., Witten, D., Hastie, T., and Tibshirani, R.: An Introduction to Statistical Learning, Springer, New York, 606 pp., https://doi.org/10.1007/978-1-4614-7138-7, 2013.
Jebur, M. N., Pradhan, B., and Tehrany, M. S.: Optimization of landslide conditioning factors using very high-resolution airborne laser scanning (LiDAR) data at catchment scale, Remote Sens. Environ., 152, 150–165, https://doi.org/10.1016/j.rse.2014.05.013, 2014.
Jurak, V., Ivan Matković, Miklin, Ž., and Snježana Mihalić: Data analysis of the landslides in the Republic of Croatia: Present state and perspectives, in: Landslides, 1923–1929, 1996.
Jurak, V., Ortolan, Ž., Ivšić, T., Herak, M., Šumanovac, F., Vukelić, I., Jukić, M., and Šurina, Z.: Geotehničko i seizmičko mikrozoniranje grada Zagreba – pokušaji i ostvarenje (Geotechnical and seizmological microzonation of Zagreb City - attempts and accomplishments), in: Zbornik radova s konferencije razvitak Zagreba, Razvitak Zagreba, Zagreb, Croatia, 1–2 February 2008, 99–108, 2008 (in Croatian).
Kavzoglu, T., Sahin, E. K., and Colkesen, I.: Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression, Landslides, 11, 425–439, https://doi.org/10.1007/s10346-013-0391-7, 2014.
Krkač, M., Bernat Gazibara, S., Sinčić, M., Lukačić, H., Šarić, G., and Snježana, M. A.: Impact of input dana on the quality of the landslide susceptibility large-scale maps: A case study from NW Croatia, in: Progress in Landslide Research and Technology, edited by: Alcántara-Ayala, I., Arbanas, Ž., Cuomo, S., Huntley D., Konagai, K., Mihalić Arbanas, S., Mikoš, M., Sassa, K., Tang, H., and Tiwari, B., https://doi.org/10.1007/978-94-007-2162-3_36, 2023.
Landis, J. R. and Koch, G. G.: The measurement of observer agreement for categorical dana, Biometrics, 33, 159–174, 1977
Lee, S.: Landslide susceptibility mapping using an artificial neural network in the Gangneung area, Korea, Int. J. Remote Sens., 28, 4763–4783, https://doi.org/10.1080/01431160701264227, 2007.
Mathew, J., Jha, V. K., and Rawat, G. S.: Weights of evidence modelling for landslide hazard zonation mapping in part of Bhagirathi valley, Uttarakhand, Curr. Sci., 95, 628–638, 2007.
Merghadi, A., Yunus, A. P., Dou, J., Whiteley, J., ThaiPham, B., Bui, D. T., Avtar, R., and Abderrahmane, B.: Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance, Earth Sci. Rev., 207, 103225, https://doi.org/10.1016/j.earscirev.2020.103225, 2020
Mihalić, S.: Recommendations for Landslide Hazard and Risk Mapping in Croatia, Geol. Croatica, 51, 195–204, https://doi.org/10.4154/GC.1998.15, 1988
Mihalić Arbanas, S., Bernat, S., Fabijanović, S., and Arbanas, Ž.: The analysis of historical landslide information from the area of the City of Zagreb and Primorsko-Goranska County, in: Proceedings of the 1st Regional Symposium on Landslides in the Adriatic-Balkan Region – Landslide and Flood Hazard Assessment, 91–96, https://www.researchgate.net/publication/271908916_Analysis_of_Historical_Landslide_Information_from_the_Area_of_the_City_of_Zagreb_and_Primorsko-Goranska_County (last access: 10 December 2024), 2014.
Mihalić Arbanas, S., Krkač, M., Bernat Gazibara, S.: Application of advanced technologies in landslide research in the area of the City of Zagreb (Croatia, Europe), Geol. Croatica, 69, 179–192, https://doi.org/10.4154/gc.2016.18, 2016.
Mihalić Arbanas, S., Bernat Gazibara, S., Krkač, M., Sinčić, M., Lukačić, H., Jagodnik, P., and Arbanas, Ž.: Landslide Detection and Spatial Prediction: Application of Data and Information from Landslide Maps, in: Progress in Landslide Research and Technology, Volume 1, edited by: Alcantara-Ayala, I., Arbanas, Ž., Huntley, D., Konagai, K., Mikoš, M., Sassa, K., Sassa, S., Tang, H., and Tiwari, B., Fullerton, CA, USA, 195–212, https://doi.org/10.1007/978-3-031-18471-0_16, 2023.
Miklin, Ž., Mlinar, Ž., Brkić, Ž., Hečimović, I., and Dolić, M.: Detailed engineering geological map of Podsljeme urbanised zone at a scale of 1:5000 (DIGK-Phase I), 2007.
Nefeslioglu, H. A., Gokceoglu, C., and Sonmez, H.: An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps, Eng. Geol., 97, 171–191, https://doi.org/10.1016/j.enggeo.2008.01.004, 2008.
Neuhäuser, B. and Terhorst, B.: Landslide susceptibility assessment using “weights-of-evidence” applied to a study area at the Jurassic escarpment (SW-Germany), Geomorphology, 86, 12–24, https://doi.org/10.1016/j.geomorph.2006.08.002, 2007.
Neuhäuser, B., Damm, B., and Terhorst, B.: GIS-based assessment of landslide susceptibility on the base of the Weights-of-Evidence model, Landslides, 9, 511–528, https://doi.org/10.1007/s10346-011-0305-5, 2012.
Pascale, S., Parisi, S., Mancini, A., Schiattarella, M., Conforti, M., Sole, A., Murgante, B., and Sdao, F.: Landslide Susceptibility Mapping Using Artificial Neural Network in the Urban Area of Senise and San Costantino Albanese (Basilicata, Southern Italy), 473–488, https://doi.org/10.1007/978-3-642-39649-6_34, 2013.
Polak, K., Klemar, M., Nejkova, M., Radošević, N., Stepan, Z., Miroslav, M., and Križanić, Z.: Lithological analysis and categorisation of terrain according to soil stability of Medvednica slopes in the area of the City of Zagreb, Geotehnika-Geoexpert, Zagreb, 102 pp., 1979 (in Croatian).
Petschko, H., Bell, R., and Glade, T.: Effectiveness of visually analyzing LiDAR DTM derivatives for earth and debris slide inventory mapping for statistical susceptibility modeling, Landslides, 13, 857–872, https://doi.org/10.1007/s10346-015-0622-1, 2016.
Pourghasemi, H. R., Sadhasivam, N., Amiri, M., Eskandari, S., and Santosh, M.: Landslide susceptibility assessment and mapping using state-of-the art machine learning techniques, Nat. Hazards, 108, 1291–1316, https://doi.org/10.1007/s11069-021-04732-7, 2021.
Pradhan, B.: A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS, Comput. Geosci., 51, 350–365, https://doi.org/10.1016/j.cageo.2012.08.023, 2013.
Ray, R.L., Lazzari, M., and Olutimehin, T.: Remote Sensing Approaches and Related Techniques to Map and Study Landslides, in: Landslides – Investigation and Monitoring, IntechOpen, https://doi.org/10.5772/intechopen.93681, 2020.
Razak, K. A., Santangelo, M., Van Westen, C. J., Straatsma, M. W., and de Jong, S. M.: Generating an optimal DTM from airborne laser scanning data for landslide mapping in a tropical forest environment, Geomorphology, 190, 112–125, https://doi.org/10.1016/j.geomorph.2013.02.021, 2013.
Reichenbach, P., Rossi, M., Malamud, B. D., Mihir, M., and Guzzetti, F.: A review of statistically-based landslide susceptibility models, Earth Sci. Rev., 180, 60–91, https://doi.org/10.1016/j.earscirev.2018.03.001, 2018.
Rossi, M., Guzzetti, F., Reichenbach, P., Mondini, A. C., and Peruccacci, S.: Optimal landslide susceptibility zonation based on multiple forecasts, Geomorphology, 114, 129–142, https://doi.org/10.1016/j.geomorph.2009.06.020, 2010.
Rossi, M., Bornaetxea, T., and Reichenbach, P.: LAND-SUITE V1.0: a suite of tools for statistically based landslide susceptibility zonation, Geosci. Model Dev., 15, 5651–5666, https://doi.org/10.5194/gmd-15-5651-2022, 2022.
Sandić, C., Marjanović, M., Abolmasov, B., and Tošić, R.: Integrating landslide magnitude in the susceptibility assessment of the City of Doboj, using machine learning and heuristic approach, J. Maps, 19, https://doi.org/10.1080/17445647.2022.2163199, 2023.
Sarkar, S., Roy, A. K., and Martha, T. R.: Landslide susceptibility assessment using Information Value Method in parts of the Darjeeling Himalayas, J. Geol. Soc. India, 82, 351–362, https://doi.org/10.1007/s12594-013-0162-z, 2013.
Scaioni, M., Longoni, L., Melillo, V., and Papini, M.: Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives, Remote Sens. (Basel), 6, 9600–9652, https://doi.org/10.3390/rs6109600, 2014.
Shirvani, Z.: A Holistic Analysis for Landslide Susceptibility Mapping Applying Geographic Object-Based Random Forest: A Comparison between Protected and Non-Protected Forests, Remote Sens. (Basel), 12, 434, https://doi.org/10.3390/rs12030434, 2020.
Sinčić, M., Bernat Gazibara, S., Krkač, M., Lukačić, H., and Mihalić Arbanas, S.: The Use of High-Resolution Remote Sensing Data in Preparation of Input Data for Large-Scale Landslide Hazard Assessments, Land (Basel), 11, 1360, https://doi.org/10.3390/land11081360, 2022a.
Sinčić, M., Bernat Gazibara, S., Krkač, M., and Mihalić Arbanas, S.: Landslide susceptibility assessment of the City of Karlovac using the bivariate statistical analysis, Rudarsko-geološko-naftni zbornik, 37, 149–170, https://doi.org/10.17794/rgn.2022.2.13, 2022b.
Soeters, R. and van Westen, C. J.: Slope Instability Recognition Analysis and Zonation, in: Landslides: Investigation and Mitigation, edited by: Turner, K. T. and Schuster, R. L., Washington DC, 129–177, https://www.researchgate.net/publication/209803184_Slope_instability_Recognition_analysis_and_zonation (last access: 10 December 2024), 1996.
State Geodetic Administration: WMS server, topographic map: https://geoportal.dgu.hr/services/tk/wms (last access: 6 February 2024), 2024.
Šikić, V.: Engineering geology of Zagreb – north and south, 1967.
Šikić, K., Basch, O., and Šimunić, A.: Basic Geological Map, scale 1:100 000, Zagreb sheet, 1972.
Šikić, K., Basch, O., and Šimunić, A.: Geological notes for Basic geological map, scale 1:100 000, Zagreb sheet, 75 pp., 1979.
Tehrany, M. S., Pradhan, B., and Jebur, M. N.: Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS, J. Hydrol. (Amst), 504, 69–79, https://doi.org/10.1016/j.jhydrol.2013.09.034, 2013.
Tehrany, M. S., Pradhan, B., and Jebur, M. N.: Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS, J. Hydrol. (Amst), 512, 332–343, https://doi.org/10.1016/j.jhydrol.2014.03.008, 2014.
Tyagi, A., Tiwari, R. K., and James, N.: Mapping the landslide susceptibility considering future land-use land-cover scenario, Landslides, 20, 65–76, https://doi.org/10.1007/s10346-022-01968-7, 2023.
Vakhshoori, V. and Zare, M.: Is the ROC curve a reliable tool to compare the validity of landslide susceptibility maps?, Geomatics, Nat. Hazard. Risk, 9, 249–266, https://doi.org/10.1080/19475705.2018.1424043, 2018.
van Westen, C. J.: Use of weights of evidence modeling for landslide susceptibility mapping, 23 pp., https://filetransfer.itc.nl/pub/westen/GISSIZ/Chapter 6 - Exercises/GIS exercises/Data driven models/Exercise data_driven models 2002.pdf (last access: 10 December 2024), 2002.
van Westen, C. J., van Duren, I., Kruse, H. M. G., and Terlien, M. T. J.: Training Package for Geographic Information Systems in Slope Stability Zonation, Part 1: Theory, ITC Publication, Eeschede, 245 pp., 1993.
van Westen, C. J., Rengers, N., and Soeters, R.: Use of Geomorphological Information in Indirect Landslide Susceptibility Assessment, Nat. Hazards, 30, 399–419, https://doi.org/10.1023/B:NHAZ.0000007097.42735.9e, 2003.
van Westen, C. J., Castellanos, E., and Kuriakose, S. L.: Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview, Eng. Geol., 102, 112–131, https://doi.org/10.1016/j.enggeo.2008.03.010, 2008.
Vapnik, V. N.: The Nature of Statistical Learning Theory, Springer New York, New York, NY, https://doi.org/10.1007/978-1-4757-2440-0, 1995.
Vojteková, J. and Vojtek, M.: Assessment of landslide susceptibility at a local spatial scale applying the multi-criteria analysis and GIS: a case study from Slovakia, Geomatics, Nat. Hazard. Risk, 11, 131–148, https://doi.org/10.1080/19475705.2020.1713233, 2020.
Wang, L.-J., Guo, M., Sawada, K., Lin, J., and Zhang, J.: A comparative study of landslide susceptibility maps using logistic regression, frequency ratio, decision tree, weights of evidence and artificial neural network, Geosci. J., 20, 117–136, https://doi.org/10.1007/s12303-015-0026-1, 2016a.
Wang, Q., Li, W., Wi, Y., Pei, Y., Xing, M., and Yang, D.: A comparative study on the landslide susceptibility mapping using evidential belief function and weights of evidence models, J. Earth Syst. Sci., 125, 645–662, https://doi.org/10.1007/s12040-016-0686-x, 2016b.
Wang, G., Chen, X., and Chen, W.: Spatial Prediction of Landslide Susceptibility Based on GIS and Discriminant Functions, ISPRS Int. J. Geoinf., 9, 144, https://doi.org/10.3390/ijgi9030144, 2020.
Wang, H., Zhang, L., Luo, H., He, J., and Cheung, R. W. M.: AI-powered landslide susceptibility assessment in Hong Kong, Eng. Geol., 288, 106103, https://doi.org/10.1016/j.enggeo.2021.106103, 2021.
Xiao, T., Yu, L., Tian, W., Zhou, C., and Wang, L.: Reducing Local Correlations Among Causal Factor Classifications as a Strategy to Improve Landslide Susceptibility Mapping, Front. Earth Sci. (Lausanne), 9, 781674, https://doi.org/10.3389/feart.2021.781674, 2021.
Xing, Y., Yue, J., Guo, Z., Chen, Y., Hu, J., and Travé, A.: Large-Scale Landslide Susceptibility Mapping Using an Integrated Machine Learning Model: A Case Study in the Lvliang Mountains of China, Front. Earth Sci. (Lausanne), 9, 722491, https://doi.org/10.3389/feart.2021.722491, 2021.
Yan, G., Liang, S., Gui, X., Xie, Y., and Zhao, H.: Optimizing landslide susceptibility mapping in the Kongtong District, NW China: comparing the subdivision criteria of factors, Geocarto. Int., 34, 1408–1426, https://doi.org/10.1080/10106049.2018.1499816, 2019.
Yao, X., Tham, L. G., and Dai, F. C.: Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China, Geomorphology, 101, 572–582, https://doi.org/10.1016/j.geomorph.2008.02.011, 2008.
Yin, K. L. and Yan, T. Z.: Statistical prediction model for slope instability of methamorphosed rocks, in: Proceedings of Fifth International Symposium on Landslides, 1269–1272, 1988.
Yusof, N. M., Pradhan, B., Shafri, H. Z. M., Jebur, M. N., and Yusoff, Z.: Spatial landslide hazard assessment along the Jelapang Corridor of the North-South Expressway in Malaysia using high resolution airborne LiDAR data, Arab. J. Geosci., 8, 9789–9800, https://doi.org/10.1007/s12517-015-1937-x, 2015.
Zêzere, J. L., Pereira, S., Melo, R., Oliveira, S. C., and Garcia, R. A. C.: Mapping landslide susceptibility using data-driven methods, Sci. Total Environ., 589, 250–267, https://doi.org/10.1016/j.scitotenv.2017.02.188, 2017.
Zhao, X. and Chen, W.: Optimization of Computational Intelligence Models for Landslide Susceptibility Evaluation, Remote Sens. (Basel), 12, 2180, https://doi.org/10.3390/rs12142180, 2020.
Short summary
The paper focuses on classifying continuous landslide conditioning factors for susceptibility modelling, which resulted in 54 landslide susceptibility models that tested 11 classification criteria in combination with 5 statistical methods. The novelty of the research is that using stretched landslide conditioning factor values results in models with higher accuracy and that certain statistical methods are more sensitive to the landslide conditioning factor classification criteria than others.
The paper focuses on classifying continuous landslide conditioning factors for susceptibility...
Altmetrics
Final-revised paper
Preprint