Articles | Volume 22, issue 4
https://doi.org/10.5194/nhess-22-1395-2022
© Author(s) 2022. 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-22-1395-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Apr 2022
Research article |
| 21 Apr 2022
| 21 Apr 2022
Assessing the importance of conditioning factor selection in landslide susceptibility for the province of Belluno (region of Veneto, northeastern Italy)
Sansar Raj Meena
CORRESPONDING AUTHOR
Department of Geosciences, University of Padua, Padua, Italy
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, the Netherlands
Silvia Puliero
Department of Geosciences, University of Padua, Padua, Italy
Kushanav Bhuyan
Department of Geosciences, University of Padua, Padua, Italy
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, the Netherlands
Mario Floris
Department of Geosciences, University of Padua, Padua, Italy
Filippo Catani
Department of Geosciences, University of Padua, Padua, Italy
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Sansar Raj Meena, Lorenzo Nava, Kushanav Bhuyan, Silvia Puliero, Lucas Pedrosa Soares, Helen Cristina Dias, Mario Floris, and Filippo Catani
Earth Syst. Sci. Data, 15, 3283–3298, https://doi.org/10.5194/essd-15-3283-2023, https://doi.org/10.5194/essd-15-3283-2023, 2023
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Landslides occur often across the world, with the potential to cause significant damage. Although a substantial amount of research has been conducted on the mapping of landslides using remote-sensing data, gaps and uncertainties remain when developing models to be operational at the global scale. To address this issue, we present the High-Resolution Global landslide Detector Database (HR-GLDD) for landslide mapping with landslide instances from 10 different physiographical regions globally.
Sansar Raj Meena, Florian Albrecht, Daniel Hölbling, Omid Ghorbanzadeh, and Thomas Blaschke
Nat. Hazards Earth Syst. Sci., 21, 301–316, https://doi.org/10.5194/nhess-21-301-2021, https://doi.org/10.5194/nhess-21-301-2021, 2021
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Comprehensive and sustainable landslide management, including identification of landslide-susceptible areas, requires a lot of organisations and people to collaborate efficiently. In this study, we propose a concept for a system that provides users with a platform to share the location of landslide events for further collaboration in Nepal. The system can be beneficial for specifying potentially risky regions and consequently, the development of risk mitigation strategies at the local level.
Chengyong Fang, Xuanmei Fan, Xin Wang, Lorenzo Nava, Hao Zhong, Xiujun Dong, Jixiao Qi, and Filippo Catani
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-239, https://doi.org/10.5194/essd-2024-239, 2024
Preprint under review for ESSD
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In this study, we present the largest publicly available landslide dataset, GDCLD, which includes multi-sensor high-resolution images from various locations around the world. We test GDCLD with seven advanced algorithms and show that it is effective in achieving reliable landslide mapping across different triggers and environments, with great potential in enhancing emergency response and disaster management.
Sansar Raj Meena, Lorenzo Nava, Kushanav Bhuyan, Silvia Puliero, Lucas Pedrosa Soares, Helen Cristina Dias, Mario Floris, and Filippo Catani
Earth Syst. Sci. Data, 15, 3283–3298, https://doi.org/10.5194/essd-15-3283-2023, https://doi.org/10.5194/essd-15-3283-2023, 2023
Short summary
Short summary
Landslides occur often across the world, with the potential to cause significant damage. Although a substantial amount of research has been conducted on the mapping of landslides using remote-sensing data, gaps and uncertainties remain when developing models to be operational at the global scale. To address this issue, we present the High-Resolution Global landslide Detector Database (HR-GLDD) for landslide mapping with landslide instances from 10 different physiographical regions globally.
Sansar Raj Meena, Florian Albrecht, Daniel Hölbling, Omid Ghorbanzadeh, and Thomas Blaschke
Nat. Hazards Earth Syst. Sci., 21, 301–316, https://doi.org/10.5194/nhess-21-301-2021, https://doi.org/10.5194/nhess-21-301-2021, 2021
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Comprehensive and sustainable landslide management, including identification of landslide-susceptible areas, requires a lot of organisations and people to collaborate efficiently. In this study, we propose a concept for a system that provides users with a platform to share the location of landslide events for further collaboration in Nepal. The system can be beneficial for specifying potentially risky regions and consequently, the development of risk mitigation strategies at the local level.
Roberta Bonì, Claudia Meisina, Linda Poggio, Alessandro Fontana, Giulia Tessari, Paolo Riccardi, and Mario Floris
Proc. IAHS, 382, 277–284, https://doi.org/10.5194/piahs-382-277-2020, https://doi.org/10.5194/piahs-382-277-2020, 2020
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In this work, an innovative methodology to generate the automatic ground motion areas mapping is presented. The procedure was tested using different sensors such as ERS-1/2, ENVISAT, COSMO-SkyMed and Sentinel-1 over an area of about 500 km2 in the Venetian-Friulian coastal Plain (NE Italy). The resulting mapping allows to detect priority areas where to address further in situ investigations such as to verify the presence of localized buried landforms.
Giovanni Forzieri, Matteo Pecchi, Marco Girardello, Achille Mauri, Marcus Klaus, Christo Nikolov, Marius Rüetschi, Barry Gardiner, Julián Tomaštík, David Small, Constantin Nistor, Donatas Jonikavicius, Jonathan Spinoni, Luc Feyen, Francesca Giannetti, Rinaldo Comino, Alessandro Wolynski, Francesco Pirotti, Fabio Maistrelli, Ionut Savulescu, Stéphanie Wurpillot-Lucas, Stefan Karlsson, Karolina Zieba-Kulawik, Paulina Strejczek-Jazwinska, Martin Mokroš, Stefan Franz, Lukas Krejci, Ionel Haidu, Mats Nilsson, Piotr Wezyk, Filippo Catani, Yi-Ying Chen, Sebastiaan Luyssaert, Gherardo Chirici, Alessandro Cescatti, and Pieter S. A. Beck
Earth Syst. Sci. Data, 12, 257–276, https://doi.org/10.5194/essd-12-257-2020, https://doi.org/10.5194/essd-12-257-2020, 2020
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Strong winds may uproot and break trees and represent a risk for forests. Despite the importance of this natural disturbance and possible intensification in view of climate change, spatial information about wind-related impacts is currently missing on a pan-European scale. We present a new database of wind disturbances in European forests comprised of more than 80 000 records over the period 2000–2018. Our database is a unique spatial source for the study of forest disturbances at large scales.
Teresa Salvatici, Veronica Tofani, Guglielmo Rossi, Michele D'Ambrosio, Carlo Tacconi Stefanelli, Elena Benedetta Masi, Ascanio Rosi, Veronica Pazzi, Pietro Vannocci, Miriana Petrolo, Filippo Catani, Sara Ratto, Hervè Stevenin, and Nicola Casagli
Nat. Hazards Earth Syst. Sci., 18, 1919–1935, https://doi.org/10.5194/nhess-18-1919-2018, https://doi.org/10.5194/nhess-18-1919-2018, 2018
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In this paper, we present the application of the physically based HIRESSS model (High Resolution Stability Simulator) to forecast the occurrence of shallow landslides in a portion of the Aosta Valley region (Italy). An in-depth study of the geotechnical and hydrological properties of the hillslopes controlling shallow landslides formation was conducted, in order to generate an input map of parameters. The main aim of this study is to set up a regional landslide early warning system.
G. Artese, S. Fiaschi, D. Di Martire, S. Tessitore, M. Fabris, V. Achilli, A. Ahmed, S. Borgstrom, D. Calcaterra, M. Ramondini, S. Artese, M. Floris, A. Menin, M. Monego, and V. Siniscalchi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 23–28, https://doi.org/10.5194/isprs-archives-XLI-B7-23-2016, https://doi.org/10.5194/isprs-archives-XLI-B7-23-2016, 2016
G. Salbego, M. Floris, E. Busnardo, M. Toaldo, and R. Genevois
Nat. Hazards Earth Syst. Sci., 15, 2461–2472, https://doi.org/10.5194/nhess-15-2461-2015, https://doi.org/10.5194/nhess-15-2461-2015, 2015
D. Lagomarsino, S. Segoni, A. Rosi, G. Rossi, A. Battistini, F. Catani, and N. Casagli
Nat. Hazards Earth Syst. Sci., 15, 2413–2423, https://doi.org/10.5194/nhess-15-2413-2015, https://doi.org/10.5194/nhess-15-2413-2015, 2015
S. Segoni, A. Battistini, G. Rossi, A. Rosi, D. Lagomarsino, F. Catani, S. Moretti, and N. Casagli
Nat. Hazards Earth Syst. Sci., 15, 853–861, https://doi.org/10.5194/nhess-15-853-2015, https://doi.org/10.5194/nhess-15-853-2015, 2015
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We monitor and forecast (with lead times up to 48h) regional-scale landslide hazard with an early warning system (EWS) implemented on a user-friendly WebGIS interface.
The EWS detects the most critical rainfall conditions using a mosaic of 25 site-specific thresholds. Moreover, when the rainfall paths recorded by the instruments are compared with the thresholds, the thresholds are shifted in the time axis and adjusted to all possible starting times until the most hazardous scenario is found.
S. Segoni, A. Rosi, G. Rossi, F. Catani, and N. Casagli
Nat. Hazards Earth Syst. Sci., 14, 2637–2648, https://doi.org/10.5194/nhess-14-2637-2014, https://doi.org/10.5194/nhess-14-2637-2014, 2014
F. Catani, D. Lagomarsino, S. Segoni, and V. Tofani
Nat. Hazards Earth Syst. Sci., 13, 2815–2831, https://doi.org/10.5194/nhess-13-2815-2013, https://doi.org/10.5194/nhess-13-2815-2013, 2013
P. Mercogliano, S. Segoni, G. Rossi, B. Sikorsky, V. Tofani, P. Schiano, F. Catani, and N. Casagli
Nat. Hazards Earth Syst. Sci., 13, 771–777, https://doi.org/10.5194/nhess-13-771-2013, https://doi.org/10.5194/nhess-13-771-2013, 2013
G. Martelloni, S. Segoni, D. Lagomarsino, R. Fanti, and F. Catani
Hydrol. Earth Syst. Sci., 17, 1229–1240, https://doi.org/10.5194/hess-17-1229-2013, https://doi.org/10.5194/hess-17-1229-2013, 2013
V. Tofani, S. Segoni, A. Agostini, F. Catani, and N. Casagli
Nat. Hazards Earth Syst. Sci., 13, 299–309, https://doi.org/10.5194/nhess-13-299-2013, https://doi.org/10.5194/nhess-13-299-2013, 2013
G. Rossi, F. Catani, L. Leoni, S. Segoni, and V. Tofani
Nat. Hazards Earth Syst. Sci., 13, 151–166, https://doi.org/10.5194/nhess-13-151-2013, https://doi.org/10.5194/nhess-13-151-2013, 2013
Related subject area
Landslides and Debris Flows Hazards
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
Addressing class imbalance in soil movement predictions
Assessing the impact of climate change on landslides near Vejle, Denmark, using public data
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
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
Temporal clustering of precipitation for detection of potential landslides
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
Dynamic response and breakage of trees subject to a landslide-induced air blast
Debris-flow surges of a very active alpine torrent: a field database
Rainfall thresholds estimation for shallow landslides in Peru from gridded daily data
Instantaneous limit equilibrium back analyses of major rockslides triggered during the 2016–2017 central Italy seismic sequence
Deadly disasters in southeastern South America: flash floods and landslides of February 2022 in Petrópolis, Rio de Janeiro
Multi-event assessment of typhoon-triggered landslide susceptibility in the Philippines
Antecedent rainfall as a critical factor for the triggering of debris flows in arid regions
Sensitivity analysis of a built environment exposed to the synthetic monophasic viscous debris flow impacts with 3-D numerical simulations
Characteristics and causes of natural and human-induced landslides in a tropical mountainous region: the rift flank west of Lake Kivu (Democratic Republic of the Congo)
Spatio-temporal analysis of slope-type debris flow activity in Horlachtal, Austria, based on orthophotos and lidar data since 1947
Assessing the relationship between weather conditions and rockfall using terrestrial laser scanning to improve risk management
Using principal component analysis to incorporate multi-layer soil moisture information in hydrometeorological thresholds for landslide prediction: an investigation based on ERA5-Land reanalysis data
Assessing uncertainties in landslide susceptibility predictions in a changing environment (Styrian Basin, Austria)
Brief communication: An autonomous UAV for catchment-wide monitoring of a debris flow torrent
How volcanic stratigraphy constrains headscarp collapse scenarios: the Samperre cliff case study (Martinique island, Lesser Antilles)
Landslide susceptibility assessment in the rocky coast subsystem of Essaouira, Morocco
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
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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
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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.
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
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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
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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.
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
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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
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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
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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
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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
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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
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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.
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
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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
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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
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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
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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
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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
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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
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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.
Fabiola Banfi, Emanuele Bevacqua, Pauline Rivoire, Sérgio C. Oliveira, Joaquim G. Pinto, Alexandre M. Ramos, and Carlo De Michele
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2023-212, https://doi.org/10.5194/nhess-2023-212, 2023
Revised manuscript accepted for NHESS
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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.
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
Yu Zhuang, Aiguo Xing, Perry Bartelt, Muhammad Bilal, and Zhaowei Ding
Nat. Hazards Earth Syst. Sci., 23, 1257–1266, https://doi.org/10.5194/nhess-23-1257-2023, https://doi.org/10.5194/nhess-23-1257-2023, 2023
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Tree destruction is often used to back calculate the air blast impact region and to estimate the air blast power. Here we established a novel model to assess air blast power using tree destruction information. We find that the dynamic magnification effect makes the trees easier to damage by a landslide-induced air blast, but the large tree deformation would weaken the effect. Bending and overturning are two likely failure modes, which depend heavily on the properties of trees.
Suzanne Lapillonne, Firmin Fontaine, Frédéric Liebault, Vincent Richefeu, and Guillaume Piton
Nat. Hazards Earth Syst. Sci., 23, 1241–1256, https://doi.org/10.5194/nhess-23-1241-2023, https://doi.org/10.5194/nhess-23-1241-2023, 2023
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Debris flows are fast flows most often found in torrential watersheds. They are composed of two phases: a liquid phase which can be mud-like and a granular phase, including large boulders, transported along with the flow. Due to their destructive nature, accessing features of the flow, such as velocity and flow height, is difficult. We present a protocol to analyse debris flow data and results of the Réal torrent in France. These results will help experts in designing models.
Carlos Millán-Arancibia and Waldo Lavado-Casimiro
Nat. Hazards Earth Syst. Sci., 23, 1191–1206, https://doi.org/10.5194/nhess-23-1191-2023, https://doi.org/10.5194/nhess-23-1191-2023, 2023
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This study is the first approximation of regional rainfall thresholds for shallow landslide occurrence in Peru. This research was generated from a gridded precipitation data and landslide inventory. The analysis showed that the threshold based on the combination of mean daily intensity–duration variables gives the best results for separating rainfall events that generate landslides. Through this work the potential of thresholds for landslide monitoring at the regional scale is demonstrated.
Luca Verrucci, Giovanni Forte, Melania De Falco, Paolo Tommasi, Giuseppe Lanzo, Kevin W. Franke, and Antonio Santo
Nat. Hazards Earth Syst. Sci., 23, 1177–1190, https://doi.org/10.5194/nhess-23-1177-2023, https://doi.org/10.5194/nhess-23-1177-2023, 2023
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Stability analyses in static and seismic conditions were performed on four rockslides that occurred during the main shocks of the 2016–2017 central Italy seismic sequence. These results also indicate that specific structural features of the slope must carefully be accounted for in evaluating potential hazards on transportation infrastructures in mountainous regions.
Enner Alcântara, José A. Marengo, José Mantovani, Luciana R. Londe, Rachel Lau Yu San, Edward Park, Yunung Nina Lin, Jingyu Wang, Tatiana Mendes, Ana Paula Cunha, Luana Pampuch, Marcelo Seluchi, Silvio Simões, Luz Adriana Cuartas, Demerval Goncalves, Klécia Massi, Regina Alvalá, Osvaldo Moraes, Carlos Souza Filho, Rodolfo Mendes, and Carlos Nobre
Nat. Hazards Earth Syst. Sci., 23, 1157–1175, https://doi.org/10.5194/nhess-23-1157-2023, https://doi.org/10.5194/nhess-23-1157-2023, 2023
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The municipality of Petrópolis (approximately 305 687 inhabitants) is nestled in the mountains 68 km outside the city of Rio de Janeiro. On 15 February 2022, the city of Petrópolis in Rio de Janeiro, Brazil, received an unusually high volume of rain within 3 h (258 mm). This resulted in flash floods and subsequent landslides that caused 231 fatalities, the deadliest landslide disaster recorded in Petrópolis. This work shows how the disaster was triggered.
Joshua N. Jones, Georgina L. Bennett, Claudia Abancó, Mark A. M. Matera, and Fibor J. Tan
Nat. Hazards Earth Syst. Sci., 23, 1095–1115, https://doi.org/10.5194/nhess-23-1095-2023, https://doi.org/10.5194/nhess-23-1095-2023, 2023
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We modelled where landslides occur in the Philippines using landslide data from three typhoon events in 2009, 2018, and 2019. These models show where landslides occurred within the landscape. By comparing the different models, we found that the 2019 landslides were occurring all across the landscape, whereas the 2009 and 2018 landslides were mostly occurring at specific slope angles and aspects. This shows that landslide susceptibility must be considered variable through space and time.
Shalev Siman-Tov and Francesco Marra
Nat. Hazards Earth Syst. Sci., 23, 1079–1093, https://doi.org/10.5194/nhess-23-1079-2023, https://doi.org/10.5194/nhess-23-1079-2023, 2023
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Debris flows represent a threat to infrastructure and the population. In arid areas, they are observed when heavy rainfall hits steep slopes with sediments. Here, we use digital surface models and radar rainfall data to detect and characterize the triggering and non-triggering rainfall conditions. We find that rainfall intensity alone is insufficient to explain the triggering. We suggest that antecedent rainfall could represent a critical factor for debris flow triggering in arid regions.
Xun Huang, Zhijian Zhang, and Guoping Xiang
Nat. Hazards Earth Syst. Sci., 23, 871–889, https://doi.org/10.5194/nhess-23-871-2023, https://doi.org/10.5194/nhess-23-871-2023, 2023
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A sensitivity analysis on the building impact force resulting from the representative built environment parameters is executed through the FLOW-3D model. The surrounding buildings' properties, especially the azimuthal angle, have been confirmed to play significant roles in determining the peak impact forces. The single and combined effects of built environments are analyzed in detail. This will improve understanding of vulnerability assessment and migration design against debris flow hazards.
Jean-Claude Maki Mateso, Charles L. Bielders, Elise Monsieurs, Arthur Depicker, Benoît Smets, Théophile Tambala, Luc Bagalwa Mateso, and Olivier Dewitte
Nat. Hazards Earth Syst. Sci., 23, 643–666, https://doi.org/10.5194/nhess-23-643-2023, https://doi.org/10.5194/nhess-23-643-2023, 2023
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This research highlights the importance of human activities on the occurrence of landslides and the need to consider this context when studying hillslope instability patterns in regions under anthropogenic pressure. Also, this study highlights the importance of considering the timing of landslides and hence the added value of using historical information for compiling an inventory.
Jakob Rom, Florian Haas, Tobias Heckmann, Moritz Altmann, Fabian Fleischer, Camillo Ressl, Sarah Betz-Nutz, and Michael Becht
Nat. Hazards Earth Syst. Sci., 23, 601–622, https://doi.org/10.5194/nhess-23-601-2023, https://doi.org/10.5194/nhess-23-601-2023, 2023
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In this study, an area-wide slope-type debris flow record has been established for Horlachtal, Austria, since 1947 based on historical and recent remote sensing data. Spatial and temporal analyses show variations in debris flow activity in space and time in a high-alpine region. The results can contribute to a better understanding of past slope-type debris flow dynamics in the context of extreme precipitation events and their possible future development.
Tom Birien and Francis Gauthier
Nat. Hazards Earth Syst. Sci., 23, 343–360, https://doi.org/10.5194/nhess-23-343-2023, https://doi.org/10.5194/nhess-23-343-2023, 2023
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On highly fractured rockwalls such as those found in northern Gaspésie, most rockfalls are triggered by weather conditions. This study highlights that in winter, rockfall frequency is 12 times higher during a superficial thaw than during a cold period in which temperature remains below 0 °C. In summer, rockfall frequency is 22 times higher during a heavy rainfall event than during a mainly dry period. This knowledge could be used to implement a risk management strategy.
Nunziarita Palazzolo, David J. Peres, Enrico Creaco, and Antonino Cancelliere
Nat. Hazards Earth Syst. Sci., 23, 279–291, https://doi.org/10.5194/nhess-23-279-2023, https://doi.org/10.5194/nhess-23-279-2023, 2023
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We propose an approach exploiting PCA to derive hydrometeorological landslide-triggering thresholds using multi-layered soil moisture data from ERA5-Land reanalysis. Comparison of thresholds based on single- and multi-layered soil moisture information provides a means to identify the significance of multi-layered data for landslide triggering in a region. In Sicily, the proposed approach yields thresholds with a higher performance than traditional precipitation-based ones (TSS = 0.71 vs. 0.50).
Raphael Knevels, Helene Petschko, Herwig Proske, Philip Leopold, Aditya N. Mishra, Douglas Maraun, and Alexander Brenning
Nat. Hazards Earth Syst. Sci., 23, 205–229, https://doi.org/10.5194/nhess-23-205-2023, https://doi.org/10.5194/nhess-23-205-2023, 2023
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In summer 2009 and 2014, rainfall events occurred in the Styrian Basin (Austria), triggering thousands of landslides. Landslide storylines help to show potential future changes under changing environmental conditions. The often neglected uncertainty quantification was the aim of this study. We found uncertainty arising from the landslide model to be of the same order as climate scenario uncertainty. Understanding the dimensions of uncertainty is crucial for allowing informed decision-making.
Fabian Walter, Elias Hodel, Erik S. Mannerfelt, Kristen Cook, Michael Dietze, Livia Estermann, Michaela Wenner, Daniel Farinotti, Martin Fengler, Lukas Hammerschmidt, Flavia Hänsli, Jacob Hirschberg, Brian McArdell, and Peter Molnar
Nat. Hazards Earth Syst. Sci., 22, 4011–4018, https://doi.org/10.5194/nhess-22-4011-2022, https://doi.org/10.5194/nhess-22-4011-2022, 2022
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Debris flows are dangerous sediment–water mixtures in steep terrain. Their formation takes place in poorly accessible terrain where instrumentation cannot be installed. Here we propose to monitor such source terrain with an autonomous drone for mapping sediments which were left behind by debris flows or may contribute to future events. Short flight intervals elucidate changes of such sediments, providing important information for landscape evolution and the likelihood of future debris flows.
Marc Peruzzetto, Yoann Legendre, Aude Nachbaur, Thomas J. B. Dewez, Yannick Thiery, Clara Levy, and Benoit Vittecoq
Nat. Hazards Earth Syst. Sci., 22, 3973–3992, https://doi.org/10.5194/nhess-22-3973-2022, https://doi.org/10.5194/nhess-22-3973-2022, 2022
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Volcanic edifices result from successive construction and dismantling phases. Thus, the geological units forming volcanoes display complex geometries. We show that such geometries can be reconstructed thanks to aerial views, topographic surveys and photogrammetric models. In our case study of the Samperre cliff (Martinique, Lesser Antilles), it allows us to link destabilizations from a rocky cliff to the existence of a filled paleo-valley and estimate a potentially unstable volume.
Abdellah Khouz, Jorge Trindade, Sérgio C. Oliveira, Fatima El Bchari, Blaid Bougadir, Ricardo A. C. Garcia, and Mourad Jadoud
Nat. Hazards Earth Syst. Sci., 22, 3793–3814, https://doi.org/10.5194/nhess-22-3793-2022, https://doi.org/10.5194/nhess-22-3793-2022, 2022
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The aim of this study was to assess the landslide susceptibility of the rocky coast of Essaouira using the information value model. The resulting susceptibility maps could be used for both environmental protection and general planning of future development activities.
Cited articles
ARPAV: Cambiamenti climatici, per l'IPCC i tempi stringono, Il focus sul Veneto, https://www.arpa.veneto.it/temi-ambientali/meteo/riferimenti/documenti/documenti-meteo/IPCC E CAMBIAMENTI CLIMATICI IN VENETO.pdf (last access: 9 April 2022), 2021.
Baglioni, A., Tosoni, D., De Marco, P., and Arziliero, L.: Analisi del dissesto da frana in Veneto, ISPRA, https://www.isprambiente.gov.it/contentfiles/00003200/3228-capitolo-10-veneto.pdf (last access: 9 April 2022), 2006.
Baird, C.: Comparison of Risk Assessment Instruments in Juvenile Justice, NCCD, https://www.njjn.org/uploads/digital-library/NCCD_risk-assessment-comparison_August-2013.pdf (last access: 9 April 2022), 2013.
Boretto, G., Crema, S., Marchi, L., Monegato, G., Arziliero, L., and Cavalli, M.: Assessing the effect of the Vaia storm on sediment source areas and connectivity storm in the Liera catchment (Dolomites), in: EGU General Assembly 2021, online, 19–30 April 2021, EGU21-7643, https://doi.org/10.5194/egusphere-egu21-7643, 2021.
Brabb, E. E., Pampeyan, E. H., and Bonilla, M. G.: Landslide susceptibility in San Mateo County, California, Reston, VA, Report 360, https://doi.org/10.3133/mf360, 1972.
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001.
Can, R., Kocaman, S., and Gokceoglu, C.: A Comprehensive Assessment of XGBoost Algorithm for Landslide Susceptibility Mapping in the Upper Basin of Ataturk Dam, Turkey, Appl. Sci., 11, 4993, 2021.
Castellanos Abella, E. A. and Van Westen, C. J.: Qualitative landslide susceptibility assessment by multicriteria analysis: A case study from San Antonio del Sur, Guantánamo, Cuba, Geomorphology, 94, 453–466, https://doi.org/10.1016/j.geomorph.2006.10.038, 2008.
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.
Chacón, J., Irigaray, C., Fernández, T., and El Hamdouni, R.: Engineering geology maps: landslides and geographical information systems, B. Eng. Geol. Environ., 65, 341–411, https://doi.org/10.1007/s10064-006-0064-z, 2006.
Chen, T., Trinder, J. C., and Niu, R.: Object-oriented landslide mapping using ZY-3 satellite imagery, random forest and mathematical morphology, for the Three-Gorges Reservoir, China, Remote Sens., 9, 333, 2017.
Chen, W., Peng, J. B., Hong, H. Y., Shahabi, H., Pradhan, B., Liu, J. Z., Zhu, A. X., Pei, X. J., and Duan, Z.: Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China, Sci. Total Environ., 626, 1121–1135, https://doi.org/10.1016/j.scitotenv.2018.01.124, 2018.
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.
Compagnoni, B., Galluzzo, F., Bonomo, R., and Tacchia, D.: Carta geologica d'Italia, Dipartimento difesa del suolo, APAT, Firenze, 2005.
Corò, D., Galgaro, A., Fontana, A., and Carton, A.: A regional rockfall database: the Eastern Alps test site, Environ. Earth Sci., 74, 1731–1742, https://doi.org/10.1007/s12665-015-4181-5, 2015.
Dahal, R. K., Hasegawa, S., Nonomura, A., Yamanaka, M., Masuda, T., and Nishino, K.: GIS-based weights-of-evidence modelling of rainfall-induced landslides in small catchments for landslide susceptibility mapping, Environ. Geol., 54, 311–324, https://doi.org/10.1007/s00254-007-0818-3, 2008.
Dai, F. C., Lee, C. F., and Ngai, Y. Y.: Landslide risk assessment and management: an overview, Eng. Geol., 64, 65–87, https://doi.org/10.1016/S0013-7952(01)00093-X, 2002.
Demir, G., Aytekin, M., Akgun, A., Ikizler, B., and Tatar, O.: A comparison of landslide susceptibility mapping of the eastern part of the North Anatolian Fault Zone (Turkey) by likelihood-frequency ratio and analytic hierarchy process methods, Nat. Hazards, 65, 1481–1506, 2013.
Desiato, F., Lena, F., Baffo, F., Suatoni, B., and Toreti, A.: Indicatori Del CLIMA in Italia Elaborati Attraverso Il Sistema SCIA, APAT, Rome, http://www.scia.isprambiente.it/wwwrootscia/Documentazione/Indicatori del clima in Italia.pdf (last access: 9 April 2022), 2005.
Doglioni, C.: Thrust tectonics examples from the Venetian Alps, Studi Geologici Camerti s.v., 117–129, http://193.204.8.201:8080/jspui/handle/1336/240 (last access: 9 April 2022), 1990.
Dunning, S., Massey, C., and Rosser, N.: Structural and geomorphological features of landslides in the Bhutan Himalaya derived from terrestrial laser scanning, Geomorphology, 103, 17–29, 2009.
Dury, G., Carson, M. A. and Kirkby, M. J.: Hillslope form and Process, Cambridge University Press, London, vii + 475 pp., https://doi.org/10.1016/0012-8252(72)90132-8), 1972.
Ercanoglu, M. and Gokceoglu, C.: Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach, Environ. Geol., 41, 720–730, https://doi.org/10.1007/s00254-001-0454-2, 2002.
Floris, M., Iafelice, M., Squarzoni, C., Zorzi, L., De Agostini, A., and Genevois, R.: Using online databases for landslide susceptibility assessment: an example from the Veneto Region (northeastern Italy), Nat. Hazards Earth Syst. Sci., 11, 1915–1925, https://doi.org/10.5194/nhess-11-1915-2011, 2011.
Forzieri, G., Pecchi, M., Girardello, M., Mauri, A., Klaus, M., Nikolov, C., Rüetschi, M., Gardiner, B., Tomaštík, J., Small, D., Nistor, C., Jonikavicius, D., Spinoni, J., Feyen, L., Giannetti, F., Comino, R., Wolynski, A., Pirotti, F., Maistrelli, F., Savulescu, I., Wurpillot-Lucas, S., Karlsson, S., Zieba-Kulawik, K., Strejczek-Jazwinska, P., Mokroš, M., Franz, S., Krejci, L., Haidu, I., Nilsson, M., Wezyk, P., Catani, F., Chen, Y.-Y., Luyssaert, S., Chirici, G., Cescatti, A., and Beck, P. S. A.: A spatially explicit database of wind disturbances in European forests over the period 2000–2018, Earth Syst. Sci. Data, 12, 257–276, https://doi.org/10.5194/essd-12-257-2020, 2020.
Froude, M. J. and Petley, D. N.: Global fatal landslide occurrence from 2004 to 2016, Nat. Hazards Earth Syst. Sci., 18, 2161–2181, https://doi.org/10.5194/nhess-18-2161-2018, 2018.
Gariano, S. L., Verini Supplizi, G., Ardizzone, F., Salvati, P., Bianchi, C., Morbidelli, R., and Saltalippi, C.: Long-term analysis of rainfall-induced landslides in Umbria, central Italy, Nat. Hazards, 106, 2207–2225, https://doi.org/10.1007/s11069-021-04539-6, 2021.
Ghorbanzadeh, O., Rostamzadeh, H., Blaschke, T., Gholaminia, K., and Aryal, J.: A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping, Nat. Hazards, 94, 497–517, https://doi.org/10.1007/s11069-018-3449-y, 2018.
Glade, T., Anderson, M. G., and Crozier, M. J.: Landslide hazard and risk, John Wiley & Sons, ISBN 0-471-48663-9,, 2006.
Goetz, J. N., Brenning, A., Petschko, H., and Leopold, P.: Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling, Comput. Geosci., 81, 1–11, https://doi.org/10.1016/j.cageo.2015.04.007, 2015.
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, 2006.
Huang, F., Chen, J., Du, Z., Yao, C., Huang, J., Jiang, Q., Chang, Z., and Li, S.: Landslide Susceptibility Prediction Considering Regional Soil Erosion Based on Machine-Learning Models, ISPRS Int. J. Geo-Inf., 9, 377, 2020.
Iadanza, C., Trigila, A., Starace, P., Dragoni, A., Biondo, T., and Roccisano, M.: IdroGEO: A Collaborative Web Mapping Application Based on REST API Services and Open Data on Landslides and Floods in Italy, ISPRS Int. J. Geo-Inf., 10, 89, 2021.
Komac, M.: A landslide susceptibility model using the Analytical Hierarchy Process method and multivariate statistics in perialpine Slovenia, Geomorphology, 74, 17–28, https://doi.org/10.1016/j.geomorph.2005.07.005, 2006.
Lee, S.: Landslide detection and susceptibility mapping in the Sagimakri area, Korea using KOMPSAT-1 and weight of evidence technique, Environ. Earth Sci., 70, 3197–3215, https://doi.org/10.1007/s12665-013-2385-0, 2013.
Linden, A.: Measuring diagnostic and predictive accuracy in disease management: an introduction to receiver operating characteristic (ROC) analysis, J. Eval. Clin. Pract., 12, 132–139, 2006.
Liu, L.-L., Yang, C., and Wang, X.-M.: Landslide susceptibility assessment using feature selection-based machine learning models, Geomech. Eng., 25, 1–16, 2021.
Mahalingam, R., Olsen, M. J., and O'Banion, M. S.: Evaluation of landslide susceptibility mapping techniques using lidar-derived conditioning factors (Oregon case study), Geomat. Nat. Hazards Risk, 7, 1884–1907, 2016.
Meena, S. R. and Gudiyangada Nachappa, T: Impact of spatial resolution of digital elevation model on landslide susceptibility mapping: a case study in Kullu Valley, Himalayas, Geosciences, 9, 360, https://doi.org/10.3390/geosciences9080360, 2019.
Melville, B., Lucieer, A., and Aryal, J.: Object-based random forest classification of Landsat ETM+ and WorldView-2 satellite imagery for mapping lowland native grassland communities in Tasmania, Australia, Int. J. Appl. Earth Obs., 66, 46–55, 2018.
Merghadi, A., Abderrahmane, B., and Tien Bui, D.: Landslide Susceptibility Assessment at Mila Basin (Algeria): A Comparative Assessment of Prediction Capability of Advanced Machine Learning Methods, ISPRS Int. J. Geo-Inf., 7, 268, https://doi.org/10.3390/ijgi7070268, 2018.
Meten, M., PrakashBhandary, N., and Yatabe, R.: Effect of Landslide Factor Combinations on the Prediction Accuracy of Landslide Susceptibility Maps in the Blue Nile Gorge of Central Ethiopia, Geoenvironmental Disasters, 2, 9, https://doi.org/10.1186/s40677-015-0016-7, 2015.
Micheletti, N., Foresti, L., Robert, S., Leuenberger, M., Pedrazzini, A., Jaboyedoff, M., and Kanevski, M.: Machine Learning Feature Selection Methods for Landslide Susceptibility Mapping, Math. Geosci., 46, 33–57, https://doi.org/10.1007/s11004-013-9511-0, 2014.
Mondal, S. and Maiti, R.: Integrating the analytical hierarchy process (AHP) and the frequency ratio (FR) model in landslide susceptibility mapping of Shiv-khola watershed, Darjeeling Himalaya, Int. J. Disast. Risk Sc., 4, 200–212, 2013.
Pham, B. T., Tien Bui, D., Pourghasemi, H. R., Indra, P., and Dholakia, M. B.: Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods, Theor. Appl. Climatol., 128, 255–273, https://doi.org/10.1007/s00704-015-1702-9, 2015.
Pham, B. T., Tien Bui, D., and Prakash, I.: Bagging based Support Vector Machines for spatial prediction of landslides, Environ. Earth Sci., 77, 146, https://doi.org/10.1007/s12665-018-7268-y, 2018.
Pourghasemi, H. R., Pradhan, B., and Gokceoglu, C.: Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran, Nat. Hazards, 63, 965–996, 2012.
Pourghasemi, H. R., Yansari, Z., Panagos, P., and Pradhan, B.: Analysis and evaluation of landslide susceptibility: a review on articles published during 2005–2016 (periods of 2005–2012 and 2013–2016), Arab. J. Geosci., 11, 1–12, 2018.
Pradhan, B.: Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches, J. Indian Soc. Remote, 38, 301–320, https://doi.org/10.1007/s12524-010-0020-z, 2010.
Raja, N. B., Çiçek, I., Türkoğlu, N., Aydin, O., and Kawasaki, A.: Landslide susceptibility mapping of the Sera River Basin using logistic regression model, Nat. Hazards, 85, 1323–1346, https://doi.org/10.1007/s11069-016-2591-7, 2017.
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.
Riley, S. J., DeGloria, S. D., and Elliot, R.: Index that quantifies topographic heterogeneity, Intermountain Journal of Sciences, 5, 23–27, 1999.
Rossi, M., Guzzetti, F., Salvati, P., Donnini, M., Napolitano, E., and Bianchi, C.: A predictive model of societal landslide risk in Italy, Earth-Sci. Rev., 196, 102849, https://doi.org/10.1016/j.earscirev.2019.04.021, 2019.
Ruff, M. and Czurda, K.: Landslide susceptibility analysis with a heuristic approach in the Eastern Alps (Vorarlberg, Austria), Geomorphology, 94, 314–324, 2008.
Sahin, E. K.: Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest, SN Applied Sciences, 2, 1308, https://doi.org/10.1007/s42452-020-3060-1, 2020.
Sauro, F., Zampieri, D., and Filipponi, M.: Development of a deep karst system within a transpressional structure of the Dolomites in north-east Italy, Geomorphology, 184, 51–63, https://doi.org/10.1016/j.geomorph.2012.11.014, 2013.
Schönborn, G.: Balancing cross sections with kinematic constraints: The Dolomites (northern Italy), Tectonics, 18, 527–545, 1999.
Segoni, S., Pappafico, G., Luti, T., and Catani, F.: Landslide susceptibility assessment in complex geological settings: Sensitivity to geological information and insights on its parameterization, Landslides, 17, 2443–2453, 2020.
Senouci, R., Taibi, N.-E., Teodoro, A. C., Duarte, L., Mansour, H., and Yahia Meddah, R.: GIS-Based Expert Knowledge for Landslide Susceptibility Mapping (LSM): Case of Mostaganem Coast District, West of Algeria, Sustainability, 13, 630, 2021.
Shahabi, H. and Hashim, M.: Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment, Sci. Rep., 5, 9899, 2015.
Shahabi, H., Khezri, S., Ahmad, B. B., and Hashim, M.: Landslide susceptibility mapping at central Zab basin, Iran: a comparison between analytical hierarchy process, frequency ratio and logistic regression models, Catena, 115, 55–70, 2014.
Stanley, T. A., Kirschbaum, D. B., Benz, G., Emberson, R. A., Amatya, P. M., Medwedeff, W., and Clark, M. K.: Data-Driven Landslide Nowcasting at the Global Scale, Front. Earth Sci., 9, 640043, https://doi.org/10.3389/feart.2021.640043, 2021.
Tay, L. T., Lateh, H., Hossain, M. K., and Kamil, A. A.: Landslide hazard mapping using a poisson distribution: a case study in Penang Island, Malaysia, in: Landslide science for a safer geoenvironment, Springer, Cham, 521–525, https://doi.org/10.5923/c.jce.201402.12, 2014.
Trigila, A. and Iadanza, C.: Landslides and floods in Italy: hazard and risk indicators – Summary Report 2018, ISPRA, ISBN 978-88-448-0901-0, 2018.
Trigila, A., Iadanza, C., and Spizzichino, D.: Quality assessment of the Italian Landslide Inventory using GIS processing, Landslides, 7, 455–470, https://doi.org/10.1007/s10346-010-0213-0, 2010.
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.
Xiao, T., Segoni, S., Chen, L., Yin, K., and Casagli, N.: A step beyond landslide susceptibility maps: a simple method to investigate and explain the different outcomes obtained by different approaches, Landslides, 17, 627–640, 2020.
Youssef, A. M. and Pourghasemi, H. R.: Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia, Geosci. Front., 12, 639–655, https://doi.org/10.1016/j.gsf.2020.05.010, 2021.
Short summary
The study investigated the importance of the conditioning factors in predicting landslide occurrences using the mentioned models. In this paper, we evaluated the importance of the conditioning factors (features) in the overall prediction capabilities of the statistical and machine learning algorithms.
The study investigated the importance of the conditioning factors in predicting landslide...
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