Articles | Volume 24, issue 3
https://doi.org/10.5194/nhess-24-823-2024
© Author(s) 2024. 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-24-823-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Mar 2024
Research article |
| 08 Mar 2024
| 08 Mar 2024
Space–time landslide hazard modeling via Ensemble Neural Networks
Ashok Dahal
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, Enschede, AE 7500, the Netherlands
Hakan Tanyas
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, Enschede, AE 7500, the Netherlands
Cees van Westen
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, Enschede, AE 7500, the Netherlands
Mark van der Meijde
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, Enschede, AE 7500, the Netherlands
Paul Martin Mai
Physical Science and Engineering (PSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
Raphaël Huser
Statistics Program, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
Luigi Lombardo
Physical Science and Engineering (PSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
Viewed
Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.
Total article views: 5,338 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 06 Apr 2023)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 4,487 | 755 | 96 | 5,338 | 255 | 282 |
- HTML: 4,487
- PDF: 755
- XML: 96
- Total: 5,338
- BibTeX: 255
- EndNote: 282
Total article views: 4,284 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 08 Mar 2024)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 3,443 | 747 | 94 | 4,284 | 229 | 259 |
- HTML: 3,443
- PDF: 747
- XML: 94
- Total: 4,284
- BibTeX: 229
- EndNote: 259
Total article views: 1,054 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 06 Apr 2023)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,044 | 8 | 2 | 1,054 | 26 | 23 |
- HTML: 1,044
- PDF: 8
- XML: 2
- Total: 1,054
- BibTeX: 26
- EndNote: 23
Viewed (geographical distribution)
Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.
Total article views: 5,338 (including HTML, PDF, and XML)
Thereof 5,162 with geography defined
and 176 with unknown origin.
Total article views: 4,284 (including HTML, PDF, and XML)
Thereof 4,121 with geography defined
and 163 with unknown origin.
Total article views: 1,054 (including HTML, PDF, and XML)
Thereof 1,041 with geography defined
and 13 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
37 citations as recorded by crossref.
- On the use of rainfall time series for regional landslide prediction by means of functional regression M. Ahmed et al. https://doi.org/10.1016/j.enggeo.2026.108860
- Space-time variability modelling of landslide susceptibility for strategic infrastructure under changing climate scenarios: The case study of the mega clean energy transmission network (Yangtze River Basin, China) B. Jin et al. https://doi.org/10.1016/j.enggeo.2026.108738
- Mass Movement Risk Assessment in the Loess Hilly Region of Northwest China Using a Weighted Information Theoretic Framework Z. Hu et al. https://doi.org/10.3390/geosciences15120468
- Space–time modeling of Net Primary Productivity before and after major earthquakes Y. Duan et al. https://doi.org/10.1016/j.jag.2026.105273
- Climate change-adapted spatiotemporal prediction and monthly dynamic risk assessment of rainfall-induced landslides using 3ED-ConvLSTM Y. Zhao & H. Hazarika https://doi.org/10.1080/17538947.2025.2592378
- Machine Learning Ensemble Methods for Co-Seismic Landslide Susceptibility: Insights from the 2015 Nepal Earthquake T. Bhattarai & N. Bhandary https://doi.org/10.3390/app15158477
- Unlocking Accuracy: Inventory Geometry as a Missing Link in Machine Learning-Based Rockfall Susceptibility Mapping (Case Study: Calcareous Dorsal, Rif, Morocco) Y. El Miloudi et al. https://doi.org/10.1007/s41748-026-01192-6
- Catena matters: Enhancing landslide prediction with soil profile characteristics and explainable AI A. Achu et al. https://doi.org/10.1016/j.enggeo.2026.108599
- Probabilistic landslide hazard assessments: adaptation of spatial models to large slow-moving earth flows and preliminary evaluation in Loja (Ecuador) J. Soto et al. https://doi.org/10.1007/s12665-024-11905-7
- An Enhanced U-Net Model for Large-Scale Landslide Prediction Using Multi-Source Remote Sensing Data and Physical Risk Assessment L. Zheng et al. https://doi.org/10.4236/jcc.2025.138004
- Integration of machine learning model and CMIP6 analysis for climate change impact-led landslide susceptibility and population exposure assessments in the Nepal Himalaya T. Bhattarai & N. Bhandary https://doi.org/10.1186/s40677-026-00382-8
- What controls the distribution of post-Little Ice Age landslides around the South Patagonian Icefield? G. Seier et al. https://doi.org/10.1007/s10346-025-02681-x
- Towards physics-informed neural networks for landslide prediction A. Dahal & L. Lombardo https://doi.org/10.1016/j.enggeo.2024.107852
- Advanced risk assessment framework for land subsidence impacts on transmission towers in salt lake region B. Jin et al. https://doi.org/10.1016/j.envsoft.2024.106058
- Predicting deep-seated landslide displacement on Taiwan's Lushan through the integration of convolutional neural networks and the Age of Exploration-Inspired Optimizer J. Chou et al. https://doi.org/10.5194/nhess-25-119-2025
- At the Junction Between Deep Learning and Statistics of Extremes: Formalizing the Landslide Hazard Definition A. Dahal et al. https://doi.org/10.1029/2024JH000164
- Distribution-agnostic landslide hazard modelling via Graph Transformers G. Belvederesi et al. https://doi.org/10.1016/j.envsoft.2024.106231
- How to enrich training data for machine learning-based landslide hazard prediction with spatio-temporal precipitation information? A. Edrich et al. https://doi.org/10.1080/17499518.2026.2616779
- Spatial joint hazard assessment of landslide susceptibility and intensity within a single framework: Environmental insights from the Wenchuan earthquake Z. Tang et al. https://doi.org/10.1016/j.scitotenv.2025.178545
- High Resolution Precipitation and Soil Moisture Data Integration for Landslide Susceptibility Mapping Y. Peiro et al. https://doi.org/10.3390/geosciences14120330
- Constraining landslide frequency across the United States to inform county-level risk reduction L. Luna et al. https://doi.org/10.5194/nhess-25-3279-2025
- Outlook on the knowledge gaps to reduce land degradation in Europe M. Zoka et al. https://doi.org/10.3897/soils4europe.e148999
- Susceptibility modeling of hydro-morphological processes considered river topology N. Wang et al. https://doi.org/10.1080/10095020.2024.2440614
- Applications of Geographic Information Systems in Ecological Impact Assessment: A Methods Landscape, Practical Bottlenecks, and Future Pathways J. Dong et al. https://doi.org/10.3390/su172210358
- Space-time modeling of cascading hazards: Chaining wildfires, rainfall and landslide events through machine learning M. Di Napoli et al. https://doi.org/10.1016/j.catena.2024.108452
- An updated version of the SZ-plugin: From space to space–time data-driven modeling in QGIS G. Titti et al. https://doi.org/10.1016/j.jag.2025.104679
- Development of an Embedded In-Mass Inertial Device for Landslide and Rockfall Monitoring M. Shahsavar et al. https://doi.org/10.3390/app16104787
- Post-earthquake landslide hazard evolution: Spatio-temporal analysis of active fault zone in Western Himalayas S. Bukhari et al. https://doi.org/10.1007/s11629-025-0204-1
- Towards automatic delineation of landslide source and runout K. Bhuyan et al. https://doi.org/10.1016/j.enggeo.2024.107866
- Landslide susceptibility methodology for railway planning: a comparative analysis of statistical and machine learning methods in a case study of Marche region, Italy R. Rani et al. https://doi.org/10.1016/j.trgeo.2025.101731
- Interpretable co-seismic landslide prediction: Unveiling the potential of multidirectional peak ground acceleration B. Gao et al. https://doi.org/10.1016/j.enggeo.2025.108153
- Spatiotemporal modeling and projection framework of rainfall-induced landslide risk under climate change B. Du et al. https://doi.org/10.1016/j.jenvman.2024.123474
- Long and short-term perspectives on space–time landslide modelling T. Wang et al. https://doi.org/10.1016/j.jag.2025.104694
- A benchmark dataset and workflow for landslide susceptibility zonation M. Alvioli et al. https://doi.org/10.1016/j.earscirev.2024.104927
- Preliminary assessment of the knowledge gaps to reduce land degradation in Europe M. Zoka et al. https://doi.org/10.3897/soils4europe.e119137
- Short to long term space-time prediction of rain-induced landslides under uncertainty A. Mondini et al. https://doi.org/10.1016/j.scitotenv.2025.179453
- Space-time explainable modelling of regional hillslope deformation, an example from the Tibetan Plateau J. He et al. https://doi.org/10.1016/j.rse.2025.114924
37 citations as recorded by crossref.
- On the use of rainfall time series for regional landslide prediction by means of functional regression M. Ahmed et al. https://doi.org/10.1016/j.enggeo.2026.108860
- Space-time variability modelling of landslide susceptibility for strategic infrastructure under changing climate scenarios: The case study of the mega clean energy transmission network (Yangtze River Basin, China) B. Jin et al. https://doi.org/10.1016/j.enggeo.2026.108738
- Mass Movement Risk Assessment in the Loess Hilly Region of Northwest China Using a Weighted Information Theoretic Framework Z. Hu et al. https://doi.org/10.3390/geosciences15120468
- Space–time modeling of Net Primary Productivity before and after major earthquakes Y. Duan et al. https://doi.org/10.1016/j.jag.2026.105273
- Climate change-adapted spatiotemporal prediction and monthly dynamic risk assessment of rainfall-induced landslides using 3ED-ConvLSTM Y. Zhao & H. Hazarika https://doi.org/10.1080/17538947.2025.2592378
- Machine Learning Ensemble Methods for Co-Seismic Landslide Susceptibility: Insights from the 2015 Nepal Earthquake T. Bhattarai & N. Bhandary https://doi.org/10.3390/app15158477
- Unlocking Accuracy: Inventory Geometry as a Missing Link in Machine Learning-Based Rockfall Susceptibility Mapping (Case Study: Calcareous Dorsal, Rif, Morocco) Y. El Miloudi et al. https://doi.org/10.1007/s41748-026-01192-6
- Catena matters: Enhancing landslide prediction with soil profile characteristics and explainable AI A. Achu et al. https://doi.org/10.1016/j.enggeo.2026.108599
- Probabilistic landslide hazard assessments: adaptation of spatial models to large slow-moving earth flows and preliminary evaluation in Loja (Ecuador) J. Soto et al. https://doi.org/10.1007/s12665-024-11905-7
- An Enhanced U-Net Model for Large-Scale Landslide Prediction Using Multi-Source Remote Sensing Data and Physical Risk Assessment L. Zheng et al. https://doi.org/10.4236/jcc.2025.138004
- Integration of machine learning model and CMIP6 analysis for climate change impact-led landslide susceptibility and population exposure assessments in the Nepal Himalaya T. Bhattarai & N. Bhandary https://doi.org/10.1186/s40677-026-00382-8
- What controls the distribution of post-Little Ice Age landslides around the South Patagonian Icefield? G. Seier et al. https://doi.org/10.1007/s10346-025-02681-x
- Towards physics-informed neural networks for landslide prediction A. Dahal & L. Lombardo https://doi.org/10.1016/j.enggeo.2024.107852
- Advanced risk assessment framework for land subsidence impacts on transmission towers in salt lake region B. Jin et al. https://doi.org/10.1016/j.envsoft.2024.106058
- Predicting deep-seated landslide displacement on Taiwan's Lushan through the integration of convolutional neural networks and the Age of Exploration-Inspired Optimizer J. Chou et al. https://doi.org/10.5194/nhess-25-119-2025
- At the Junction Between Deep Learning and Statistics of Extremes: Formalizing the Landslide Hazard Definition A. Dahal et al. https://doi.org/10.1029/2024JH000164
- Distribution-agnostic landslide hazard modelling via Graph Transformers G. Belvederesi et al. https://doi.org/10.1016/j.envsoft.2024.106231
- How to enrich training data for machine learning-based landslide hazard prediction with spatio-temporal precipitation information? A. Edrich et al. https://doi.org/10.1080/17499518.2026.2616779
- Spatial joint hazard assessment of landslide susceptibility and intensity within a single framework: Environmental insights from the Wenchuan earthquake Z. Tang et al. https://doi.org/10.1016/j.scitotenv.2025.178545
- High Resolution Precipitation and Soil Moisture Data Integration for Landslide Susceptibility Mapping Y. Peiro et al. https://doi.org/10.3390/geosciences14120330
- Constraining landslide frequency across the United States to inform county-level risk reduction L. Luna et al. https://doi.org/10.5194/nhess-25-3279-2025
- Outlook on the knowledge gaps to reduce land degradation in Europe M. Zoka et al. https://doi.org/10.3897/soils4europe.e148999
- Susceptibility modeling of hydro-morphological processes considered river topology N. Wang et al. https://doi.org/10.1080/10095020.2024.2440614
- Applications of Geographic Information Systems in Ecological Impact Assessment: A Methods Landscape, Practical Bottlenecks, and Future Pathways J. Dong et al. https://doi.org/10.3390/su172210358
- Space-time modeling of cascading hazards: Chaining wildfires, rainfall and landslide events through machine learning M. Di Napoli et al. https://doi.org/10.1016/j.catena.2024.108452
- An updated version of the SZ-plugin: From space to space–time data-driven modeling in QGIS G. Titti et al. https://doi.org/10.1016/j.jag.2025.104679
- Development of an Embedded In-Mass Inertial Device for Landslide and Rockfall Monitoring M. Shahsavar et al. https://doi.org/10.3390/app16104787
- Post-earthquake landslide hazard evolution: Spatio-temporal analysis of active fault zone in Western Himalayas S. Bukhari et al. https://doi.org/10.1007/s11629-025-0204-1
- Towards automatic delineation of landslide source and runout K. Bhuyan et al. https://doi.org/10.1016/j.enggeo.2024.107866
- Landslide susceptibility methodology for railway planning: a comparative analysis of statistical and machine learning methods in a case study of Marche region, Italy R. Rani et al. https://doi.org/10.1016/j.trgeo.2025.101731
- Interpretable co-seismic landslide prediction: Unveiling the potential of multidirectional peak ground acceleration B. Gao et al. https://doi.org/10.1016/j.enggeo.2025.108153
- Spatiotemporal modeling and projection framework of rainfall-induced landslide risk under climate change B. Du et al. https://doi.org/10.1016/j.jenvman.2024.123474
- Long and short-term perspectives on space–time landslide modelling T. Wang et al. https://doi.org/10.1016/j.jag.2025.104694
- A benchmark dataset and workflow for landslide susceptibility zonation M. Alvioli et al. https://doi.org/10.1016/j.earscirev.2024.104927
- Preliminary assessment of the knowledge gaps to reduce land degradation in Europe M. Zoka et al. https://doi.org/10.3897/soils4europe.e119137
- Short to long term space-time prediction of rain-induced landslides under uncertainty A. Mondini et al. https://doi.org/10.1016/j.scitotenv.2025.179453
- Space-time explainable modelling of regional hillslope deformation, an example from the Tibetan Plateau J. He et al. https://doi.org/10.1016/j.rse.2025.114924
Saved (final revised paper)
Latest update: 23 Jun 2026
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.
We propose a modeling approach capable of recognizing slopes that may generate landslides, as...
Altmetrics
Final-revised paper
Preprint