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
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Total article views: 1,263 (including HTML, PDF, and XML)
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Cited
16 citations as recorded by crossref.
- At the Junction Between Deep Learning and Statistics of Extremes: Formalizing the Landslide Hazard Definition A. Dahal et al. 10.1029/2024JH000164
- Distribution-agnostic landslide hazard modelling via Graph Transformers G. Belvederesi et al. 10.1016/j.envsoft.2024.106231
- Spatial joint hazard assessment of landslide susceptibility and intensity within a single framework: Environmental insights from the Wenchuan earthquake Z. Tang et al. 10.1016/j.scitotenv.2025.178545
- High Resolution Precipitation and Soil Moisture Data Integration for Landslide Susceptibility Mapping Y. Peiro et al. 10.3390/geosciences14120330
- Susceptibility modeling of hydro-morphological processes considered river topology N. Wang et al. 10.1080/10095020.2024.2440614
- Space-time modeling of cascading hazards: Chaining wildfires, rainfall and landslide events through machine learning M. Di Napoli et al. 10.1016/j.catena.2024.108452
- Probabilistic landslide hazard assessments: adaptation of spatial models to large slow-moving earth flows and preliminary evaluation in Loja (Ecuador) J. Soto et al. 10.1007/s12665-024-11905-7
- Towards automatic delineation of landslide source and runout K. Bhuyan et al. 10.1016/j.enggeo.2024.107866
- Towards physics-informed neural networks for landslide prediction A. Dahal & L. Lombardo 10.1016/j.enggeo.2024.107852
- Spatiotemporal modeling and projection framework of rainfall-induced landslide risk under climate change B. Du et al. 10.1016/j.jenvman.2024.123474
- Advanced risk assessment framework for land subsidence impacts on transmission towers in salt lake region B. Jin et al. 10.1016/j.envsoft.2024.106058
- A benchmark dataset and workflow for landslide susceptibility zonation M. Alvioli et al. 10.1016/j.earscirev.2024.104927
- Preliminary assessment of the knowledge gaps to reduce land degradation in Europe M. Zoka et al. 10.3897/soils4europe.e119137
- 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. 10.5194/nhess-25-119-2025
- Generating multi-temporal landslide inventories through a general deep transfer learning strategy using HR EO data K. Bhuyan et al. 10.1038/s41598-022-27352-y
- Mapping landslides through a temporal lens: an insight toward multi-temporal landslide mapping using the u-net deep learning model K. Bhuyan et al. 10.1080/15481603.2023.2182057
14 citations as recorded by crossref.
- At the Junction Between Deep Learning and Statistics of Extremes: Formalizing the Landslide Hazard Definition A. Dahal et al. 10.1029/2024JH000164
- Distribution-agnostic landslide hazard modelling via Graph Transformers G. Belvederesi et al. 10.1016/j.envsoft.2024.106231
- Spatial joint hazard assessment of landslide susceptibility and intensity within a single framework: Environmental insights from the Wenchuan earthquake Z. Tang et al. 10.1016/j.scitotenv.2025.178545
- High Resolution Precipitation and Soil Moisture Data Integration for Landslide Susceptibility Mapping Y. Peiro et al. 10.3390/geosciences14120330
- Susceptibility modeling of hydro-morphological processes considered river topology N. Wang et al. 10.1080/10095020.2024.2440614
- Space-time modeling of cascading hazards: Chaining wildfires, rainfall and landslide events through machine learning M. Di Napoli et al. 10.1016/j.catena.2024.108452
- Probabilistic landslide hazard assessments: adaptation of spatial models to large slow-moving earth flows and preliminary evaluation in Loja (Ecuador) J. Soto et al. 10.1007/s12665-024-11905-7
- Towards automatic delineation of landslide source and runout K. Bhuyan et al. 10.1016/j.enggeo.2024.107866
- Towards physics-informed neural networks for landslide prediction A. Dahal & L. Lombardo 10.1016/j.enggeo.2024.107852
- Spatiotemporal modeling and projection framework of rainfall-induced landslide risk under climate change B. Du et al. 10.1016/j.jenvman.2024.123474
- Advanced risk assessment framework for land subsidence impacts on transmission towers in salt lake region B. Jin et al. 10.1016/j.envsoft.2024.106058
- A benchmark dataset and workflow for landslide susceptibility zonation M. Alvioli et al. 10.1016/j.earscirev.2024.104927
- Preliminary assessment of the knowledge gaps to reduce land degradation in Europe M. Zoka et al. 10.3897/soils4europe.e119137
- 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. 10.5194/nhess-25-119-2025
2 citations as recorded by crossref.
- Generating multi-temporal landslide inventories through a general deep transfer learning strategy using HR EO data K. Bhuyan et al. 10.1038/s41598-022-27352-y
- Mapping landslides through a temporal lens: an insight toward multi-temporal landslide mapping using the u-net deep learning model K. Bhuyan et al. 10.1080/15481603.2023.2182057
Latest update: 21 Feb 2025
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...
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