Articles | Volume 20, issue 1
https://doi.org/10.5194/nhess-20-271-2020
© Author(s) 2020. 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-20-271-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Jan 2020
Research article |
| 22 Jan 2020
| 22 Jan 2020
Dynamic path-dependent landslide susceptibility modelling
Jalal Samia
CORRESPONDING AUTHOR
Laboratory of Geo-Information Science and Remote Sensing,
Wageningen University & Research, Droevendaalsesteeg 3, 6708 PB, Wageningen, the Netherlands
Soil Geography and Landscape Group, Wageningen University & Research, Droevendaalsesteeg 3, 6708 PB, Wageningen, the Netherlands
Department of Geography and Urban Planning, University of
Mazandaran, Pardis Campus, 47416-13534 PB, Babolsar, Iran
Arnaud Temme
Department of Geography, Kansas State University, 920 N17th Street,
Manhattan, KS 66506, USA
Institute of Arctic and Alpine Research, University of Colorado Boulder,
Campus Box 450, Boulder, CO 803309-0450, USA
Arnold Bregt
Laboratory of Geo-Information Science and Remote Sensing,
Wageningen University & Research, Droevendaalsesteeg 3, 6708 PB, Wageningen, the Netherlands
Jakob Wallinga
Soil Geography and Landscape Group, Wageningen University & Research, Droevendaalsesteeg 3, 6708 PB, Wageningen, the Netherlands
Fausto Guzzetti
Istituto di Ricerca per la Protezione Idrogeologica, Consiglio
Nazionale delle Ricerche, Via Madonna Alta 126, 06128 Perugia, Italy
Francesca Ardizzone
Istituto di Ricerca per la Protezione Idrogeologica, Consiglio
Nazionale delle Ricerche, Via Madonna Alta 126, 06128 Perugia, Italy
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Cited
21 citations as recorded by crossref.
- Data-driven landslide forecasting: Methods, data completeness, and real-time warning T. Xiao & L. Zhang 10.1016/j.enggeo.2023.107068
- An updating of landslide susceptibility prediction from the perspective of space and time Z. Chang et al. 10.1016/j.gsf.2023.101619
- Enhancing post-seismic landslide susceptibility modeling in China through a time-variant approach: a spatio-temporal analysis X. Guo et al. 10.1080/17538947.2023.2265907
- The use of machine learning techniques for a predictive model of debris flows triggered by short intense rainfall M. Ponziani et al. 10.1007/s11069-023-05853-x
- Post-earthquake spatiotemporal evolution characteristics of typical landslide sources in the Jiuzhaigou meizoseismal area C. Huang et al. 10.1007/s10064-024-03724-8
- Machine Learning-Based Evaluation of Susceptibility to Geological Hazards in the Hengduan Mountains Region, China J. Zhao et al. 10.1007/s13753-022-00401-w
- Dynamic process, influence, and triggering mechanism of slope remodelling by landslide clusters in the South Jingyang Tableland, China S. Hu et al. 10.1016/j.catena.2022.106518
- Automatic recognition of slide mass and inversion analysis of landslide based on discrete element method Y. Tang et al. 10.1016/j.cageo.2023.105338
- Space–time landslide hazard modeling via Ensemble Neural Networks A. Dahal et al. 10.5194/nhess-24-823-2024
- Landslide spatial prediction using cluster analysis Z. Zhao et al. 10.1016/j.gr.2024.02.006
- Development of a data-driven model for spatial and temporal shallow landslide probability of occurrence at catchment scale M. Bordoni et al. 10.1007/s10346-020-01592-3
- Multi-event assessment of typhoon-triggered landslide susceptibility in the Philippines J. Jones et al. 10.5194/nhess-23-1095-2023
- The future of landslides’ past—a framework for assessing consecutive landsliding systems A. Temme et al. 10.1007/s10346-020-01405-7
- Can global rainfall estimates (satellite and reanalysis) aid landslide hindcasting? U. Ozturk et al. 10.1007/s10346-021-01689-3
- Spatio-temporal forecasting of landslide hazard in Chongqing National Transmission Protection Regions, China B. Jin et al. 10.1080/17538947.2024.2392843
- Geohazards explained 10 U. Ozturk 10.1111/gto.12391
- Temporal Variations in Landslide Distributions Following Extreme Events: Implications for Landslide Susceptibility Modeling J. Jones et al. 10.1029/2021JF006067
- Landslide size matters: A new data-driven, spatial prototype L. Lombardo et al. 10.1016/j.enggeo.2021.106288
- Evaluating the Response of Hydrological Stress Indices Using the CHyM Model over a Wide Area in Central Italy A. Lombardi et al. 10.3390/hydrology9080139
- Characteristics of landslide path dependency revealed through multiple resolution landslide inventories in the Nepal Himalaya S. Roberts et al. 10.1016/j.geomorph.2021.107868
- Landslides across the USA: occurrence, susceptibility, and data limitations B. Mirus et al. 10.1007/s10346-020-01424-4
20 citations as recorded by crossref.
- Data-driven landslide forecasting: Methods, data completeness, and real-time warning T. Xiao & L. Zhang 10.1016/j.enggeo.2023.107068
- An updating of landslide susceptibility prediction from the perspective of space and time Z. Chang et al. 10.1016/j.gsf.2023.101619
- Enhancing post-seismic landslide susceptibility modeling in China through a time-variant approach: a spatio-temporal analysis X. Guo et al. 10.1080/17538947.2023.2265907
- The use of machine learning techniques for a predictive model of debris flows triggered by short intense rainfall M. Ponziani et al. 10.1007/s11069-023-05853-x
- Post-earthquake spatiotemporal evolution characteristics of typical landslide sources in the Jiuzhaigou meizoseismal area C. Huang et al. 10.1007/s10064-024-03724-8
- Machine Learning-Based Evaluation of Susceptibility to Geological Hazards in the Hengduan Mountains Region, China J. Zhao et al. 10.1007/s13753-022-00401-w
- Dynamic process, influence, and triggering mechanism of slope remodelling by landslide clusters in the South Jingyang Tableland, China S. Hu et al. 10.1016/j.catena.2022.106518
- Automatic recognition of slide mass and inversion analysis of landslide based on discrete element method Y. Tang et al. 10.1016/j.cageo.2023.105338
- Space–time landslide hazard modeling via Ensemble Neural Networks A. Dahal et al. 10.5194/nhess-24-823-2024
- Landslide spatial prediction using cluster analysis Z. Zhao et al. 10.1016/j.gr.2024.02.006
- Development of a data-driven model for spatial and temporal shallow landslide probability of occurrence at catchment scale M. Bordoni et al. 10.1007/s10346-020-01592-3
- Multi-event assessment of typhoon-triggered landslide susceptibility in the Philippines J. Jones et al. 10.5194/nhess-23-1095-2023
- The future of landslides’ past—a framework for assessing consecutive landsliding systems A. Temme et al. 10.1007/s10346-020-01405-7
- Can global rainfall estimates (satellite and reanalysis) aid landslide hindcasting? U. Ozturk et al. 10.1007/s10346-021-01689-3
- Spatio-temporal forecasting of landslide hazard in Chongqing National Transmission Protection Regions, China B. Jin et al. 10.1080/17538947.2024.2392843
- Geohazards explained 10 U. Ozturk 10.1111/gto.12391
- Temporal Variations in Landslide Distributions Following Extreme Events: Implications for Landslide Susceptibility Modeling J. Jones et al. 10.1029/2021JF006067
- Landslide size matters: A new data-driven, spatial prototype L. Lombardo et al. 10.1016/j.enggeo.2021.106288
- Evaluating the Response of Hydrological Stress Indices Using the CHyM Model over a Wide Area in Central Italy A. Lombardi et al. 10.3390/hydrology9080139
- Characteristics of landslide path dependency revealed through multiple resolution landslide inventories in the Nepal Himalaya S. Roberts et al. 10.1016/j.geomorph.2021.107868
1 citations as recorded by crossref.
Latest update: 19 Nov 2024
Short summary
For the Collazzone study area in Italy, we quantified how much landslides follow others using Ripley's K function, finding that susceptibility is increased within 60 m and 17 years after a previous landslide. We then calculated the increased susceptibility for every pixel and for the 17-time-slice landslide inventory. We used these as additional explanatory variables in susceptibility modelling. Model performance increased substantially with this landslide history component included.
For the Collazzone study area in Italy, we quantified how much landslides follow others using...
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