Articles | Volume 18, issue 9
https://doi.org/10.5194/nhess-18-2455-2018
© Author(s) 2018. 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-18-2455-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Effective surveyed area and its role in statistical landslide susceptibility assessments
Txomin Bornaetxea
CORRESPONDING AUTHOR
Department of Geography, Prehistory and Archaeology, Faculty of Arts of the University of the Basque Country UPV/EHU, c/ Tomás y Valiente, s/n, 01006, Vitoria-Gasteiz,
Spain
Mauro Rossi
Consiglio Nazionale delle Ricerche, Istituto di Ricerca per la Protezione Idrogeologica, via Madonna Alta 126, 06128 Perugia, Italy
Ivan Marchesini
Consiglio Nazionale delle Ricerche, Istituto di Ricerca per la Protezione Idrogeologica, via Madonna Alta 126, 06128 Perugia, Italy
Massimiliano Alvioli
Consiglio Nazionale delle Ricerche, Istituto di Ricerca per la Protezione Idrogeologica, via Madonna Alta 126, 06128 Perugia, Italy
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Cited
37 citations as recorded by crossref.
- A spaceborne SAR-based procedure to support the detection of landslides G. Esposito et al. 10.5194/nhess-20-2379-2020
- A benchmark dataset and workflow for landslide susceptibility zonation M. Alvioli et al. 10.1016/j.earscirev.2024.104927
- Geomorphological slope units of the Himalayas M. Alvioli et al. 10.1080/17445647.2022.2052768
- Prediction of spatial landslide susceptibility applying the novel ensembles of CNN, GLM and random forest in the Indian Himalayan region S. Saha et al. 10.1007/s00477-022-02212-3
- r.survey: a tool for calculating visibility of variable-size objects based on orientation T. Bornaetxea & I. Marchesini 10.1080/13658816.2021.1942476
- A comprehensive review of machine learning‐based methods in landslide susceptibility mapping S. Liu et al. 10.1002/gj.4666
- Exploring available landslide inventories for susceptibility analysis in Gipuzkoa province (Spain) T. Bornaetxea et al. 10.1007/s11069-023-06103-w
- Space-time data-driven modeling of precipitation-induced shallow landslides in South Tyrol, Italy M. Moreno et al. 10.1016/j.scitotenv.2023.169166
- An Improved Method for the Evaluation and Local Multi-Scale Optimization of the Automatic Extraction of Slope Units in Complex Terrains Z. Yang et al. 10.3390/rs14143444
- A Comprehensive Comparison of Stable and Unstable Area Sampling Strategies in Large-Scale Landslide Susceptibility Models Using Machine Learning Methods M. Sinčić et al. 10.3390/rs16162923
- Landslide Susceptibility Mapping in Terms of the Slope-Unit or Raster-Unit, Which is Better? S. Ma et al. 10.1007/s12583-021-1407-1
- An ensemble model for landslide susceptibility mapping in a forested area A. Arabameri et al. 10.1080/10106049.2019.1585484
- Assessment of earthquake-induced landslide inventories and susceptibility maps using slope unit-based logistic regression and geospatial statistics B. Pokharel et al. 10.1038/s41598-021-00780-y
- Integrating Data Modality and Statistical Learning Methods for Earthquake-Induced Landslide Susceptibility Mapping Z. Miao et al. 10.3390/app12031760
- Correlation does not imply geomorphic causation in data-driven landslide susceptibility modelling – Benefits of exploring landslide data collection effects S. Steger et al. 10.1016/j.scitotenv.2021.145935
- Economic landslide susceptibility under a socio-economic perspective: an application to Umbria Region (Central Italy) M. Donnini et al. 10.1007/s10037-020-00143-6
- Effects of raster resolution on real probability of landslides X. Shao et al. 10.1016/j.rsase.2020.100364
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- Landslide Susceptibility Mapping Based on Multitemporal Remote Sensing Image Change Detection and Multiexponential Band Math X. Yu et al. 10.3390/su15032226
- Influence of sampling design on landslide susceptibility modeling in lithologically heterogeneous areas A. Dornik et al. 10.1038/s41598-022-06257-w
- A landslide susceptibility assessment method considering the similarity of geographic environments based on graph neural network Q. Zhang et al. 10.1016/j.gr.2024.04.013
- Event-Based Landslide Modeling in the Styrian Basin, Austria: Accounting for Time-Varying Rainfall and Land Cover R. Knevels et al. 10.3390/geosciences10060217
- Adopting the margin of stability for space–time landslide prediction – A data-driven approach for generating spatial dynamic thresholds S. Steger et al. 10.1016/j.gsf.2024.101822
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- Landslide susceptibility maps of Italy: Lesson learnt from dealing with multiple landslide types and the uneven spatial distribution of the national inventory M. Loche et al. 10.1016/j.earscirev.2022.104125
- Graph neural networks with constraints of environmental consistency for landslide susceptibility evaluation H. Zeng et al. 10.1080/13658816.2022.2103819
- The Open Landslide Project (OLP), a New Inventory of Shallow Landslides for Susceptibility Models: The Autumn 2019 Extreme Rainfall Event in the Langhe-Monferrato Region (Northwestern Italy) M. Licata et al. 10.3390/geosciences13100289
- Regional susceptibility assessments with heterogeneous landslide information: Slope unit- vs. pixel-based approach L. Jacobs et al. 10.1016/j.geomorph.2020.107084
- LAND-SUITE V1.0: a suite of tools for statistically based landslide susceptibility zonation M. Rossi et al. 10.5194/gmd-15-5651-2022
- Application of the group method of data handling (GMDH) approach for landslide susceptibility zonation using readily available spatial covariates M. Panahi et al. 10.1016/j.catena.2021.105779
- Parameter-free delineation of slope units and terrain subdivision of Italy M. Alvioli et al. 10.1016/j.geomorph.2020.107124
- 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
- Preparing first-time slope failures hazard maps: from pixel-based to slope unit-based G. Domènech et al. 10.1007/s10346-019-01279-4
- The influence of land use and land cover change on landslide susceptibility in the Lower Mekong River Basin C. Dandridge et al. 10.1007/s11069-022-05604-4
- Rapid prediction of the magnitude scale of landslide events triggered by an earthquake H. Tanyaş et al. 10.1007/s10346-019-01136-4
34 citations as recorded by crossref.
- A spaceborne SAR-based procedure to support the detection of landslides G. Esposito et al. 10.5194/nhess-20-2379-2020
- A benchmark dataset and workflow for landslide susceptibility zonation M. Alvioli et al. 10.1016/j.earscirev.2024.104927
- Geomorphological slope units of the Himalayas M. Alvioli et al. 10.1080/17445647.2022.2052768
- Prediction of spatial landslide susceptibility applying the novel ensembles of CNN, GLM and random forest in the Indian Himalayan region S. Saha et al. 10.1007/s00477-022-02212-3
- r.survey: a tool for calculating visibility of variable-size objects based on orientation T. Bornaetxea & I. Marchesini 10.1080/13658816.2021.1942476
- A comprehensive review of machine learning‐based methods in landslide susceptibility mapping S. Liu et al. 10.1002/gj.4666
- Exploring available landslide inventories for susceptibility analysis in Gipuzkoa province (Spain) T. Bornaetxea et al. 10.1007/s11069-023-06103-w
- Space-time data-driven modeling of precipitation-induced shallow landslides in South Tyrol, Italy M. Moreno et al. 10.1016/j.scitotenv.2023.169166
- An Improved Method for the Evaluation and Local Multi-Scale Optimization of the Automatic Extraction of Slope Units in Complex Terrains Z. Yang et al. 10.3390/rs14143444
- A Comprehensive Comparison of Stable and Unstable Area Sampling Strategies in Large-Scale Landslide Susceptibility Models Using Machine Learning Methods M. Sinčić et al. 10.3390/rs16162923
- Landslide Susceptibility Mapping in Terms of the Slope-Unit or Raster-Unit, Which is Better? S. Ma et al. 10.1007/s12583-021-1407-1
- An ensemble model for landslide susceptibility mapping in a forested area A. Arabameri et al. 10.1080/10106049.2019.1585484
- Assessment of earthquake-induced landslide inventories and susceptibility maps using slope unit-based logistic regression and geospatial statistics B. Pokharel et al. 10.1038/s41598-021-00780-y
- Integrating Data Modality and Statistical Learning Methods for Earthquake-Induced Landslide Susceptibility Mapping Z. Miao et al. 10.3390/app12031760
- Correlation does not imply geomorphic causation in data-driven landslide susceptibility modelling – Benefits of exploring landslide data collection effects S. Steger et al. 10.1016/j.scitotenv.2021.145935
- Economic landslide susceptibility under a socio-economic perspective: an application to Umbria Region (Central Italy) M. Donnini et al. 10.1007/s10037-020-00143-6
- Effects of raster resolution on real probability of landslides X. Shao et al. 10.1016/j.rsase.2020.100364
- Rockfall susceptibility and network-ranked susceptibility along the Italian railway M. Alvioli et al. 10.1016/j.enggeo.2021.106301
- Terrain visibility impact on the preparation of landslide inventories: a practical example in Darjeeling district (India) T. Bornaetxea et al. 10.5194/nhess-22-2929-2022
- A novel dynamic rockfall susceptibility model including precipitation, temperature and snowmelt predictors: a case study in Aosta Valley (northern Italy) G. Bajni et al. 10.1007/s10346-023-02091-x
- Landslide Susceptibility Mapping Based on Multitemporal Remote Sensing Image Change Detection and Multiexponential Band Math X. Yu et al. 10.3390/su15032226
- Influence of sampling design on landslide susceptibility modeling in lithologically heterogeneous areas A. Dornik et al. 10.1038/s41598-022-06257-w
- A landslide susceptibility assessment method considering the similarity of geographic environments based on graph neural network Q. Zhang et al. 10.1016/j.gr.2024.04.013
- Event-Based Landslide Modeling in the Styrian Basin, Austria: Accounting for Time-Varying Rainfall and Land Cover R. Knevels et al. 10.3390/geosciences10060217
- Adopting the margin of stability for space–time landslide prediction – A data-driven approach for generating spatial dynamic thresholds S. Steger et al. 10.1016/j.gsf.2024.101822
- Assessing the utility of regionalized rock-mass geomechanical properties in rockfall susceptibility modelling in an alpine environment G. Bajni et al. 10.1016/j.geomorph.2022.108401
- Landslide susceptibility maps of Italy: Lesson learnt from dealing with multiple landslide types and the uneven spatial distribution of the national inventory M. Loche et al. 10.1016/j.earscirev.2022.104125
- Graph neural networks with constraints of environmental consistency for landslide susceptibility evaluation H. Zeng et al. 10.1080/13658816.2022.2103819
- The Open Landslide Project (OLP), a New Inventory of Shallow Landslides for Susceptibility Models: The Autumn 2019 Extreme Rainfall Event in the Langhe-Monferrato Region (Northwestern Italy) M. Licata et al. 10.3390/geosciences13100289
- Regional susceptibility assessments with heterogeneous landslide information: Slope unit- vs. pixel-based approach L. Jacobs et al. 10.1016/j.geomorph.2020.107084
- LAND-SUITE V1.0: a suite of tools for statistically based landslide susceptibility zonation M. Rossi et al. 10.5194/gmd-15-5651-2022
- Application of the group method of data handling (GMDH) approach for landslide susceptibility zonation using readily available spatial covariates M. Panahi et al. 10.1016/j.catena.2021.105779
- Parameter-free delineation of slope units and terrain subdivision of Italy M. Alvioli et al. 10.1016/j.geomorph.2020.107124
- 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
3 citations as recorded by crossref.
- Preparing first-time slope failures hazard maps: from pixel-based to slope unit-based G. Domènech et al. 10.1007/s10346-019-01279-4
- The influence of land use and land cover change on landslide susceptibility in the Lower Mekong River Basin C. Dandridge et al. 10.1007/s11069-022-05604-4
- Rapid prediction of the magnitude scale of landslide events triggered by an earthquake H. Tanyaş et al. 10.1007/s10346-019-01136-4
Latest update: 20 Nov 2024
Short summary
While producing a landslide susceptibility map using a fieldwork-based landslide inventory and a logistic regression model, two crucial questions came to our minds. (i) Shall we consider unsurveyed regions of the study area, for which landslide absence is typically assumed? (ii) Which reference mapping unit should be used in our model? So we compared four maps and found that rejecting unsurveyed regions together with slope units as reference mapping unit should be the best option.
While producing a landslide susceptibility map using a fieldwork-based landslide inventory and a...
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