Articles | Volume 17, issue 7
https://doi.org/10.5194/nhess-17-1091-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/nhess-17-1091-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
10 Jul 2017
Research article |
| 10 Jul 2017
Combination of statistical and physically based methods to assess shallow slide susceptibility at the basin scale
Sérgio C. Oliveira
CORRESPONDING AUTHOR
Centre for Geographical Studies, IGOT (Institute of Geography and Spatial Planning), Universidade de Lisboa, Edifício IGOT,
Rua Branca Edmée Marques, 1600-276 Lisbon, Portugal
José L. Zêzere
Centre for Geographical Studies, IGOT (Institute of Geography and Spatial Planning), Universidade de Lisboa, Edifício IGOT,
Rua Branca Edmée Marques, 1600-276 Lisbon, Portugal
Sara Lajas
Centre for Geographical Studies, IGOT (Institute of Geography and Spatial Planning), Universidade de Lisboa, Edifício IGOT,
Rua Branca Edmée Marques, 1600-276 Lisbon, Portugal
Raquel Melo
Centre for Geographical Studies, IGOT (Institute of Geography and Spatial Planning), Universidade de Lisboa, Edifício IGOT,
Rua Branca Edmée Marques, 1600-276 Lisbon, Portugal
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Cited
15 citations as recorded by crossref.
- Combining data-driven models to assess susceptibility of shallow slides failure and run-out R. Melo et al. 10.1007/s10346-019-01235-2
- Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan J. Dou et al. 10.1007/s10346-019-01286-5
- Debris flow run-out simulation and analysis using a dynamic model R. Melo et al. 10.5194/nhess-18-555-2018
- An integral assessment of landslide dams generated by the occurrence of rainfall-induced landslide and debris flow hazard chain L. Ortiz-Giraldo et al. 10.3389/feart.2023.1157881
- Shallow landslides predisposing and triggering factors in developing a regional early warning system D. Tiranti et al. 10.1007/s10346-018-1096-8
- Applying a Series and Parallel Model and a Bayesian Networks Model to Produce Disaster Chain Susceptibility Maps in the Changbai Mountain area, China L. Han et al. 10.3390/w11102144
- Understanding Constraints and Triggering Factors of Landslides: Regional and Local Perspectives on a Drainage Basin C. Jesus et al. 10.3390/geosciences8010002
- Modelling landslide hazards under global changes: the case of a Pyrenean valley S. Bernardie et al. 10.5194/nhess-21-147-2021
- Nationwide Susceptibility Mapping of Landslides in Kenya Using the Fuzzy Analytic Hierarchy Process Model S. Zhou et al. 10.3390/land9120535
- GIS-Based Random Forest Weight for Rainfall-Induced Landslide Susceptibility Assessment at a Humid Region in Southern China P. Wang et al. 10.3390/w10081019
- Comparison of hybrid data-driven and physical models for landslide susceptibility mapping at regional scales X. Wei et al. 10.1007/s11440-023-01841-4
- Landslide susceptibility assessment in the rocky coast subsystem of Essaouira, Morocco A. Khouz et al. 10.5194/nhess-22-3793-2022
- An artificial intelligence-based approach to predicting seismic hillslope stability under extreme rainfall events in the vicinity of Wolsong nuclear power plant, South Korea A. Pradhan & Y. Kim 10.1007/s10064-021-02138-0
- Application of hybrid machine learning model for flood hazard zoning assessments J. Wang et al. 10.1007/s00477-022-02301-3
- Improving pixel-based regional landslide susceptibility mapping X. Wei et al. 10.1016/j.gsf.2024.101782
15 citations as recorded by crossref.
- Combining data-driven models to assess susceptibility of shallow slides failure and run-out R. Melo et al. 10.1007/s10346-019-01235-2
- Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan J. Dou et al. 10.1007/s10346-019-01286-5
- Debris flow run-out simulation and analysis using a dynamic model R. Melo et al. 10.5194/nhess-18-555-2018
- An integral assessment of landslide dams generated by the occurrence of rainfall-induced landslide and debris flow hazard chain L. Ortiz-Giraldo et al. 10.3389/feart.2023.1157881
- Shallow landslides predisposing and triggering factors in developing a regional early warning system D. Tiranti et al. 10.1007/s10346-018-1096-8
- Applying a Series and Parallel Model and a Bayesian Networks Model to Produce Disaster Chain Susceptibility Maps in the Changbai Mountain area, China L. Han et al. 10.3390/w11102144
- Understanding Constraints and Triggering Factors of Landslides: Regional and Local Perspectives on a Drainage Basin C. Jesus et al. 10.3390/geosciences8010002
- Modelling landslide hazards under global changes: the case of a Pyrenean valley S. Bernardie et al. 10.5194/nhess-21-147-2021
- Nationwide Susceptibility Mapping of Landslides in Kenya Using the Fuzzy Analytic Hierarchy Process Model S. Zhou et al. 10.3390/land9120535
- GIS-Based Random Forest Weight for Rainfall-Induced Landslide Susceptibility Assessment at a Humid Region in Southern China P. Wang et al. 10.3390/w10081019
- Comparison of hybrid data-driven and physical models for landslide susceptibility mapping at regional scales X. Wei et al. 10.1007/s11440-023-01841-4
- Landslide susceptibility assessment in the rocky coast subsystem of Essaouira, Morocco A. Khouz et al. 10.5194/nhess-22-3793-2022
- An artificial intelligence-based approach to predicting seismic hillslope stability under extreme rainfall events in the vicinity of Wolsong nuclear power plant, South Korea A. Pradhan & Y. Kim 10.1007/s10064-021-02138-0
- Application of hybrid machine learning model for flood hazard zoning assessments J. Wang et al. 10.1007/s00477-022-02301-3
- Improving pixel-based regional landslide susceptibility mapping X. Wei et al. 10.1016/j.gsf.2024.101782
Latest update: 22 Apr 2024
Short summary
Approaches to assess shallow slide susceptibility at the basin scale are conceptually different depending on the use of statistical or physically based methods. Two hypotheses are tested: (i) both methods generate similar shallow slide susceptibility results and (ii) the combination of both susceptibility maps generates a more reliable susceptibility model. Model combinations registered a higher predictive capacity and the identification of areas where the results from both models are uncertain.
Approaches to assess shallow slide susceptibility at the basin scale are conceptually different...
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