Physically based shallow translational landslide susceptibility analysis in Tibo catchment, NW of Portugal

Abstract. Rainfall induced shallow landslides cause loss of life and significant damages involving private and public properties, transportation system, etc. Prediction of shallow landslides susceptible locations is a complex task that involves many disciplines: hydrology, geotechnical science, geomorphology, and statistics. Usually to accomplish this task two main approaches are used: statistical or physically based model. Reliable models' applications involve: automatic parameters calibration, objective quantification of the quality of susceptibility maps, model sensitivity analysis. This paper presents a methodology to systemically and objectively calibrate, verify and compare different models and different models performances indicators in order to individuate and eventually select the models whose behaviors are more reliable for a certain case study. The procedure was implemented in package of models for landslide susceptibility analysis and integrated in the NewAge-JGrass hydrological model. The package includes three simplified physically based models for landslides susceptibility analysis (M1, M2, and M3) and a component for models verifications. It computes eight goodness of fit indices by comparing pixel-by-pixel model results and measurements data. Moreover, the package integration in NewAge-JGrass allows the use of other components such as geographic information system tools to manage inputs-output processes, and automatic calibration algorithms to estimate model parameters. The system was applied for a case study in Calabria (Italy) along the Salerno-Reggio Calabria highway, between Cosenza and Altilia municipality. The analysis provided that among all the optimized indices and all the three models, the optimization of the index distance to perfect classification in the receiver operating characteristic plane (D2PC) coupled with model M3 is the best modeling solution for our test case.

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Probabilistic landslide susceptibility analysis in tropical mountainous terrain using the physically based r.slope.stability model

10.5194/nhess-2019-223 ◽

2019 ◽

Author(s):

Johnnatan Palacio Cordoba ◽

Martin Mergili ◽

Edier Aristizábal

Keyword(s):

Slope Stability ◽

Landslide Susceptibility ◽

Confusion Matrix ◽

Hazard Mapping ◽

Infinite Slope ◽

Susceptibility Analysis ◽

Colombian Andes ◽

Infinite Slope Stability Model ◽

Stability Model ◽

Physically Based

Abstract. Landslides triggered by rainfall are very common phenomena in complex tropical environments such as the Colombian Andes, one of the regions most affected by landslides every year. Currently in Colombia, physically based methods for landslide hazard mapping are mandatory for land use planning in urban areas. In this work, we perform probabilistic analyses with r.slope.stability, a spatially distributed, physically based model for landslide susceptibility analysis, available as an open-source tool coupled to GRASS GIS. This model considers alternatively the infinite slope stability model or the 2.5D geometry of shallow planar and deep-seated landslides with ellipsoidal or truncated failure surfaces. We test the model in the La Arenosa catchment, northern Colombian Andes. The results are compared to those yielded with the corresponding deterministic analyses and with other physically based models applied in the same catchment. Finally, the model results are evaluated against a landslide inventory using a confusion matrix and Receiver Operating Characteristic (ROC) analysis. The model performs reasonably well, the infinite slope stability model showing a better performance. The outcomes are, however, rather conservative, pointing to possible challenges with regard to the geotechnical and geo-hydraulic parameterization. The results also highlight the importance to perform probabilistic instead of – or in addition to – deterministic slope stability analyses.

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Physically-based landslide susceptibility analysis using Monte Carlo simulation in a tropical mountain basin

Georisk Assessment and Management of Risk for Engineered Systems and Geohazards ◽

10.1080/17499518.2019.1633582 ◽

2019 ◽

Vol 14 (3) ◽

pp. 192-205 ◽

Cited By ~ 2

Author(s):

Roberto J. Marin ◽

Álvaro J. Mattos

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Landslide Susceptibility ◽

Susceptibility Analysis ◽

Tropical Mountain ◽

Physically Based

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Evaluating performance of simplified physically based models for shallow landslide susceptibility

Hydrology and Earth System Sciences ◽

10.5194/hess-20-4585-2016 ◽

2016 ◽

Vol 20 (11) ◽

pp. 4585-4603 ◽

Cited By ~ 22

Author(s):

Giuseppe Formetta ◽

Giovanna Capparelli ◽

Pasquale Versace

Keyword(s):

Landslide Susceptibility ◽

Model Performance ◽

Shallow Landslides ◽

Model Verification ◽

Sensitivity Analyses ◽

Model Parameters ◽

Parameter Calibration ◽

Susceptibility Analysis ◽

Physically Based ◽

Physically Based Models

Abstract. Rainfall-induced shallow landslides can lead to loss of life and significant damage to private and public properties, transportation systems, etc. Predicting locations that might be susceptible to shallow landslides is a complex task and involves many disciplines: hydrology, geotechnical science, geology, hydrogeology, geomorphology, and statistics. Two main approaches are commonly used: statistical or physically based models. Reliable model applications involve automatic parameter calibration, objective quantification of the quality of susceptibility maps, and model sensitivity analyses. This paper presents a methodology to systemically and objectively calibrate, verify, and compare different models and model performance indicators in order to identify and select the models whose behavior is the most reliable for particular case studies.The procedure was implemented in a package of models for landslide susceptibility analysis and integrated in the NewAge-JGrass hydrological model. The package includes three simplified physically based models for landslide susceptibility analysis (M1, M2, and M3) and a component for model verification. It computes eight goodness-of-fit indices by comparing pixel-by-pixel model results and measurement data. The integration of the package in NewAge-JGrass uses other components, such as geographic information system tools, to manage input–output processes, and automatic calibration algorithms to estimate model parameters. The system was applied for a case study in Calabria (Italy) along the Salerno–Reggio Calabria highway, between Cosenza and Altilia. The area is extensively subject to rainfall-induced shallow landslides mainly because of its complex geology and climatology. The analysis was carried out considering all the combinations of the eight optimized indices and the three models. Parameter calibration, verification, and model performance assessment were performed by a comparison with a detailed landslide inventory map for the area. The results showed that the index distance to perfect classification in the receiver operating characteristic plane (D2PC) coupled with the model M3 is the best modeling solution for our test case.

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Probabilistic landslide susceptibility analysis in tropical mountainous terrain using the physically based r.slope.stability model

Natural Hazards and Earth System Science ◽

10.5194/nhess-20-815-2020 ◽

2020 ◽

Vol 20 (3) ◽

pp. 815-829 ◽

Cited By ~ 1

Author(s):

Johnnatan Palacio Cordoba ◽

Martin Mergili ◽

Edier Aristizábal

Keyword(s):

Slope Stability ◽

Landslide Susceptibility ◽

Confusion Matrix ◽

Hazard Mapping ◽

Infinite Slope ◽

Susceptibility Analysis ◽

Colombian Andes ◽

Infinite Slope Stability Model ◽

Stability Model ◽

Physically Based

Abstract. Landslides triggered by rainfall are very common phenomena in complex tropical environments such as the Colombian Andes, one of the regions of South America most affected by landslides every year. Currently in Colombia, physically based methods for landslide hazard mapping are mandatory for land use planning in urban areas. In this work, we perform probabilistic analyses with r.slope.stability, a spatially distributed, physically based model for landslide susceptibility analysis, available as an open-source tool coupled to GRASS GIS. This model considers alternatively the infinite slope stability model or the 2.5-D geometry of shallow planar and deep-seated landslides with ellipsoidal or truncated failure surfaces. We test the model in the La Arenosa catchment, northern Colombian Andes. The results are compared to those yielded with the corresponding deterministic analyses and with other physically based models applied in the same catchment. Finally, the model results are evaluated against a landslide inventory using a confusion matrix and receiver operating characteristic (ROC) analysis. The model performs reasonably well, the infinite slope stability model showing a better performance. The outcomes are, however, rather conservative, pointing to possible challenges with regard to the geotechnical and geo-hydraulic parameterization. The results also highlight the importance to perform probabilistic instead of – or in addition to – deterministic slope stability analyses.

Download Full-text