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

Every year, many countries carry out landslide susceptibility analyses to establish and manage countermeasures and reduce the damage caused by landslides. Because increases in the areas of landslides lead to new landslides, there is a growing need for landslide prediction to reduce such damage. Among the various methods for landslide susceptibility analysis, statistical methods require information about the landslide occurrence point. Meanwhile, analysis based on physical slope models can estimate stability by considering the slope characteristics, which can be applied based on information about the locations of landslides. Therefore, in this study, a probabilistic method based on a physical slope model was developed to analyze landslide susceptibility. To this end, an infinite slope model was used as the physical slope model, and Monte Carlo simulation was applied based on landslide inventory including landslide locations, elevation, slope gradient, specific catchment area (SCA), soil thickness, unit weight, cohesion, friction angle, hydraulic conductivity, and rainfall intensity; deterministic analysis was also performed for the comparison. The Mt. Umyeon area, a representative case for urban landslides in South Korea where large scale human damage occurred in 2011, was selected for a case study. The landslide prediction rate and receiver operating characteristic (ROC) curve were used to estimate the prediction accuracy so that we could compare our approach to the deterministic analysis. The landslide prediction rate of the deterministic analysis was 81.55%; in the case of the Monte Carlo simulation, when the failure probabilities were set to 1%, 5%, and 10%, the landslide prediction rates were 95.15%, 91.26%, and 90.29%, respectively, which were higher than the rate of the deterministic analysis. Finally, according to the area under the curve of the ROC curve, the prediction accuracy of the probabilistic model was 73.32%, likely due to the variability and uncertainty in the input variables.

Download Full-text

GIS-Based Probabilistic Analysis of Shallow Landslide Susceptibility Using a Transient Hydrogeological Model and Monte Carlo Simulation

Landslide Science for a Safer Geoenvironment ◽

10.1007/978-3-319-05050-8_70 ◽

2014 ◽

pp. 451-456 ◽

Cited By ~ 1

Author(s):

Jung-Hyun Lee ◽

Hyuck-Jin Park

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Probabilistic Analysis ◽

Landslide Susceptibility ◽

Shallow Landslide ◽

Hydrogeological Model

Download Full-text

Evaluating performances of simplified physically based models for landslide susceptibility

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-12-13217-2015 ◽

2015 ◽

Vol 12 (12) ◽

pp. 13217-13256 ◽

Cited By ~ 5

Author(s):

G. Formetta ◽

G. Capparelli ◽

P. Versace

Keyword(s):

Landslide Susceptibility ◽

Goodness Of Fit ◽

Shallow Landslides ◽

Model Parameters ◽

Test Case ◽

Susceptibility Analysis ◽

Physically Based ◽

Private And Public ◽

Physically Based Models

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.

Download Full-text

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.

Download Full-text

HIRESSS: a physically based slope stability simulator for HPC applications

Natural Hazards and Earth System Science ◽

10.5194/nhess-13-151-2013 ◽

2013 ◽

Vol 13 (1) ◽

pp. 151-166 ◽

Cited By ~ 67

Author(s):

G. Rossi ◽

F. Catani ◽

L. Leoni ◽

S. Segoni ◽

V. Tofani

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Slope Stability ◽

Real Time ◽

Unsaturated Soil ◽

Hydrological Model ◽

Pressure Head ◽

Stability Model ◽

Physically Based ◽

Geotechnical Stability

Abstract. HIRESSS (HIgh REsolution Slope Stability Simulator) is a physically based distributed slope stability simulator for analyzing shallow landslide triggering conditions in real time and on large areas using parallel computational techniques. The physical model proposed is composed of two parts: hydrological and geotechnical. The hydrological model receives the rainfall data as dynamical input and provides the pressure head as perturbation to the geotechnical stability model that computes the factor of safety (FS) in probabilistic terms. The hydrological model is based on an analytical solution of an approximated form of the Richards equation under the wet condition hypothesis and it is introduced as a modeled form of hydraulic diffusivity to improve the hydrological response. The geotechnical stability model is based on an infinite slope model that takes into account the unsaturated soil condition. During the slope stability analysis the proposed model takes into account the increase in strength and cohesion due to matric suction in unsaturated soil, where the pressure head is negative. Moreover, the soil mass variation on partially saturated soil caused by water infiltration is modeled. The model is then inserted into a Monte Carlo simulation, to manage the typical uncertainty in the values of the input geotechnical and hydrological parameters, which is a common weak point of deterministic models. The Monte Carlo simulation manages a probability distribution of input parameters providing results in terms of slope failure probability. The developed software uses the computational power offered by multicore and multiprocessor hardware, from modern workstations to supercomputing facilities (HPC), to achieve the simulation in reasonable runtimes, compatible with civil protection real time monitoring. A first test of HIRESSS in three different areas is presented to evaluate the reliability of the results and the runtime performance on large areas.

Download Full-text

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.

Download Full-text

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

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

Landslides ◽

10.1007/s10346-014-0494-9 ◽

2014 ◽

Vol 12 (3) ◽

pp. 455-468 ◽

Cited By ~ 7

Author(s):

Manuel Teixeira ◽

Carlos Bateira ◽

Fernando Marques ◽

Bianca Vieira

Keyword(s):

Landslide Susceptibility ◽

Susceptibility Analysis ◽

Translational Landslide ◽

Physically Based

Download Full-text

Applicability of a physically based soil water model (SWMOD) in design flood estimation in eastern Australia

Hydrology Research ◽

10.2166/nh.2016.118 ◽

2016 ◽

Vol 48 (6) ◽

pp. 1652-1665 ◽

Cited By ~ 1

Author(s):

Melanie Loveridge ◽

Ataur Rahman ◽

Peter Hill

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Soil Water ◽

Flood Frequency Analysis ◽

Simulation Framework ◽

Design Flood ◽

Quasi Monte Carlo ◽

Eastern Australia ◽

Physically Based ◽

Loss Models

Abstract Event-based rainfall–runoff models are useful tools for hydrologic design. Of the many loss models, the ‘initial loss-continuing loss’ model is widely adopted in practice. Some of the key limitations with these types of loss models include the arbitrary selection of initial moisture (IM) conditions and lack of physically meaningful parameters. This paper investigates the applicability of a physically based soil water balance model (SWMOD) with distributed IM conditions for flood modelling. Four catchments from the east coast of New South Wales, Australia, are modelled. The IM content in SWMOD represents the antecedent moisture condition. A quasi-Monte Carlo simulation framework is adopted, where the IM is stochastically varied according to a lognormal probability distribution. In calibration, it is found that the adopted modelling framework is able to simulate the majority of the observed flood hydrographs with a higher degree of accuracy; however, in a design context, when compared to the results of conventional flood frequency analysis, discrepancies are noted for a range of annual exceedance probabilities. The quasi-Monte Carlo simulation framework proved to be useful in assessing the effect of the IM content on design flood estimates.

Download Full-text