Fast physically-based model for rainfall-induced landslide susceptibility assessment at regional scale

Medina, Vicente; Hürlimann, Marcel; Guo, Zhengfu; Lloret, A.; Vaunat, Jean

doi:10.1016/j.catena.2021.105213

Cited by 102 publications

(43 citation statements)

References 57 publications

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“…In addition, landslides appear to have clustered in moderate elevations. This fits with the results in some other study areas (Catani et al, 2013;Medina et al, 2021). On one side, topography in low elevations is generally flat.…”

Section: Landslide Inventory Mappingsupporting

confidence: 91%

“…(ii) This factor also has been considered in some previous studies (Catani et al, 2013). The influence of rainfall on slope stability is mainly manifested in three aspects (Guo et al, 2020;Medina et al, 2021): The first is the softening of rock and soil by rainfall infiltration, which weakens them. The second is the hydrostatic pressure and hydrodynamic pressure formed by rainfall infiltration, and its floating force constitutes an unfavorable factor for slope stability.…”

Section: Landslide Causal Factorsmentioning

confidence: 99%

“…Based on the division of evaluation units and the selection of environmental factors within study areas, selecting a suitable model is of importance to obtain effective results (Ahmed, 2015). According to previous literature, landslide susceptibility models can generally be divided into four categories: heuristic models, deterministic models, statistical statistics, and machine learning models (Huang et al, 2017;Sezer et al, 2017;Broeckx et al, 2018;Reichenbach et al, 2018;Medina et al, 2021). Among these models, heuristic models can be considered as knowledge-based models which depend much on the experts' opinions on the geomorphology and historical landslides; thus they are highly subjective (CastellanosAbella and Van Westen, 2008).…”

Section: Introductionmentioning

confidence: 99%

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Large-Scale Landslide Susceptibility Mapping Using an Integrated Machine Learning Model: A Case Study in the Lvliang Mountains of China

et al. 2021

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Integration of different models may improve the performance of landslide susceptibility assessment, but few studies have tested it. The present study aims at exploring the way to integrating different models and comparing the results among integrated and individual models. Our objective is to answer this question: Will the integrated model have higher accuracy compared with individual model? The Lvliang mountains area, a landslide-prone area in China, was taken as the study area, and ten factors were considered in the influencing factors system. Three basic machine learning models (the back propagation (BP), support vector machine (SVM), and random forest (RF) models) were integrated by an objective function where the weight coefficients among different models were computed by the gray wolf optimization (GWO) algorithm. 80 and 20% of the landslide data were randomly selected as the training and testing samples, respectively, and different landslide susceptibility maps were generated based on the GIS platform. The results illustrated that the accuracy expressed by the area under the receiver operating characteristic curve (AUC) of the BP-SVM-RF integrated model was the highest (0.7898), which was better than that of the BP (0.6929), SVM (0.6582), RF (0.7258), BP-SVM (0.7360), BP-RF (0.7569), and SVM-RF models (0.7298). The experimental results authenticated the effectiveness of the BP-SVM-RF method, which can be a reliable model for the regional landslide susceptibility assessment of the study area. Moreover, the proposed procedure can be a good option to integrate different models to seek an “optimal” result.

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Section: Landslide Inventory Mappingsupporting

confidence: 91%

Section: Landslide Causal Factorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Large-Scale Landslide Susceptibility Mapping Using an Integrated Machine Learning Model: A Case Study in the Lvliang Mountains of China

et al. 2021

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“…A lot of models such as types of expert-based models, statistical models, physically-based models, and machine learning models have been proposed for LSP (Guzzetti et al, 1999;Huang et al, 2017;Sezer et al, 2017;Reichenbach et al, 2018;Medina et al, 2021), and it is a crucial step to select an appropriate model (Marjanović et al, 2011;Tien et al, 2015;Huang et al, 2020b). Huang et al (2020c) have compared these types of models and found that machine learning models can more accurately reflect the nonlinear relationships between landslide susceptibility indices; they ignore the complex physical processes involved in landslide initiation, and have been considered more accurate than other approaches.…”

Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Zoning Using C5.0 Decision Tree, Random Forest, Support Vector Machine and Comparison of Their Performance in a Coal Mine Area

Tao

Mei

et al. 2021

Front. Earth Sci.

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The main purpose of this study is to establish an effective landslide susceptibility zoning model and test whether underground mined areas and ground collapse in coal mine areas seriously affect the occurrence of landslides. Taking the Fenxi Coal Mine Area of Shanxi Province in China as the research area, landslide data has been investigated by the Shanxi Geological Environment Monitoring Center; adopting the 5-fold cross-validation method, and through Geostatistics analysis means the datasets of all non-landslides and landslides were divided into 80:20 proportions randomly for training and validating models. A set of 15 condition factors including terrain, geological, hydrological, land cover, and human engineering activity factors (distance to road, distance to mined area, ground collapse density) were selected as the evaluation indices to construct the susceptibility assessment model. Three machine learning algorithms for landslide susceptibility prediction (LSP) including C5.0 Decision Tree (C5.0), Random Forest (RF), and Support Vector Machine (SVM) have been selected and compared through the Areas under the Receiver Operating Characteristics (ROC) Curves (AUC), and several statistical estimates. The study revealed that for these three models the value range of prediction accuracies vary from 83.49 to 99.29% (in the training stage), and 62.26–73.58% (in the validation stage). In the two stages, AUCs are between 0.92 to 0.99 and 0.71 to 0.80 respectively. Using Jenks Natural Breaks algorithm, three LSPs levels are established as very low, low, medium, high, and very high probability of landslide by dividing the indices of the LSP. Compared with RF and SVM, C5.0 is considered better in five categories according to quantities and distribution of the landslides and their area percentage for different LSP zones. Four factors such as distance to road, lithology, profile curvature, and ground collapse density are the most suitable condition factors for LSP. The distance to mine area factor has a medium contribution and plays an obvious role in the occurrence of landslides in all the models. The result reveals that C5.0 possesses better prediction efficiency than RF and SVM, and underground mined area and ground collapse sifnigicantly affect significantly the occurrence of landslides in the Fenxi Coal Mine Area.

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“…In the literature, several ML methods and statistical techniques have been proposed to evaluate the LSMs (e.g. Pradhan et al 2010;Nefeslioglu et al 2012;Wang et al 2016;Dagdelenler et al 2016;Sevgen et al 2019;Kocaman et al 2020;Medina et al 2021;Can et al 2021;Bera et al 2021;Qi et al 2021). In recent years, a signi cant rise in LSMs produced by using data-driven ML methods has been observed.…”

Section: Introductionmentioning

confidence: 99%

Ground-Truth based Performance Assessment of Landslide Susceptibility Mapping: A Case Study after Elazig Earthquake (24 Jan 2020, Mw 6.8), Turkey

Karakas¹,

Kocaman²,

Gökçeoğlu³

2021

Preprint

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Quality assessment for landslide susceptibility maps (LSMs) is essential to increase their usability. Ground-truth based quality assessment is crucial for performance evaluation of LSM production methods. Landslides triggered by earthquakes can be employed for this purpose as they occur frequently throughout the active seismic regions of the world. After an earthquake occurred in Elazig, Turkey on 24 Jan 2020 (Mw 6.8), several landslides were activated in the mountainous parts. Here, the performances of two state-of-the-art machine learning methods, i.e., random forest (RF) and multi-layer perceptron (MLP), were investigated using the activated landslides. The landslide inventory was derived by using pre- and post-event aerial photogrammetric datasets; whereas only the pre-event datasets were utilized for LSM production. The inventory includes activity classes of inactive (L1) and active mass movements (L2), areas having new active zones occurred after 2018 inside the existing landslide (L3), and new activity after 2018 (L4). The L1 and L2 type landslides were used in model training, while L3 and L4 type landslides triggered by the earthquake were used for validation. The area under curve (AUC) values and volumetric change maps obtained from the pre- and post-event datasets were also used for the performance assessment. Furthermore, only a part of the study area was used for model training while the LSMs were produced for the whole area. The results demonstrated that RF exhibited higher classification accuracy (AUC = 0.93) than MLP (AUC = 0.87); and accurate LSMs could be produced by using a sub-part of the basin for training.

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Fast physically-based model for rainfall-induced landslide susceptibility assessment at regional scale

Cited by 102 publications

References 57 publications

Large-Scale Landslide Susceptibility Mapping Using an Integrated Machine Learning Model: A Case Study in the Lvliang Mountains of China

Large-Scale Landslide Susceptibility Mapping Using an Integrated Machine Learning Model: A Case Study in the Lvliang Mountains of China

Landslide Susceptibility Zoning Using C5.0 Decision Tree, Random Forest, Support Vector Machine and Comparison of Their Performance in a Coal Mine Area

Ground-Truth based Performance Assessment of Landslide Susceptibility Mapping: A Case Study after Elazig Earthquake (24 Jan 2020, Mw 6.8), Turkey

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