GIS-based landslide susceptibility zonation and comparative analysis using analytical hierarchy process and conventional weighting-based multivariate statistical methods in the Lachung River Basin, North Sikkim

Wadadar, Sudatta; Mukhopadhyay, Bhabani Prasad

doi:10.1007/s11069-022-05344-5

Cited by 9 publications

(2 citation statements)

References 51 publications

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“…There have been plenty of excellent works in landslide susceptibility evaluation (LSE, also called as landslide susceptibility mapping, LSM), and a variety of algorithms were suggested or employed in these works. These diverse methods typically consist of logic regression (Shou and Chen, 2021;Ge et al, 2022), weights of evidence (Goyes-Penafiel and Hernandez-Rojas, 2021), fuzzy logic (Nwazelibe et al, 2023), Analytical Hierarchy Process (AHP) (Wadadar and Mukhopadhyay, 2022), Information value (Es-Smairi et al, 2022), statistical index model (Berhane and Tadesse, 2021), support vector machine (SVM) (Daviran et al, 2022), random forest (RF) (Taalab et al, 2018), convolutional neural network (CNN) (Aslam et al, 2022), recurrent neural network (Ngo et al, 2021), and ensemble learning [such as boosted regression tree-random forest (Chowdhuri et al, 2021), random forest-cusp catastrophe model (Sun et al, 2022), CNN with metaheuristic optimization (Hakim et al, 2022), and so on]. Hakim et al (2022) suggested two ensemble deep learning models including the ensemble of CNN and grey wolf optimizer (GWO) and the complex model of CNN and imperialist competitive algorithm (ICA).…”

Section: Introductionmentioning

confidence: 99%

Landslide susceptibility evaluation based on active deformation and graph convolutional network algorithm

Wang

et al. 2023

Front. Earth Sci.

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Disastrous landslides have become a focus of the world’s attention. Landslide susceptibility evaluation (LSE) can predict where landslides may occur and has caught the attention of scientists all over the world. This work establishes integrated criteria of potential landslide recognition and combines the historical landslides and newly-identified potential landslides to improve the accuracy, rationality, and practicability of a LSE map. Moreover, slope units can well reflect the topographic constraint to landslide occurrence and development, and Graph Convolutional Network (GCN) can well portray the topological and feature relation among various slope units. The combination of slope units and GCN is for the first time employed in LSE. This work focuses on Wanzhou District, a famous landslide-serious region in the Three Gorges reservoir area, and employs multisource data to conduct potential landslide recognition and LSE and to reveal the distribution characteristics of high landslide susceptibility. Some new viewpoints are suggested as follows. 1) The established criteria of potential landslide recognition consist of the characteristics of active deformation, stratum and lithology, tectonics, topography, micro-geomorphology, environment, meteorology, earthquakes, and human engineering activity. These criteria can well eliminate 4 types of false alarm regions and is successfully validated by field survey. 2) 34 potential landslides are newly discovered, and the movement of these potential landslides were controlled or induced by the combined action of soft-hard interbedding rock mass, steep topography, frequent tectonic movement, strong fluvial erosion, abundant precipitation, and intensive road and building construction. 3) The GCN algorithm reaches a relatively high accuracy (AUC: 0.941) and outperforms the other representative machine learning algorithms of Convolutional Neural Network (AUC: 0.926), Support Vector Machine (AUC: 0.835), and CART Tree (AUC: 0.762). 4) High landslide susceptibility is caused by the coupled action of weathered rock cavities, soft rock and swelling soil, strong river erosion, abundant rainfall, and intensive human engineering activity.

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Section: Introductionmentioning

confidence: 99%

Landslide susceptibility evaluation based on active deformation and graph convolutional network algorithm

Wang

et al. 2023

Front. Earth Sci.

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“…The physical model requires detailed geotechnical parameters and is mainly aimed at the stability analysis of a single slope. Heuristic models, such as AHP, mainly rely on the experience of experts and are easily affected by the subjective opinions of experts [14,15]. Machine learning models can be divided into statistical machine learning and deep learning.…”

Section: Introductionmentioning

confidence: 99%

Improving the Accuracy of Regional Engineering Disturbance Disaster Susceptibility by Optimizing Weight Calculation Methods—A Case Study in the Himalayan Area, China

Song

Guo

et al. 2023

Sustainability

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The information value method is widely used in predicting the susceptibility of geological disasters. However, most susceptibility evaluation models assume that the weight of each influencing factor is equal, which is inconsistent with the actual situation. Therefore, this paper studies the optimization effect of weight calculation method on the information value model. Engineering disturbance disasters are developing in the Himalayan alpine valley in southeastern Tibet. First of all, this paper takes this as the research object and builds a database of engineering disturbance disasters in southeast Tibet through long-term on-site investigation. Then, the relationship between the influencing factors such as slope, aspect, relief, elevation, engineering geological rock formation, rainfall, temperature, and seismic peak acceleration and the distribution of engineering disturbance disasters is analyzed. Finally, the principal component analysis method and logistic regression method are employed to calculate the weight coefficients. Moreover, the susceptibility of engineering disturbance disasters is predicted using the information value model (IV-Only), as well as two weighted information value models (PCA-IV and LR-IV). In addition, the accuracy of these three susceptibility evaluation models is assessed based on two evaluation indexes. The results show that: compared with the equal weight method and the principal component analysis method, the logistic regression method has the highest accuracy. According to the weight coefficient, the control factors of engineering disturbance disasters in the Himalayan alpine canyon area are determined to be slope, aspect, rainfall, and elevation. The research results provide a reference method for the optimization of susceptibility evaluation model.

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Establishing a GIS-based evaluation method considering spatial heterogeneity for debris flow susceptibility mapping at the regional scale

Qin

Qiao²,

Yao³

et al. 2022

Nat Hazards

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GIS-based landslide susceptibility zonation and comparative analysis using analytical hierarchy process and conventional weighting-based multivariate statistical methods in the Lachung River Basin, North Sikkim

Cited by 9 publications

References 51 publications

Landslide susceptibility evaluation based on active deformation and graph convolutional network algorithm

Landslide susceptibility evaluation based on active deformation and graph convolutional network algorithm

Improving the Accuracy of Regional Engineering Disturbance Disaster Susceptibility by Optimizing Weight Calculation Methods—A Case Study in the Himalayan Area, China

Establishing a GIS-based evaluation method considering spatial heterogeneity for debris flow susceptibility mapping at the regional scale

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