Ensemble approach to develop landslide susceptibility map in landslide dominated Sikkim Himalayan region, India

Chowdhuri, Indrajit; Pal, Subodh Chandra; Arabameri, Alireza; Ngo, Phuong Thao Thi; Chakrabortty, Rabin; Malik, Sadhan; Das, Biswajit; Roy, Paramita

doi:10.1007/s12665-020-09227-5

Cited by 74 publications

(29 citation statements)

References 72 publications

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“…In other words, MC test is required to ensure the independent conditioning factors in a dataset (Chen et al, 2020). Tolerance (TOL) and variance inflation factors (VIF) techniques have been used by several researchers to test MC analyzes (Chowdhuri et al, 2020a). Therefore, in this study, we also used these two techniques and their respective equations were calculated as follows:…”

Section: Multi-collinearity (Mc) Analysismentioning

confidence: 99%

Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping

et al. 2021

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The optimal prediction of land subsidence (LS) is very much difficult because of limitations in proper monitoring techniques, field-base surveys and knowledge related to functioning and behavior of LS. Thus, due to the lack of LS susceptibility maps it is almost impossible to identify LS prone areas and as a result it influences severe economic and human losses. Hence, preparation of LS susceptibility mapping (LSSM) can help to prevent natural and human catastrophes and reduce the economic damages significantly. Machine learning (ML) techniques are becoming increasingly proficient in modeling purpose of such kinds of occurrences and they are increasing used for LSSM. This study compares the performances of single and hybrid ML models to preparation of LSSM for future prediction of performance analysis. In this study, the spatial prediction of LS was assessed using four ML models of maximum entropy (MaxEnt), general linear model (GLM), artificial neural network (ANN) and support vector machine (SVM). Alongside, the possible numbers of novel ensemble models were integrated through the aforementioned four ML models for optimal analysis of LSSM. An inventory LS map was prepared based on the previous occurrences of LS points and the dataset were divvied into 70:30 ratios for training and validating of the modeling process. To identify the robust and best LSSMs, receiver operating characteristic-area under curve (ROC-AUC) curve was employed. The ROC-AUC result indicated that ANN model gives the highest ROC-AUC (0.924) in training accuracy. The highest AUC (0.823) of the LSSMs was determined based on validation datasets identified by SVM followed by ANN-SVM (0.812).

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Section: Multi-collinearity (Mc) Analysismentioning

confidence: 99%

Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping

et al. 2021

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“…The Hítardalur landslide, (Figure 3), is of the same kind as in Sölvadalur (Section 6.1), but much larger, about 10,900,000 m 3…”

Section: The Slidementioning

confidence: 75%

“…In more complex terrains than the rectangular channel, the flow model is basically the same as long as there is laminar flow. As a basic flow model for the velocity vector Û for small slopes we have Equations (2) and (3). Furthermore Û will be proportional to the gradient ∇•z, z being the geographical elevation level, z = f(x,y) and ∇•z its gradient vector.…”

Section: Pseudostationary Flow and Slope Stabilitymentioning

confidence: 99%

“…The subject of this paper is hazardous landslides, their physics, and the possibility of modeling them as a part of a disaster prevention procedure. This task begins when a site has been identified as susceptible using a procedure that can involve advanced remote sensing technology [1] and the production of landslide susceptibility maps [2,3] (Figure 6; Table 5). Models can be involved in this work, e.g., GIS based models [4].…”

Section: Introductionmentioning

confidence: 99%

“…Models can be involved in this work, e.g., GIS based models [4]. Statistical models may apply [3] (p. 6.…”

Section: Introductionmentioning

confidence: 99%

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Physics and Modeling of Various Hazardous Landslides

Eliasson¹,

Sæmundsson²

2021

Geosciences

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In 2014, the Varnes classification system for landslides was updated. Complex landslides can still be a problem to classify as the classification does not include the flow type in the hydrodynamical sense. Three examples of Icelandic landslides are presented and later used as case studies in order to demonstrate the methods suggested to analyze the flow. The methods are based on the different physical properties of the flow types of the slides. Three different flow types are presented, named type (i), (ii), and (iii). Types (i) and (ii) do not include turbulent flows and their flow paths are sometimes independent of the velocity. Type (iii) include high velocity flows; they are treated with the translator wave theory, where a new type of a slope factor is used. It allows the slide to stop when the slope has flattened out to the value that corresponds to the stable slope property of the flowing material. The type studies are for a fast slide of this type, also a large slip circle slide that turns into a fast-flowing slide farther down the path and finally a large slide running so fast that it can run for a kilometer on flat land where it stops with a steep front.

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Landslide Susceptibility mapping using random forest and extreme gradient boosting: A case study of Fengjie, Chongqing

Zhang

Liu

et al. 2023

Geological Journal

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Landslide susceptibility analysis can provide theoretical support for landslide risk management. However, some susceptibility analyses are not sufficiently interpretable. Moreover, the accuracy of many research methods needs to be improved. Therefore, this study can supplement these deficiencies. This study aims to research the evaluation effects of random forest (RF) and extreme gradient boosting (XGBoost) classifier models on landslide susceptibility, and to compare their applicability in Fengjie County, Chongqing, a typical landslide‐prone area in southwest of China. Firstly, 1624 landslides information from 1980 to 2020 were obtained through field investigation, and a geospatial database of 16 conditional factors had been constructed. Secondly, non‐landslide points were selected to form a complete data set and RF and XGBoost models were established. Finally, the area under the ROC curve (AUC) value, accuracy, and F‐score were used to compare the two models. The results show that even though both classifiers have a highly accurate evaluation of landslide susceptibility, the RF model performs better. In comparison, the RF model has a higher AUC value of 0.866, and its accuracy and F‐score are approximately 2% higher than XGBoost. The land use, elevation, and lithology of Fengjie County contribute to the occurrence of landslides. This is due to human engineering activities (such as land reclamation, and housing construction) resulting in low slope stability and landslides in widely distributed sandstone, siltstone, and mudstone layers owing to their low permeability and planes of weakness.

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Ensemble approach to develop landslide susceptibility map in landslide dominated Sikkim Himalayan region, India

Cited by 74 publications

References 72 publications

Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping

Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping

Physics and Modeling of Various Hazardous Landslides

Landslide Susceptibility mapping using random forest and extreme gradient boosting: A case study of Fengjie, Chongqing

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