Landslide Susceptibility Zonation With Special Emphasis on Tectonic Features for Occurrence of Landslides in Lower Indian Himalaya

Nath, Ritu Rituraj; Sharma, M. L.; Goswami, Ajanta; Sweta, Kumari; Pareek, Naveen

doi:10.1007/s12524-020-01285-3

Cited by 12 publications

(3 citation statements)

References 27 publications

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“…There are different views on the contribution of this factor to landslide susceptibility. Some scholars believe that the fault in the region is very important to the occurrence of landslides (Demir, 2019; Guo et al, 2015; Kanwal et al, 2017; Nath et al, 2021), because the faulting can easily lead to the development of steep cliffs, so that the nearby rock mass fracture is serious and then cause landslides. On the contrary, some scholars believe that the occurrence of landslides is weakly related to faults (D. L. Sun et al, 2021; D. L. Sun, Xu, et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

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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Section: Discussionmentioning

confidence: 99%

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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“…In addition, the quantization process of the 12 LSM factors is as follows: Calculate aspect, slope, TRI 46 , and SPI 9 using spatial analysis tools in ArcGIS 10.8 software based on the digital elevation model; According to the topographic map and geological map, the lithology of the study area is divided into: hard rock, soft and hard alternation rock, and soft rock 36 ; The distance to fault 47 , the distance to drainage network 48 , and the distance to road 49 were obtained using the Euclidean distance method in the Spatial Distance Analysis Tool; The inverse distance weighting method was used to spatially interpolate the annual average rainfall data of each meteorological station to obtain the average annual rainfall in the study area 22 ; Surface cover factors have a great influence on slope stability 50 . NDVI was calculated by using the red band and near-infrared band of a 30 m resolution image of LandSAT-8 40 .…”

Section: Methodsmentioning

confidence: 99%

“…The distance to fault 47 , the distance to drainage network 48 , and the distance to road 49 were obtained using the Euclidean distance method in the Spatial Distance Analysis Tool;…”

Section: Methodsmentioning

confidence: 99%

Comparative study on landslide susceptibility mapping based on unbalanced sample ratio

Jiang

et al. 2023

Sci Rep

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The Zigui–Badong section of the Three Gorges Reservoir area is used as the research area in this study to research the impact of unbalanced sample sets on Landslide Susceptibility Mapping (LSM) and determine the sample ratio interval with the best performance for different models. We employ 12 LSM factors, five training sample sets with different sample ratios (1:1, 1:2, 1:4, 1:8, and 1:16), and C5.0, Support Vector Machine (SVM), Logistic Regression (LR), and one-dimensional Convolution Neural Network (CNN) models are used to obtain landslide susceptibility index and landslide susceptibility zoning in the study area, respectively. The prediction performance of the model is evaluated by the receiver operating characteristic curve area under the curve value, five statistical methods, and specific category precision. The results show that the CNN, SVM, and LR models in the sample ratio of 1:2 achieve better performance than on the balanced sample set, which indicates the importance of the unbalanced sample set in training the LSM modeling. The C5.0 model is always in a state of overfitting in this study and needs to be further studied. The conclusions put forward in this study help improve the scientificity and reliability of LSM.

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Earthquake Induced Landslide Hazard Evaluation for Seismic Microzonation: A Case Study of the Garhwal Himalayas

Nath

Sharma

Pareek

et al. 2023

Springer Tracts in Civil Engineering

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Landslide Susceptibility Zonation With Special Emphasis on Tectonic Features for Occurrence of Landslides in Lower Indian Himalaya

Cited by 12 publications

References 27 publications

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

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

Comparative study on landslide susceptibility mapping based on unbalanced sample ratio

Earthquake Induced Landslide Hazard Evaluation for Seismic Microzonation: A Case Study of the Garhwal Himalayas

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