Assessment of spatial distribution of rain-induced and earthquake-triggered landslides using geospatial techniques along North Sikkim Road Corridor in Sikkim Himalayas, India

Koley, Bappaditya; Nath, Anindita; Saraswati, Subhajit; Chatterjee, Uday; Bandyopadhyay, Kaushik; Bhatta, Basudeb; Ray, Bidhan Chandra

doi:10.1007/s10708-022-10585-9

Cited by 10 publications

(2 citation statements)

References 71 publications

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“…The occurrence of landslide disasters causes great losses to the economy and human life all over the world every year [1,2]. Natural events such as rainfall [3,4], earthquakes [5,6] and floods [7] often lead to a series of landslides.…”

Section: Introductionmentioning

confidence: 99%

An Efficient User-Friendly Integration Tool for Landslide Susceptibility Mapping Based on Support Vector Machines: SVM-LSM Toolbox

Huang

Ding

et al. 2022

Remote Sensing

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Landslide susceptibility mapping (LSM) is an important element of landslide risk assessment, but the process often needs to span multiple platforms and the operation process is complex. This paper develops an efficient user-friendly toolbox including the whole process of LSM, known as the SVM-LSM toolbox. The toolbox realizes landslide susceptibility mapping based on a support vector machine (SVM), which can be integrated into the ArcGIS or ArcGIS Pro platform. The toolbox includes three sub-toolboxes, namely: (1) influence factor production, (2) factor selection and dataset production, and (3) model training and prediction. Influence factor production provides automatic calculation of DEM-related topographic factors, converts line vector data to continuous raster factors, and performs rainfall data processing. Factor selection uses the Pearson correlation coefficient (PCC) to calculate the correlations between factors, and the information gain ratio (IGR) to calculate the contributions of different factors to landslide occurrence. Dataset sample production includes the automatic generation of non-landslide data, data sample production and dataset split. The accuracy, precision, recall, F1 value, receiver operating characteristic (ROC) and area under curve (AUC) are used to evaluate the prediction ability of the model. In addition, two methods—single processing and multiprocessing—are used to generate LSM. The prediction efficiency of multiprocessing is much higher than that of the single process. In order to verify the performance and accuracy of the toolbox, Wuqi County, Yan’an City, Shaanxi Province was selected as the test area to generate LSM. The results show that the AUC value of the model is 0.8107. At the same time, the multiprocessing prediction tool improves the efficiency of the susceptibility prediction process by about 60%. The experimental results confirm the accuracy and practicability of the proposed toolbox in LSM.

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Section: Introductionmentioning

confidence: 99%

An Efficient User-Friendly Integration Tool for Landslide Susceptibility Mapping Based on Support Vector Machines: SVM-LSM Toolbox

Huang

Ding

et al. 2022

Remote Sensing

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“…Additionally, since rainfall can significantly increase the frequency of landslides on the D-658 highway, Kerem Hepdeniz [ 8 ] et al selected rainfall as an indicator to assess the landslide susceptibility of the region, which provide effective landslide management strategies for the managers and stakeholders. Shu-Rong Yang [ 9 ] used a discriminant analysis approach to map landslide susceptibility in predicting rainfall-induced landslides in central Taiwan, and Bappaditya Koley [ 10 ] et al used geospatial techniques to analyze the spatial distribution of rainfall-induced landslides along North Sikkim Road Corridor in Sikkim Himalayas, India. Given that rainfall plays a significant role in landslide occurrence, most studies considered rainfall as an assessment indicator and ignored the interrelations between rainfall and landslide.…”

Section: Introductionmentioning

confidence: 99%

Refined Zoning of Landslide Susceptibility: A Case Study in Enshi County, Hubei, China

Wang

Qiu

et al. 2022

IJERPH

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At present, landslide susceptibility assessment (LSA) based on the characteristics of landslides in different areas is an effective prevention measure for landslide management. In Enshi County, China, the landslides are mainly triggered by high-intensity rainfall, which causes a large number of casualties and economic losses every year. In order to effectively control the landslide occurrence in Enshi County and mitigate the damages caused by the landslide. In this study, eight indicators were selected as assessment indicators for LSA in Enshi County. The analytic hierarchy process (AHP) model, information value (IV) model and analytic hierarchy process-information value (AHP-IV) model were, respectively, applied to assess the landslide distribution of landslides in the rainy season (RS) and non-rainy season (NRS). Based on the three models, the study area was classified into five levels of landslide susceptibility, including very high susceptibility, high susceptibility, medium susceptibility, low susceptibility, and very low susceptibility. The receiver operating characteristic (ROC) curve was applied to verify the model accuracy. The results showed that the AHP-IV model (ROC = 0.7716) was more suitable in RS, and the IV model (ROC = 0.8237) was the most appropriate model in NRS. Finally, combined with the results of landslide susceptibility in RS and NRS, an integrated landslide susceptibility map was proposed, involving year-round high susceptibility, RS high susceptibility, NRS high susceptibility and year-round low susceptibility. The integrated landslide susceptibility results provide a more detailed division in terms of the different time periods in a year, which is beneficial for the government to efficiently allocate landslide management funds and propose effective landslide management strategies. Additionally, the focused arrangement of monitoring works in landslide-prone areas enable collect landslide information efficiently, which is helpful for the subsequent landslide preventive management.

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Correlating Circular Failure Charts, Limit Equilibrium and Finite Element Method Based Safety Factor for Circular Failure Type Landslides

Varshney,

Siddique,

Ahamad

et al. 2024

Advances in Natural and Technological Hazards Research

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Assessment of spatial distribution of rain-induced and earthquake-triggered landslides using geospatial techniques along North Sikkim Road Corridor in Sikkim Himalayas, India

Cited by 10 publications

References 71 publications

An Efficient User-Friendly Integration Tool for Landslide Susceptibility Mapping Based on Support Vector Machines: SVM-LSM Toolbox

An Efficient User-Friendly Integration Tool for Landslide Susceptibility Mapping Based on Support Vector Machines: SVM-LSM Toolbox

Refined Zoning of Landslide Susceptibility: A Case Study in Enshi County, Hubei, China

Correlating Circular Failure Charts, Limit Equilibrium and Finite Element Method Based Safety Factor for Circular Failure Type Landslides

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