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A platform for research: civil engineering, architecture and urbanism
Comprehensive Analysis of Landslide Susceptibility Factors in Assam: A Case Study
Landslides are significant natural hazards that can gravely damage infrastructure, cause fatalities, and disrupt socioeconomic activity in landslide prone areas. The Dima Hasao District of the state of Assam in northeast India is one such area. To lower danger from landslides, accurate mapping of landslide susceptibility is crucial. The foundation of the study was the gathering of various geographic data and records of prior landslides. These data were integrated in a Geographic Information System (GIS) for analysis and modelling. Landslide susceptibility mapping was then carried out using Binary Logistic Regression. Utilizing proper statistical metrics and validation methods, and employing Receiver Operating Characteristic (ROC) curves and Area Under Curve (AUC), the model’s performance was evaluated. The most trustworthy model was determined by comparing the prediction potency of the calibrated regression equation. By applying the chosen model to the study area, the landslide susceptibility map was produced. The study facilitated an understanding of the most dominant factors of landslides in the area. The generated map divided the region into zones with varying levels of landslide vulnerability, such as very low, low, medium, high and very high. The landslide susceptibility map can assist decision-makers in identifying areas at high risk of landslides and implementing appropriate mitigation measures. The findings from this research provide valuable information for land-use planning, infrastructure development, and disaster management in Dima Hasao District of Assam, and show that the method can be used to develop landslide susceptibility maps for other landslide-prone regions of the world.
Comprehensive Analysis of Landslide Susceptibility Factors in Assam: A Case Study
Landslides are significant natural hazards that can gravely damage infrastructure, cause fatalities, and disrupt socioeconomic activity in landslide prone areas. The Dima Hasao District of the state of Assam in northeast India is one such area. To lower danger from landslides, accurate mapping of landslide susceptibility is crucial. The foundation of the study was the gathering of various geographic data and records of prior landslides. These data were integrated in a Geographic Information System (GIS) for analysis and modelling. Landslide susceptibility mapping was then carried out using Binary Logistic Regression. Utilizing proper statistical metrics and validation methods, and employing Receiver Operating Characteristic (ROC) curves and Area Under Curve (AUC), the model’s performance was evaluated. The most trustworthy model was determined by comparing the prediction potency of the calibrated regression equation. By applying the chosen model to the study area, the landslide susceptibility map was produced. The study facilitated an understanding of the most dominant factors of landslides in the area. The generated map divided the region into zones with varying levels of landslide vulnerability, such as very low, low, medium, high and very high. The landslide susceptibility map can assist decision-makers in identifying areas at high risk of landslides and implementing appropriate mitigation measures. The findings from this research provide valuable information for land-use planning, infrastructure development, and disaster management in Dima Hasao District of Assam, and show that the method can be used to develop landslide susceptibility maps for other landslide-prone regions of the world.
Comprehensive Analysis of Landslide Susceptibility Factors in Assam: A Case Study
Lecture Notes in Civil Engineering
Pandey, Manish (editor) / Jayakumar, K. V. (editor) / Pal, Manali (editor) / Singh, Vijay P. (editor) / Saikia, Dibyajyoti (author) / Goswami, Monomoy (author) / Zaman, Rakibur (author) / Kalita, Madhurjya (author)
International Conference on Hydraulics, Water Resources and Coastal Engineering ; 2023 ; Warangal, India
Soft Computing and Geospatial Techniques in Water Resources Engineering ; Chapter: 10 ; 155-171
2024-12-02
17 pages
Article/Chapter (Book)
Electronic Resource
English
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