Multivariate statistical algorithms for landslide susceptibility assessment in Kailash Sacred landscape, Western Himalaya

• Published in: Geomatics, natural hazards and risk Vol. 14; no. 1
• Main Authors: Pandey, Arvind, Shekhar Sarkar, Mriganka, Palni, Sarita, Parashar, Deepanshu, Singh, Gajendra, Kaushik, Saurabh, Chandra, Naveen, Costache, Romulus, Pratap Singh, Ajit, Pratap Mishra, Arun, Almohamad, Hussein, Al-Mutiry, Motrih, Abdo, Hazem Ghassan
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 31.12.2023; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Algorithms; Baseline studies; Boyce Index; Generalized linear models; Hazard mitigation; Himalaya; landscape vulnerability; landslide conditioning; Landslide susceptibility modelling; Landslides; Landslides & mudslides; Mathematical models; Maximum entropy; Modelling; Mountain regions; Mountainous areas; Multivariate analysis; Regional development; Regional planning; risk assessment; Statistical analysis; Statistical models; Support vector machines; Vulnerability
• ISSN: 1947-5705; 1947-5713
• DOI: 10.1080/19475705.2023.2227324

Landslide susceptibility mapping plays an imperative role in mitigating hazards and determining the future direction of developmental activities in mountainous regions. Here, we used 518 landslide occurrences and nine landslide-conditioning parameters to build landslide vulnerability models in the Kailash Sacred Landscape (KSL), India. Four multivariate statistical models were applied, namely the generalized linear model (GLM), maximum entropy (MaxEnt), Mahalanobis D 2 (MD), and support vector machine (SVM), to calibrate and compare four maps of landslide susceptibility. The results demonstrated the outperformance of Mahalanobis D 2 for predictability compared to other models obtained from the area under the receiver operating characteristic curve (ROC). The ensemble model data shows that 10.5% of the landscape has susceptible conditions for future landslides, whereas 89.50% of the landscape falls under the safe zone. The occurrence of landslides in the KSL is linked to the middle elevations, vicinity to water bodies, and the motorable roads. Furthermore, the observed patterns and the resulting models exhibit the major variables that cause landslides and their respective significance. The current modelling approach could provide baseline data at the regional scale to improve the developmental planning in the KSL.