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

Abstract

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 D² (MD), and support vector machine (SVM), to calibrate and compare four maps of landslide susceptibility. The results demonstrated the outperformance of Mahalanobis D² 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.

Keywords:

• Landslide susceptibility modelling
• landslide conditioning
• landscape vulnerability
• Boyce Index
• risk assessment
• Himalaya

Acknowledgements

The authors of the study are greatly acknowledging late Dr R. S. Rawal, India KSLCDI programme coordinator, for his continuous encouragement to make the programme successful. Special gratitude to the ICIMOD and team for their overall guidance and funding support during the study.

Disclosure statement

The authors have no conflicts of interest to declare.

Authors’ contributions

Conceptualization and supervision: AP, MSS, SP, DP, GS, NC and SK; writing review and editing: AP, MSS, SP, DP, GS, SK, NC, APS and APM; data curation and formal analysis: AP, MSS, SP, DP, GS, SK and NC; evidence collection, review, and editing: AP, MSS, SP, DP, GS, SK, NC, APS, APM, RC, HGA, HA and MAM

Data availability statement

All necessary data supporting the findings are available in the manuscript. If any additional data would be required by the readers, the same would be shared by the authors electronically whenever it is required.

Additional information

Funding

This project was funded by Princess Nourah bint Abdulrahman University Research Supporting Project Number PNURSP2022R241, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. This research was supported by the Institutes of the Uttarakhand Space Application Centre (USAC), G.B. Pant National Institute of Himalayan Environment (GBP-NIHE), the Wildlife Institute of India (WII), and the Ministry of Environment, Forests and Climate Change (MoEF&CC) India.