MaxEnt Machine Learning Technique based Assessment of Landslide Susceptibility of West Nayar Basin (Garhwal Himalaya), Uttarakhand, India

Landslide susceptibility prediction mapping plays an imperative role in hazard mitigation by prioritizing areas for intervention and implementing effective risk reduction measures, thereby safeguarding communities and infrastructure. In this current assessment, 121 landslide occurrences and eight landslide-conditioning parameters were considered to develop a landslide susceptibility model for the West Nayar Basin (WNB), Uttarakhand, India. The Maximum Entropy multivariate statistical model (MaxEnt) was applied to calibrate and assess landslide susceptibility. The ensemble model data reveal that 2.69% and 7.31% of the WNB area are classified as very highly and highly susceptible to landslides, respectively. Meanwhile, around 65% of the basin is designated as a safe zone with a lower risk of landslides, and 25% of the area is identified as having a moderate probability of landslide risk. The major and frequent occurrence of landslides in the WNB is linked to low to middle elevations, proximity to rivers, and motorable roads. Consequently, the resulting model and observed patterns highlight the major variables that cause landslides and their corresponding significance. This modeling approach provides baseline data at a regional scale, which can enhance economic development planning in the WNB by informing better land use and watershed management practices. Integrating such models into planning processes ensures more resilient infrastructure and communities, promoting sustainable development in landslide-prone areas.