Mapping Landslide Susceptibility for Sustainable Development: A Case Study of Meghalaya’s Southwest Khasi Hills

The growing hazard of landslides offers a severe obstacle to comprehensive development initiatives, needing appropriate methodologies for measuring landslide vulnerability. Susceptibility mapping, a critical tool in this endeavor, assists in identifying at-risk locations and directing proactive mitigation actions. This research focuses on the Meghalaya Southwest Khasi Hills area, characterized by complicated terrain, steep slopes, and excessive rainfall, resulting in significant mortality and property damage from landslides. The work entails creating a landslide inventory map utilizing historical landslide data separated into various training and testing datasets. The Analytical Hierarchy Process (AHP) technique creates a landslide susceptibility map (LSM) central to the work. This technique combines many factors, including slope, aspect, elevation, curvature, rainfall, land use land cover (LULC), and normalized difference vegetation index (NDVI), to create an LSM. The LSM is classified into four levels: very low susceptibility (9%), low susceptibility (26%), moderate susceptibility (50%), and high susceptibility (15%), clearly displaying its regional dispersion. From the results, it is observed that slopes near drainage and transportation networks are more vulnerable. Model validation is conducted through the ROC curve, which confirms the LSM dependability. The LSM performs well in detecting landslide-prone locations, with an accuracy rate curve of 0.724. The LSM is vital for detecting landslide-prone areas, reducing hazards, guiding safe land development, improving land-use rules, promoting safety, sustainable development, and risk reduction.