Mapping Land Subsidence Risk Using GIS and Slope-Based Regression: A Case Study from Meghalaya

Land subsidence is a critical geohazard that threatens infrastructure stability, environmental sustainability, and public safety, particularly in data-scarce and terrain-sensitive regions. This study presents a GIS-based framework for mapping land subsidence susceptibility using a slope-based polynomial regression approach. Twenty national and international case studies were reviewed to identify key environmental and hydrological factors influencing subsidence. Statistical correlation and regression analysis revealed slope as the most influential parameter, exhibiting a strong non-linear relationship with subsidence occurrence. The developed regression model was applied to a selected road corridor in Meghalaya using slope data derived from Digital Elevation Models (DEMs) within a GIS environment. The resulting susceptibility map categorizes the study area into low, moderate, and high-risk zones, which show strong agreement with known patterns of terrain instability. The proposed approach offers a cost-effective and scalable method for preliminary subsidence risk assessment in regions lacking extensive field monitoring and provides valuable insights for infrastructure planning and hazard mitigation.