Geological and machine learning based landslide risk assessment in the context of community knowledge and perception

Aizawl, located in Mizoram, northeast India, faces persistent landslide risk due to fragile geology, steep terrain, and unregulated urban expansion. This study assesses the correspondence between community perceived landslide drivers and scientifically derived factors. Research included questionnaire survey, field investigation, and Random Forest based susceptibility modelling (LSM). The survey indicated 66.9% of respondents had experienced landslides. Heavy rainfall (86.2%) and poor drainage (69.0%) were the dominant perceived triggers. Risk perception was statistically uniform across demographic groups (all p > 0.05). Causal knowledge showed a significant positive association with preparedness behaviour (Spearman ρ = 0.479, p < 0.001). The LSM classified 30.5% of the municipal area as very high susceptibility, capturing 78.8% of events (ROC-AUC = 0.726; F1 = 0.793); all failures coincided with settlement zones (38.3%). Geotechnical investigation showed moderate to high rock mass weathering. Out of 15 sites, 13 were classified as Zone IV (LHZ) and rated Bad (SMR). Planar, wedge, and toppling failures were confirmed. Cross scheme analysis demonstrated perfect rank agreement between LHZ and LSM hazard drivers (Spearman ρ = 1.000, p < 0.001), with directional alignment between community perceived causes and LSM factors (ρ = 0.600). These results establish a quantitative basis for community integrated landslide assessment in Aizawl; broader applicability requires validation under site specific geological and socio-cultural conditions.