Hybrid MCDM–Isolation Forest approach for spatiotemporal landslide mapping

Landslide assessment requires static factors defining baseline conditions and dynamic factors acting as triggers. However, integrating various factors into susceptibility assessments increases uncertainty in multi-criteria decision making (MCDM) frameworks and reduces the accuracy of data-driven models. This study proposes a novel landslide susceptibility assessment for Yunnan, China, integrating factor weights from MCDM and one-class classification. The approach advances susceptibility mapping by incorporating 18 static and dynamic factors, including variability in precipitation, soil moisture, vegetation, and land cover. Monte Carlo simulations validated the factor-weighting scheme of Best–Worst Method and Isolation Forest, identifying slope angle and precipitation (mean and variability) as dominant drivers, reflecting the combined influence of steep terrain and monsoonal rainfall in Yunnan. The susceptibility map reveals that 15 % of Yunnan falls into the very low, 27 % into the low, 28 % into the moderate, 22 % into the high, and 8 % into the very high susceptibility classes. This indicates that a substantial portion of the province remains highly susceptible. Susceptibility classes were examined using analysis of variance. Slope angle, mean precipitation, and soil moisture emerged as the most influential factors in distinguishing landslide-prone zones. These findings supports spatiotemporal adaptive planning and targeted mitigation strategies to reduce landslide risk in Yunnan.