ABSTRACT This work delves into the assessment of 12 selected terrain factors responsible for landslides in the Mao Maran region of North‐East Himalaya in India and includes a comparative study of the 2020 landslide susceptibility map (LSM) with a projected 2030 LSM. The terrain factors considered in this study are land use and land cover (LULC), stream power index (SPI), geology, 7aspect, relative relief, soil, profile curvature, road buffer, topographic wetness index (TWI), drainage buffer, fault/fold/thrust (FFT) and slope. A detailed investigation of how the selected terrain factors influence landslides in the study area was conducted using the weight of evidence method and the final LSM was generated using a random forest model. Future LULC map simulation was done using the artificial neural network cellular automata model (ANN‐CA). Additionally, LULC maps for the years 2000, 2010, 2020 and 2030 were generated to identify differences in physical parameters over time. Change detection revealed a decrease in dense forest classes and an increase in scrubland, settlement, and barren land classes. LSM maps for 2020 and 2030 were generated using the receiver operating characteristic (ROC) curve, with prediction accuracies of 88.1% for 2020 and 85.5% for 2030. The LULC factor played a significant role in influencing landslides, and changes in LULC inferred a substantial impact on the landslide conditioning scenario. This research can be useful for planning, mitigation, and development activities in the study area.
Khusulio et al. (Mon,) studied this question.