Understanding the scale effects of topographical variables on landslide susceptibility mapping in Sikkim Himalaya using deep learning approaches

Abstract

In geomorphological hazard studies, selecting DEM data with the proper spatial resolution is necessary for optimal analysis of prediction performance. Henceforth, accurate resolution of DEM data in landslide susceptibility study is also crucial in this perspective. This study determines the scale effects of DEM derived hydro-topographic factors in LS mapping in the Rangpo river basin, Sikkim Himalaya, India. Five different DEM data i.e., ALOS (12.5 m), and AW3D30, SRTM, ASTER and Cartosat-1 with each 30 m resolution were used in this study. Three neural network algorithms were applied to produce LSM. The results of this investigation revealed that, among the three employed neural network techniques, the deep learning algorithm with ALOS DEM data performed the best. The proposed unique approach i.e., combination of scale effects and deep learning algorithm can be useful to produce precise LSMs in hilly areas around the globe, and will be helpful for sustainable development.