Application of novel deep boosting framework-based earthquake induced landslide hazards prediction approach in Sikkim Himalaya

Abstract

A major earthquake (6.9 Moment magnitude) occurred in the Sikkim and Darjeeling areas of the Indian Himalaya as well as in the adjacent Nepal on 18th September 2011, triggering a large number of landslides. A total of 188 landslide locations were extracted in order to create the landslide inventory map (LIM). The earthquake-induced landslide susceptibility maps (LSMs) were created using an Artificial Neural Network (ANN) model and three novel deep learning approaches (DLAs), namely Deep Boosting (DB), Deep Learning Neural Network (DLNN), and Deep Learning Tree (DLT), as well as training points and 22 conditioning factors. The earthquake-induced LSMs validated using several statistical indices and the results showed optimal accuracy for all models, where DB yielding the highest prediction rate curve (PRC) of 98.5%. This is followed by DLT (97%), DLNN (96%), and ANN (91%). The results demonstrate maximum efficacy of the proposed earthquake-induced LSM.