Application of novel deep boosting framework-based earthquake induced landslide hazards prediction approach in Sikkim Himalaya. Issue 26 (13th December 2022)

• Record Type: Journal Article
• Authors: Chowdhuri, Indrajit; Pal, Subodh Chandra; Janizadeh, Saeid; Saha, Asish; Ahmadi, Kourosh; Chakrabortty, Rabin; Islam, Abu Reza Md. Towfiqul; Roy, Paramita; Shit, Manisa
• Is Part Of: Geocarto international. Volume 37:Issue 26(2023)
• Page Start: 12509
• Page End: 12535
• Publication Date: 2022-12-13
• Subjects: 6.9Mw earthquake -- landslide -- sikkim -- deep learning -- deep boosting; Remote sensing -- Periodicals; Geographic information systems -- Periodicals; Geology -- Periodicals; Cartography -- Periodicals; 621.3678
• DOI: 10.1080/10106049.2022.2068675
• Languages: English
• ISSNs: 1010-6049

Abstract

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.