Detection of areas prone to flood-induced landslides risk using certainty factor and its hybridization with FAHP, XGBoost and deep learning neural network. Issue 25 (13th December 2022)

• Record Type: Journal Article
• Authors: Costache, Romulus; Ali, Sk Ajim; Parvin, Farhana; Pham, Quoc Bao; Arabameri, Alireza; Nguyen, Hoang; Crăciun, Anca; Anh, Duong Tran
• Is Part Of: Geocarto international. Volume 37:Issue 25(2023)
• Page Start: 7303
• Page End: 7338
• Publication Date: 2022-12-13
• Subjects: Deep learning neural network -- bivariate statistics -- flood-induced landslide susceptibility -- GIS -- India; Remote sensing -- Periodicals; Geographic information systems -- Periodicals; Geology -- Periodicals; Cartography -- Periodicals; 621.3678
• DOI: 10.1080/10106049.2021.1973115
• Languages: English
• ISSNs: 1010-6049

Abstract

Abstract: This article is intended to assess the flood-induced landslide susceptibility in the Indian state of Assam. This study area has high frequency and severity of landslides that are triggered by heavy rainfall and floods. In order to obtain the results, two machine learning models (XGBoost and DLNN) and one of the fuzzy-multi-criteria decision-making methods (FAHP) were combined with certainty factor (CF) bivariate statistic model. Firstly, 16 landslide predictors and 198 flood-induced landslide locations were prepared, this data set being split into training (70%) and validating data sets (30%). The analysis of the results shows that the region's most prone to flood-induced landslide occurrence can be found in the southern part, while those less prone to these phenomena are generally located in the northern part of the study area. Receiver Operating Characteristic (ROC) curve indicator shows that XGBoost-CF is the most performance model (area under the curve [AUC] = 0.977), followed by FAHP-CF (AUC = 0.976), DLNN-CF (AUC= 0.974) and CF (AUC = 0.963).