Rainfall induced landslide susceptibility mapping using novel hybrid soft computing methods based on multi-layer perceptron neural network classifier. Issue 10 (21st July 2022)

• Record Type: Journal Article
• Authors: Sahana, Mehebub; Pham, Binh Thai; Shukla, Manas; Costache, Romulus; Thu, Do Xuan; Chakrabortty, Rabin; Satyam, Neelima; Nguyen, Huu Duy; Phong, Tran Van; Le, Hiep Van; Pal, Subodh Chandra; Areendran, G.; Imdad, Kashif; Prakash, Indra
• Is Part Of: Geocarto international. Volume 37:Issue 10(2022)
• Page Start: 2747
• Page End: 2771
• Publication Date: 2022-07-21
• Subjects: Landslide susceptibility -- soft computing -- multi-layer perception neural network -- ensemble techniques -- India; Remote sensing -- Periodicals; Geographic information systems -- Periodicals; Geology -- Periodicals; Cartography -- Periodicals; 621.3678
• DOI: 10.1080/10106049.2020.1837262
• Languages: English
• ISSNs: 1010-6049

Abstract

Abstract: In this study, we have investigated rainfall induced landslide susceptibility of the Uttarkashi district of India through the developmentof different novel GIS based soft computing approaches namely Bagging-MLPC, Dagging-MLPC, Decorate-MLPC which are a combination Multi-layer Perceptron Neural Network Classifier ( MLPC) and Bagging, Dagging, and Decorate ensemble methods, respectively. The proposed models were trained and validated with the help of 103 historical landslide events (divided into 2 samples: training (70%) and validation (30%)) and 12 landslide conditioning factors. The accuracy of the models was evaluated using different statistical methods including Area Under Curve (AUC) of Receiver Operating Characteristic (ROC). The results show that though performance of all the studied models is good (AUC > 0.80) but of the hybrid Bagging-MLPC model is the best (AUC:0.965). Therefore, this newly hybrid model (Bagging-MLPC) can be used for the accurate landslide susceptibility mapping and assessment of landslide prone areas for landslide prevention and management.