Flood susceptibility mapping in Brahmaputra floodplain of Bangladesh using deep boost, deep learning neural network, and artificial neural network

Floods are considered one of the most destructive natural hydro-meteorological disasters across the world. The present study attempts to assess flood susceptibility of the Brahmaputra floodplain of Bangladesh using Deep Boost, Deep Learning Neural Network, and Artificial Neural Network. Primarily, flood inventory maps were prepared from fieldworks and satellite image classification. Consequently, the flood locations were segregated into 70% training and 30% validation samples randomly for running the models and validating the models, respectively. The complete procedure is designed to be considered 12 flood conditioning criteria under four relevant components. The efficiency assessment of DLNN, DB, and ANN models using validation data through the area under the curve (AUC) reveals that DB demonstrates higher accuracy (AUC= .917) than DLNN (AUC = .901) and ANN (AUC= .895) approaches. Therefore, proposed susceptibility mapping approaches are efficient in assessing flood susceptibility accurately and can be implemented in other flood-affected regions.