An Ensemble of J48 Decision Tree with AdaBoost and Bagging for Flood Susceptibility Mapping in the Sundarbans of West Bengal, India

Flood is a widespread geomorphic hazard that causes an immense destruction not only in physical terms but also mentally, socially, and economically. To limit its destructive effects, proper planning, cope up ideas, and mitigation strategies are required. So the present study deals with the preparation of flood susceptibility mapping in the Sundarban region of West Bengal, India. The study prepares a flood inventory map and also identifies the collinearity among the factors and their IGR values. The factors selected in the present study include drainage density, drainage proximity, SPI, TWI, annual rainfall, vulnerable embankments, flood inundation, LULC, elevation, slope, curvature, and clay content. Three models and their ensemble approach were used to prepare the flood susceptibility maps. The models used were J48DT, J48DT-AdaBoost, and J48DT-Bagging. Finally, the outputs obtained were validated using six techniques namely, Sensitivity, Specificity, AUC (%), Kappa, MAE, and RMSE.