Field based index of flood vulnerability (IFV): A new validation technique for flood susceptible models

The flood hazard management is one of the major challenges in the floodplain regions worldwide. With the rise in population growth and the spread of infrastructural development, the level of risk has increased over time. Therefore, the prediction of flood susceptible area is a key challenge for the adoption of management plans. Flood susceptibility modeling is technically a common work, but it is still a very tough job to validate flood susceptible models in a very rigorous and scientific manner. Therefore, the present work in the Atreyee River Basin of India and Bangladesh was planned to establish artificial neural network (ANN), radial basis function (RBF), random forest (RF) and their ensemble-based flood susceptibility models. The flood susceptible models were constructed based on nine flood conditioning parameters. The flood susceptibility models were validated in a conventional way using the receiver operating curve (ROC). To validate the flood-susceptible models, a two dimensional (2D) hydraulic flood simulation model was developed. Also, the index of flood vulnerability model was developed and applied for validating the flood susceptible models, which was a very unique way to validate the predictive models. Friedman test and Wilcoxon Signed rank test were employed to compare the generated flood susceptible models. Results showed that 11.95%–12.99% of the entire basin area (10188.4 km2) comes under very high flood-susceptible zones. Accuracy evaluation results have shown that the performance of ensemble flood susceptible models outperforms other standalone machine learning models. The flood simulation model and IFV model were also spatially adjusted with the flood susceptibility models. Therefore, the present study recommended for the ensemble flood susceptibility prediction and IFV based validation along with conventional ways