Improving flood susceptibility forecasting with an innovative deep learning approach

Abstract

Floods are often regarded as one of the natural disasters that cause the greatest damage, properly it is notoriously challenging to effectively estimate and forecast flooding. Loss of lives and properties are damaged due to flood disaster and that also affects the basic needs of people to live, property damage, agricultural and animal loss, failure of infrastructure facilities, and deterioration of health due to waterborne infections. To overcome this problem, we proposed an advanced water wave optimized adaptive generative adversarial network (AWWO-AGAN) method. The purpose of our proposed AWWO-AGAN method is to predict the flood susceptibility in order to rescue people from flood disaster. The research collected rainfall regions dataset in India for flood susceptibility and the collected data is preprocessed using Continuous Wavelet Transform (CWT) to clean the unnecessary data. Linear Discriminant Analysis (LDA) method is employed to extract the data features. The simulation findings of the result use a Python tool. The findings demonstrated that the suggested framework performed better than other traditional methods. The metrics used to assess the performance of proposed method in terms of accuracy (96%), Precision (93.25%), sensitivity (94%) and specificity (97%).The parameter of specificity provides a high prediction level of flood susceptibility in suspected areas and the results would be beneficial for real-time flood predictions.