Hybrid deep learning model for flood frequency assessment and flood forecasting

Abstract

The most common and persistent natural hazard to people across the globe is flooding. The frequency of floods in a given place is defined as the likelihood and intensity of floods occurring there within a certain period. Examining historical flood data and using techniques are often used to determine the likelihood that a flood of a certain size would occur in a specific location. The method of flood prediction involves making forecasts on the frequency and severity of flooding. It may be influenced by a number of factors, including the topography, river flow, soil moisture content, and the period of rainfall. In this research, we provide a novel Cat Swarm Optimized Spatial Adversarial Network (CSO-SAN) technique for predicting and assessing flood frequency. This technique simulates the yearly greatest flow at the river Mahanadi measurement sites at Andhiyarkore, Bamanidhi, Baronda, and Kurubhatta over 60 years. The CSO-SAN model is adapted for the flood forecasting component to predict the frequency and size of future floods. The model incorporates real-time data from various sources, such as meteorological predictions and information on river flow, to anticipate the probability and severity of upcoming floods. Compared to other conventional statistical techniques and forecasting models, the CSO-SAN model outperformed them in tests conducted on the Mahanadi river basins. The model offers a viable method for improving the precision of flood frequency evaluation and flood forecasting, with significant advantages for managing and reducing flood risk.