Establish An Advanced River Flow Prediction Model Using An Artificial Intelligence Approach

Abstract

Accurate river flow predictions are essential for effective management of water resources, flood control, and environmental protection due to the increasing frequency of extreme weather events and shifting hydrological tendencies. An improved river flow prediction method utilizing artificial intelligence (AI) approaches is presented in this research. To advance the method's accuracy and rate of convergence, the raw river flow data is first normalized to decrease the impact of outliers and guarantee consistency across different scales. To raise the convergence speed and predictive performance, the Refined Northern Goshawk Optimized Deep Neural Network (RNGO-DNN), a novel hybrid method, incorporates optimized optimization methods that were influenced by the hunting tactics of the northern goshawk. When associated with conventional prediction approaches, the method's performance shows greater accuracy and dependability. The proposed method is measured against existing approaches, demonstrating larger performance in forecasting river flow by using metrics such as R-squared (R²) of 0.98, Root Mean Square Error (RMSE) of 0.20, and Mean Absolute Error (MAE) of 0.08 in River flow forecast. The outcomes illustrate that the suggested technique achieves significantly better than conventional prediction methods, providing a reliable tool for forecasting river flow and improving resource management and environmental sustainability.