Artificial Neural Network Model for Forecasting Haditha Reservoir Inflow in the West of Iraq

Abstract

Water resources such as rivers and reservoirs are affected by multiple factors such as climate change and rainfall patterns. Therefore, forecasting river discharges becomes crucial for effective water resource management. River discharge forecasting techniques, such as artificial neural models, provide a framework for analyzing and predicting river flow patterns over specific periods. These forecasts can be used to better manage water resources through water resource planning, dam operation, and flood prevention. Forecasting river discharges improve water resource management and make sustainable strategic decisions to optimize water use and reduce risks associated with floods and water scarcity. The study aims to use artificial neural network models to predict the daily discharge of the Euphrates River upstream of Haditha reservoir in Anbar province. Two different models were used: The feed-forward back Propagation single layer (FFBP) neural network and the Multilayer Perceptron (MLP) neural network. The models were trained and tested using daily discharge time series data (2018-2023). The results showed that the Multilayer neural network structure with the combination (3-12-6-1) was better than the single layer (FFBP) based on the coefficient of determination (R ² ) values of 0.90 for the second model MLP and 0.88 for the first model (FFBP) and the values root mean square error (RMSE) of 52.64 and 14.97, respectively. The results revealed the effect of time delay and dependence on previous discharges on the inputs of artificial neural network models (ANNs). A time delay of three days turned out to be the best and provided excellent performance for both models.