Artificial neural network model for predicting water inflow into a reservoir

Abstract

RELEVANCE of this study lies in the use of an artificial neural network to predict the volume of water in the Coca reservoir (Coca hydroelectric power station in Ethiopia). As you know, hydropower, being renewable energy, is one of the technologies that produce electricity with the least impact on global climate change. During this time, Ethiopia received about 87% (4,674 MW) of electricity from hydropower. It is one of the countries affected by the problems of climatic phenomena, such as floods, droughts and hurricanes, which affect the potential of hydropower. THE PURPOSE. In order to maintain safe operation, good production efficiency, better water resources management, effective decision-making, accident prevention and early warning and restrictions on electricity production, water volume forecasting is necessary. Which, in turn, is a nonlinear problem, and a multilinear perceptron-type neural network (MLP) is suitable for this purpose. METHODS. In this study, different models with different selected number of nodes and layers were identified, since there is no specific rule for determining the architecture of an artificial neural network. Statistical analysis (mean square error (MSE) and R-squared (R2)) was used to verify the validity of the model by comparing the actual values of water inflow with the predicted values. results. The inflow prediction was carried out using the ANN method based on a multilayer perceptron (MLP). The performance of each model was evaluated using the mean square error (MSE) and efficiency coefficient (R2), which are among the most commonly used statistical methods in hydrological modeling. CONCLUSION. The results obtained show that the models successfully predicted flood runoff over the reservoir.

Keywords

• reservoir inflow
• forecasting
• artificial neural network
• multilayer perceptron
• hydroelectric power station