INFLOW PREDICTION FOR PROPOSED HALELE WAREBESSA CASCADED RESERVOIRS IN OMO GHIBE BASIN USING ARTIFICIAL NEURAL NETWORKS

Abstract

Operational planning of water resource systems like reservoir and hydropower plants call for real - time forecasting of reservoir inflow. An effective reservoir inflow forecasting enables the reservoir operators to get the accurate information for decision making in planning and operating the reservoirs. An accurate prediction of reservoir inflow as an important measures for effective management strategy such as optimization in operating policies, irrigation, hydropower plants and warning of impending floods or drought condition. The prime aim of this study is used to develop the best ANN model using historical data to predict a real Halele Warebessa reservoir inflow, one day ahead and one month ahead based on different techniques of Neural Network. The best input scenario employs the areal rainfall Rt-2, Rt-1, Rt and lag inflow Qt-2, Qt-1. A total of t wenty years historical data (1989-2008) of daily and monthly areal rainfall and inflow of the catchment is used for Halele and Warebessa reservoir respectively to train and validate networks. Three types of Neural Network Architectures i.e. Multilayer Perceptron (MLP), Radial Basis Function (RBF) and General Feed Forward (GFF) are conducted in study . The number of hidden neurons and Epochs is fixed by trial and error till there is no further improvement on the desired output. The optimum Artificial Neural Network with 5 inputs, 2 neurons in hidden layer and one output is selected. To evaluate the accuracy of the proposed model, the RMSE, MAE, R 2 and NSE are employed and the value proves that MLP is a superior model to GFF and RBF. Finally, MLP network is trained and conveyed to determine inflow to reservoir prediction at R 2 = 0.99 and 0.79, NSE = 0.98 and 0.96, RMSE = 0.0033 and 0.267, for calibration and R 2 = 0.99 and 0.79, NSE= 0.82 and 0.73, RMSE=0.0018 and 4160.9, MAE = 0.0319 and 34.93 validation respectively for Halele and Warebessa by using data subjected to early stopping approach. The overall results reveal that Multilayer perceptron is demonstrating good result for both daily and monthly reservoir inflow predicting of Halele and Warebessa respectively.