Reservoir Operation by Artificial Neural Network Model ( Mosul Dam –Iraq, as a Case Study)

Abstract

Reservoir operation forecasting plays an important role in managing water resources systems. Artificial Neural Network (ANN) model was applied for Mosul-Dam reservoir which is located on Tigris River, which the objectives of water resources development and flood control. Feed-forward multi-layer perceptions (MLPs) are used and trained with the back-propagation algorithm, as they have a high capability of data mapping. The data set has a period of 23 years from 1990 to 2012..The Input data were inflow (It), evaporation (Et), rainfall (Rt), reservoir storage (St) and outflow (Ot). The best convergence after more than 1000 trials was achieved for the combination of inflow (It), inflow (It-1), inflow (It-2), evaporation (Et), reservoir storage (St), rainfall (Rt), outflow (Ot-1) and outflow (Ot-2) with error tolerance, learning rate, momentum rate, number of cycles and number of hidden layers as 0.001, 1, 0.9,50000 and 9 respectively. The coefficient of determination (R2) and MAPE were (0.972) and (17.15) respectively. The results of ANN models for the training, testing and validation were compared with the observed data. The predicted values from the neural networks matched the measured values very well. The application of ANN technique and the predicted equation by using the connection weights and the threshold levels, assist the reservoir operation decision and future updating, also it is an important Model for finding the missing data. The ANN technique can accurately predict the monthly Outflow.