Application of static and dynamic artificial neural networks for forecasting inflow discharges, case study: Sefidroud Dam reservoir

Abstract

Accurate forecasting of reservoir inflow is of great importance in water resources planning and management and it can highly affect decisions and policies of reservoir operation with respect to flood control, drought management, water supply, and hydropower generation. In this study, different models of static and dynamic artificial neural networks (ANN) including static feed forward neural network (FFNN), non-linear autoregressive (NAR), and nonlinear autoregressive with exogenous inputs (NARX) are employed in order to forecast Sefidroud Dam reservoir inflows. The capability of studied networks with a range of different input variables in predicting reservoir inflows are then compared. All employed models are trained using inflow discharge and precipitation data with different time delays and an optimum number of neurons in the hidden layers are obtained. In addition, the time index (T) is also employed as the input data to the proposed models in order to increase the accuracy of the estimates. The obtained results indicate that NAR dynamic neural network has a better performance in comparison with FFNN and NARX models. Furthermore, using 12 time delays for inflow discharges and precipitation data leads to the best accessible results where adding time index (T) increases the accuracy. The results obtained from this study provide useful information for reservoir inflow simulation. In other words, these results are critical for water resources management and planning particularly in the field of dam reservoir operation which is crucial under reported water crisis in Iran.