Prediction of Daily Flow to the Great Zab River Using Artificial Neural Network Models

The daily flow of rivers is one the most important components hydrological cycle and plays an role in planning management various water resources projects, as process predicting such very necessary operation reservoirs, to prevent flooding estimating abundance or scarcity. This study aims use two types neural network models predict Great Zab river basin Northern Iraq region for period (2012- 2021). Two Artificial Neural Networks (ANNs) are investigated evaluated forecasting river. first Feed Forward Back Propagation (FFBP), second Multi-Layer Perceptron Network (MLP). Data has been analyzed by comparing simulation outputs delivered with performance indices named (a) correlation coefficient root mean square errors, which can be denoted (R^2) (RMSE) respectively. results showed that MLP structured (3-14-7-1), able flows Eski-kelek station on squared-errors (0.91, 51.7),