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In hydrology and water resource engineering, water flow forecasting is of great importance for getting the information about the river engineering, dam structure design and water-related inflow demand management. In order to prevent flooding on the downstream side of the river during the rainy season, sufficient outflow from a barrage should be maintained. It is very difficult to predict the desired water flow using physically-based models and conventional regression-based methods due to the nonlinear and fuzzy nature of hydrological activity and scarcity of relevant data. These traditional methods are incapable to handle the complex non-linearity and non-stationarity process of water flow. Thus, the aim of this study is to develop intelligent hybrid artificial intelligence model, namely genetic algorithm based Artificial Neural Network (GA-ANN) for monthly Water Flow prediction in the Mahanadi river system. All …