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The appropriate design of the pond coast structures and the reservoir supplement management entails an accurate pond water surface simulation. A hybrid Genetic Algorithm - Artificial Neural Network (GA-ANN) is presented and applied in this research to estimate the next 3- and 5-day water surfaces. Training and validation of the GAANN is performed using the 4-year daily water surface measurements performed on Chahnimeh reservoir located on the eastern side of Iran. Various input combinations are applied to the GA-ANN method. According to the results, for both the next 3- and 5-day estimation models, the input combination, consisting of the past 2-day water surface data, contributes to the optimal yield. Root Mean Squared Error of the optimum next 3- and 5-day prediction GA-ANN models were obtained to be 0.1798 and 0.3102, respectively. This paper found that in modelling the 3- and 5-day ahead pond water level, the best input variables are the information on the one and two previous days. [ABSTRACT FROM AUTHOR]