In water distribution network calibration of quality models, bulk decay and wall decay coefficients are considered as the adjustable parameters. Bulk decay coefficient usually is gained by using a laboratory bottle test method, but the wall decay coefficient is calibrated with field data of chlorine residual at nodes. In this research, a method is presented to adjust the wall decay coefficients of pipes. Therefore, a metamodeling approach is developed by the combination of Ant Colony

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... n (ACO) algorithm and an Artificial Neural Network (ANN) with the EPANET simulator. The proposed method used on a two-loop test example and real water distribution network. Results showed that proposed method can increase the speed of model 58 times more rapidly than simple method in two-loop network. In the real network, the classification based on the average flow velocity produced the best results among all categories and classification based on material, diameter, and age of pipes produced the best results among the physical criteria. Also comparison of results between the measured and calculated data for testing data showed that the average error is equal to 3.85 percent and the calibration model has a good performance.