In water distribution network calibration of quality models, bulk and wall decay coefficients are considered to be the adjustable parameters. The bulk decay coefficient is usually gained by using a laboratory bottle test method, but the wall decay coefficient is calibrated with field data of residual chlorine at nodes. This paper aims to present a method to adjust the wall decay coefficients of pipes. A metamodelling approach is developed by the combination of an Ant Colony Optimization (ACO) algorithm and an artificial neural network (ANN) with the EPANET simulator. The proposed method is applied on a two-loop test example and real water distribution network. Results showed that the proposed method can increase the speed of solution 58 times more rapidly than the simple method in the two-loop network. In the real network, the classification based on the average flow velocity produced the best results among all categories, and the 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 an average error of 3.85% and the calibration model gave good performance.

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