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When manually calibrating a water quality model, considerable time and attention are required to calibrate each water quality variable and model parameter. Hence, developing an automated model that allows for efficient and objective automatic calibration is highly desirable. The QUAL2Kw model calibrates the QUAL2K model automatically using a genetic algorithm (GA). As the calibration results of a GA vary strongly with the performance criteria used as the objective function in GA optimization problems, this study compares and analyzes auto-calibration results and selects the optimal criterion for each objective from among 6 performance criteria: CV(RMSE), R2, NSE, PBIAS, RSR, and SSNR. Additionally, a multi-objective auto-calibration was conducted using two kinds of performance statistics as the objective function of the GA. The auto-calibration model was applied to the Youngsan River in Korea. The TMDL was established to achieve water quality goals at specific target points. Among the 6 auto-calibration results based on a single performance criterion, NSE was the best criterion for calculating fitness through auto-calibration. When the calibration accuracies of the TMDL target points and the entire river are considered simultaneously, an objective function using multiple performance criteria, specifically CV(RMSE) and RSR, was selected as the final auto-calibration of the model. * Masterminded EasyChair and created the first stable version of this document † Created the first draft of this document Engineering EPiC Series in Engineering Volume 3, 2018, Pages 447–455 HIC 2018. 13th International Conference on Hydroinformatics G. La Loggia, G. Freni, V. Puleo and M. De Marchis (eds.), HIC 2018 (EPiC Series in Engineering, vol. 3),

Automatic Calibration and Performance Evaluation of a Water Quality Model for a River Greatly Influenced by Wastewater Treatment Plant Effluent