| The Storm Water Management Model (SWMM) is a world-class computer model, widely-used to evaluate, analyze and manage problems in both hydraulics and hydrology. In order to improve the reliability of the model, a parameter-optimization approach is required to determine the “best” input parameter sets. Within SWMM, the RUNOFF module is the best candidate module for uncertainty reduction by parameter optimization.; In this study, a heuristic procedure for optimization of parameters is developed that includes consideration of: sensitivity analysis, model state determination, objective function selection, and parameter optimization.; For parameter optimization, a genetic algorithm (GA) method is developed. The basic principle of the GA is the same principle that controls the genetic reproduction process with crossover and mutation are the major operations. By applying the genetic algorithm to SWMM with the aid of the sensitivity wizard in PCSWMM, a sensitivity-based GA method for automating the calibration of runoff model is developed.; Four stages were used to determine the efficiency, robustness, accuracy and reliability of the sensitivity-based GA calibration method. Overall, the average accuracy of the calibrated model was within 97% of the target dataset (TD) after approximately 58 cycles of GA calibration program, on the average.; In this research, the following main objectives were accomplished: (i) Apply the theory of GA optimization for calibrating the SWMM parameters. (ii) Demonstration of GA calibration method. (iii) Evaluation of robustness and efficiency of the GA calibration method by using the automatic program developed in this study. |
