SWMM Calibration using Genetic Algorithms
The Storm Water Management Model (SWMM) is 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 hydrology module RUNOFF is the best candidate module for uncertainty reduction by parameter optimization.
In this chapter we describe how the genetic algorithm (GA) method was developed to optimize SWMM RUNOFF parameters. The GA calibration method and its accuracy, efficiency, robustness and reliability are demonstrated. The basic principle of the GA is the same principle that controls the genetic reproduction process with crossover and mutation as the major operations. By applying the genetic algorithm to SWMM with the aid of the sensitivity wizard in the graphical decision support system PCSWMM, a sensitivity-based method for automating the calibration of runoff model was developed.
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.
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