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In order to successfully calibrate an urban drainage model, multiple criteria should be considered, which raises the issue of adopting a method for comparing different parameter sets according to a set of objectives. Multi-objective genetic algorithms (MOGA) have proved effective in numerous such applications, where most of the techniques relying on the condition of Pareto efficiency to compare different solutions. However, as the number of criteria increases, the ratio of Pareto optimal to feasible solutions increases as well, worsening the efficiency of the genetic algorithm search. In this paper, firstly the drawbacks of single objective calibration approach are highlighted. Then, a new MOGA, the preference ordering genetic algorithm, is proposed, that alleviates the drawbacks of conventional Pareto-based methods. The efficacy of this algorithm is demonstrated on the calibration of a physically-based, distributed sewer network model, and the comparison is made with a known MOGA NSGA-II. The results are very encouraging because the obtained parameter sets closely resembled both calibration and validation events. The identifiability of 10 model parameters were analysed, showing significantly smaller ranges of optimal values for parameters related to impervious areas compared to those related to pervious areas, which is reasonable considering relatively low rainfall intensities. In addition to standard ways of presenting calibration results, “radar” plots were also used to present information on trade-off for eight objective functions for four rainfall-runoff events. |
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