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首页> 外文会议>International Conference on Water and Environment(WE-2003); 20031215-18; Bhopal(IN) >A New Approach To Multi-Criteria Calibration Of A Rainfall-Runoff Model
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A New Approach To Multi-Criteria Calibration Of A Rainfall-Runoff Model

机译：降雨径流模型多准则标定的新方法

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摘要

Over the past three decades, a lot of research has been carried out to develop faster and more sophisticated techniques for automatic calibration of hydrologic models. Calibration of models, in general, refers to the process of estimating the values of the model parameters so that t7he model results closely resemble the observed historical data. More recently, automatic calibration routines with multiple objective capabilities are increasing popular. However, as more objective functions are included in the calibration, the number of possible Pareto optimal solutions as well as the number of 'near' optimal parameter sets increases. The calibration problem quickly becomes a decision-making problem. In the practical sense, users of automatic calibration routines have to face the task of selecting a set of suitable model parameters from the numerous possible Pareto optimal sets. The authors propose a new method of automatic calibration, which combines an effective optimisation routine, based on multi-objective genetic algorithms, and Pareto selection. The proposed method is used to calibrate the NAM rainfall-runoff model for a Danish catchment. The results indicated that the proposed method was able to sieve through the numerous Pareto optimal solutions and shortlist a small number of optimal solutions for further investigation, if necessary.
机译：在过去的三十年中，进行了许多研究以开发更快，更复杂的技术来自动校准水文模型。通常，模型校准是指估计模型参数值的过程，以使模型结果与观察到的历史数据非常相似。最近，具有多个目标功能的自动校准例程越来越受欢迎。但是，随着校准中包含更多目标函数，可能的帕累托最优解的数量以及“接近”的最优参数集的数量都会增加。校准问题很快成为决策问题。从实际意义上讲，自动校准例程的用户必须面对从众多可能的帕累托最优集合中选择一组合适的模型参数的任务。作者提出了一种新的自动校准方法，该方法将基于多目标遗传算法的有效优化例程和帕累托选择相结合。所提出的方法用于校准丹麦流域的NAM降雨径流模型。结果表明，所提出的方法能够筛选出众多的帕累托最优解，并在必要时筛选出少数最优解以供进一步研究。

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