基于参数优化的集对分析月径流预测

Monthly Runoff Prediction Based on Set Pair Analysis with Parameters Optimization

采用集对分析法进行月径流预报时,针对级别划分较多时不易确定差异度分量系数的问题,建立了基于SCEM-UA算法优化该系数的月径流预报方法。研究实例表明,本方法能够有效区分集对间不同差异度的影响,优化所得的差异度分量系数是有效的、合理的,能够提高月径流预报精度并发布概率预报。此外,分析表明集对分析预报中的参数不确定性在模型不确定性中占主导地位。

When predicting monthly runoff with set pair analysis （SPA） method, it will become much difficult to determine the dif- ference degree coefficients as the number of runoff levels increased. To solve this problem, the SCEM-UA algorithm was employed for optimizing the difference degree coefficients of SPA. This method was applied to forecast the monthly runoff. The results show that the proposed method can effectively distinguish the effects of difference degree among set pairs, and the difference degree co- efficient optimized by the method is effective and reasonable, the runoff prediction accuracy is improved greatly. Also, the parame- ter uncertainty occupies the largest proportion in the model uncertainty.