半湿润流域洪水预报实时校正方法比较

为了提高新安江模型在半湿润流域的洪水预报精度,选择K最近邻(KNN)算法、传统的误差自回归(AR)方法、反馈模拟方法3种实时校正方法,以陕西省陈河流域为试验对象进行洪水预报。以洪峰相对误差和纳什效率系数为评价指标,分析对比3种方法的校正效果。结果表明:3种校正方法均能提高预报纳什效率系数,其中反馈模拟最优,AR、KNN效果次之;反馈模拟对洪峰误差校正相比于KNN算法在短预见期内更为精确,两者均能减小AR法在洪峰误差校正上的不足;加入历史样本的KNN算法在洪峰误差校正上效果优于反馈模拟,可有效提高洪水预报精度。

Comparison of real-time correction methods of flood forecasting in semi-humid watershed

To provide more reliable simulations and forecasts using the Xinanjiang model in the semi-humid watersheds, this study introduced three real-time correction methods into the flood forecasting, respectively, including the K-nearest neighbor algorithm(the KNN method), the traditional error autoregression method(the AR method)and the simulating feedback method. The Chenhe Basin, in Shaanxi Province, was selected as the experimental basin. Considering the relative error of flood peak and the coefficient of Nash-Sutcliffe efficiency as evaluation indicators, this study analyzed the results of three correction methods. The results show that all three kinds of correction methods can improve the coefficient of Nash-Sutcliffe efficiency and the simulating feedback method was optimal on the Nash-Sutcliffe efficiency coefficient, while the AR method and the KNN method were the second best. The simulating feedback method allow a remarkable improvement compared with the KNN method in a short forecast period, and both of them can effectively avoid the defect of the AR method in terms of the error correction of flood peak. The results also indicate the KNN method with historical samples yielded better results than the simulation of feedback method on the error correction of flood peak, which can effectively improve the accuracy of flood forecasting.