基于熵权的可变模糊聚类与识别的水库洪水分类实时预报

Application of variable fuzzy set method based on entropy weight to classified real-time flood forecasting

流域洪水的形成机制具有高度的复杂性和不确定性,但又表现出自身的规律性。在洪水形成过程中流域内洪水的产、汇流过程受降雨强度、降雨中心和天气环流等诸多因素的共同影响。通过对影响因素与洪水过程的分析,找出其中的规律,为在实时洪水预报过程中充分考虑各种影响因素的作用提供可能,为此本文建立实时洪水分类预报模型,该模型利用模糊聚类方法通过分析影响因子对洪水产、汇流过程分类,并用模糊识别模型建立影响因子与产、汇流类型间的信息识别模型。本文选择东水西调工程的授水水库A作为研究对象,为调水工程在汛期做好防洪准备。实验结果表明,该方法能够准确、迅速的判断洪水类型并选择相应预报模型参数,能有效提高水库实时洪水预报精度。

This paper applies a variable fuzzy set method based on entropy weight to construction of a framework of classified real-time flood forecasting using the concepts of clustering and classification. Fuzzy clustering was used to classify historical floods based on the flood antecedent impact factors and time-varying rainfall information. In the present study, this conceptual hydrological model was calibrated for each type of flood. In application, a fuzzy diagnosis model was used to identify the types of floods in future by using the flood information obtained. Results show that the classified framework gives a fast and accurate diagnosis of the type of flood and significantly improves the accuracy of flood forecasting.