趙和松1 ，王圓圓2 ，趙 齊3

(1. 水利部信息中心，北京 100053;2. 北京金水信息技術發展有限公司，北京 100053;3. 河海大學計算機與信息學院，江蘇 南京 211100)

　　摘 要：為提高用水總量預測的準確性，針對現有方法中存在的非線性多維用水因子選取不合理的問題，提出一種融合KPCA(核主成分分析)和BP神經網絡的用水總量預測方法。使用皮爾遜相關系數對用水因子進行相關性分析，選擇與用水總量最相關的多個因子作為數據輸入，利用KPCA對預測因子進行降維處理，解決數據間的非線性特征，再采用BP神經網絡建立用水總量預測模型，網絡的權值和閾值采用思維進化學習算法進行調優。采用國家統計局2007—2016年度開放統計用水數據進行實驗，結果表明：融合KPCA和BP的用水總量預測方法相對預測誤差小于5%，預測用水精度有明顯提升，可以較好預測未來用水總量，為高維非線性數據的預測提供新的優化思路。

　　關鍵詞：用水總量預測;KPCA;BP神經網絡;思維優化算法;融合方法

　　Water consumption prediction approach using KPCA and BP neural network

　　ZHAO Hesong1, WANG Yuanyuan2, ZHAO Qi3

　　(1. Information Center, Ministry of Water Resources, Beijing 100053, China;

　　2. Beijing Jinshui Information Technology Development Co., Ltd., Beijing 100053, China;

　　3. College of computer and information, Hohai University, Nanjing 211100, China)

　　Abstract： In order to improve the accuracy of total water consumption prediction, aiming at the unreasonable selection of nonlinear multi-dimensional water consumption factors in existing methods, a total water consumption prediction method based on KPCA (kernel principal component analysis) and BP neural network was proposed. Pearson correlation coefficient is used to analyze the correlation of water consumption factors. The most relevant factors of total water consumption are selected as data input. KPCA is used to reduce the dimension of prediction factors for solving the nonlinear characteristics of data. BP neural network is used to establish the prediction model of total water consumption. The weights and thresholds of the network are optimized by mind evolutionary learning algorithm. Using the water consumption data from the State Statistics Bureau between 2007 and 2016 for experiment, of which the results show　that relative prediction error of the method based on KPCA and BP is less than 5%, and the prediction accuracy is significantly improved, which can better predict the future total water consumption, and provide a new optimization idea for the prediction of high-dimensional nonlinear data.

　　Key words：Total water consumption prediction; KPCA; BP neural network; mind evolutionary algorithm; fusion approach