Wavelet analysis-based projection pursuit autoregression model and its application in the runoff forecasting of Li Xiangjiang basin

作     者：Jiang, Zhiqiang Li, Rongbo Ji, Changming Li, Anqiang Zhou, Jianzhong 

作者机构：Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan Hubei Peoples R China Changjiang Inst Survey Planning Design & Res Wuhan Peoples R China North China Elect Power Univ Coll Renewable Energy Beijing Peoples R China 

出 版 物：《HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES》 (水文科学杂志)

年 卷 期：2018年第63卷第12期

页      面：1817-1830页

核心收录：

学科分类：08[工学] 081501[工学-水文学及水资源] 0815[工学-水利工程] 

基　　金：National Key R&D Program of China [2016YFC0402209] Natural Science Foundation of China [51809098, 91647114, 91547208] Fundamental Research Funds for the Central Universities [HUST: 2017KFYXJJ 198, HUST: 2016YXZD047] 

主　　题：runoff forecasting wavelet analysis projection pursuit auto regression model Li Xiangjiang basin Ya Yangshan Reservoir 

摘      要：The wavelet analysis technique was combined in this study with the projection pursuit autoregression (PPAR) model, and a new mid- and long-term runoff forecasting model, the wavelet analysis-based PPAR (PPAR-WA) is proposed, which realizes runoff forecasting from the perspective of the internal mechanism of a sequence. The runoff forecasting of the leading hydropower station in the Li Xianjiang cascade reservoirs in China was carried out to test the performance of the proposed model, and the accuracy and stability of the forecasting results were evaluated and analysed. The results show that the average relative error of the forecasting period can reach 9.6%, and the best relative error is less than 5% in some years. In addition, compared with PPAR, a back-propagation neural network and autoregression moving average model through three evaluation indexes, the results of PPAR-WA have higher accuracy and stronger stability. So, it has a certain value of popularization and application.