文献类型：期刊文献

英文题名：Runoff prediction using hydro-meteorological variables and a new hybrid ANFIS-GPR model

作者：Liu, Zhennan Zhou, Jingnan Zeng, Xianzhong Wang, Xiaoyu Jiao, Weiguo Xu, Min Wu, Anjie

第一作者：刘振男

通信作者：Zhou, JN[1]

机构：[1]Guizhou Inst Technol, Sch Civil Engn, Guiyang 550003, Peoples R China;[2]Guizhou Inst Technol, Sch Sci, Guiyang 550003, Peoples R China;[3]Songbaishan Reservoir Management Off, Guiyang 550025, Peoples R China;[4]Anhui Agr Univ, Sch Engn, Hefei 230036, Peoples R China

第一机构：贵州理工学院土木工程学院

通信机构：corresponding author), Guizhou Inst Technol, Sch Sci, Guiyang 550003, Peoples R China.|贵州理工学院理学院;贵州理工学院;

年份：0

外文期刊名：JOURNAL OF WATER AND CLIMATE CHANGE

收录：;Scopus(收录号：2-s2.0-85163080921);WOS:【SCI-EXPANDED(收录号:WOS:000970548900001)】；

基金：This work is supported by the National Natural Science Foundation of China (No. 52069005 and 62163008) and Guizhou Provincial Science and Technology Projects (Guizhou Science Foundation-ZK[2021] General 295 and [2020]1Y248), and Science and Technology Special Funds of Guizhou Water Resources Department (KT202232), and Startup Project for High-level Talents of Guizhou Institute of Technology (XJGC20210425) and special thanks are given to the anonymous reviewers and editors for their constructive comments.

语种：英文

外文关键词：adaptive neuro-fuzzy inference system; correlation analysis; Gaussian regression process; principal component analysis; runoff prediction

摘要：Precise and credible runoff forecasting is extraordinarily vital for various activities of water resources deployment and implementation. The neoteric contribution of the current article is to develop a hybrid model (ANFIS-GPR) based on adaptive neuro-fuzzy inference system (ANFIS) and Gaussian process regression (GPR) for monthly runoff forecasting in the Beiru river of China, and the optimal input schemes of the models are discussed in detail. Firstly, variables related to runoff are selected from the precipitation, soil moisture content, and evaporation as the first set of input schemes according to correlation analysis (CA). Secondly, principal component analysis (PCA) is used to eliminate the redundant information between the original input variables for forming the second set of input schemes. Finally, the runoff is predicted based on different input schemes and different models, and the prediction performance is compared comprehensively. The results show that the input schemes jointly established by CA and PCA (CA-PCA) can greatly improve the prediction accuracy. ANFIS-GPR displays the best forecasting performance among all the peer models. In the single models, the performance of GPR is better than that of ANFIS.