Article(id=1222963542173667959, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1222963536863678929, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2023.20221932, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1663344000000, receivedDateStr=2022-09-17, revisedDate=1666108800000, revisedDateStr=2022-10-19, acceptedDate=null, acceptedDateStr=null, onlineDate=1769506830018, onlineDateStr=2026-01-27, pubDate=1682352000000, pubDateStr=2023-04-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769506830018, onlineIssueDateStr=2026-01-27, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769506830018, creator=13701087609, updateTime=1769506830018, updator=13701087609, issue=Issue{id=1222963536863678929, tenantId=1146029695717560320, journalId=1205116964453384197, year='2023', volume='41', issue='4', pageStart='1', pageEnd='220', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769506828752, creator=13701087609, updateTime=1769508076664, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1222968771057279321, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1222963536863678929, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1222968771057279322, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1222963536863678929, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=39, endPage=43, ext={EN=ArticleExt(id=1222963542416937606, articleId=1222963542173667959, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Monthly Runoff Prediction Model and Its Application Based on GPR with Physically Composite Kernel, columnId=1222925279849341011, journalTitle=Water Resources and Power, columnName=HYDROLOGY, WATER RESOURCES AND ENVIRONMENT, runingTitle=null, highlight=null, articleAbstract=

In light of the difficulty of traditional single runoff prediction models to describe future variation in runoff, a monthly runoff prediction model named AVMD-GPR-CK based on adaptive variational modal decomposition (AVMD) and Gaussian process regression (GPR-CK) with physically composite kernel was proposed. In the proposed model, the runoff series was decomposed into several subseries using AVMD. Then subseries were separately modeled according to their own characteristics, and the final prediction result was the superposition of the subsequence prediction results. The AVMD-GPR-CK was applied to forecast the future 1-12 months runoff at Xiangjiaba station in the Jinsha River basin. The results show that the deterministic coefficient of the AVMD-GPR-CK model is greater than 0.94, and the mean absolute percentage error (M_MAPE) is within ±17% for all leading times, and the M_MAPE is inside ±10% for leading times within 10 months. Furthermore, the accuracy of the AVMD-GPR-CK is significantly better than those of the commonly used BP, GRNN, RBF, and RELM models.

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鉴于传统的单一径流预报模型很难描述径流未来变化规律，将自适应变分模态分解（AVMD）与基于组合物理核函数的高斯过程回归（GPR-CK）相结合，构建了AVMD-GPR-CK预报模型，该模型采用AVMD将实测径流分解为多个子序列，对子序列依据其自身特点分别建模，子序列预报结果叠加重构即为最终预报结果。模型应用于金沙江流域向家坝站未来1~12个月的径流预报的结果表明，所有预见期AVMD-GPR-CK模型的确定性系数均大于0.94，平均绝对百分比误差（M_MAPE）在±17%以内，预见期在10个月以内时，M_MAPE在±10%以内；预报精度明显优于常见的BP、GRNN、RBF、RELM模型。

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孙娜（1992-），女，讲师、硕导，研究方向为水文水资源、人工智能应用，E-mail：sunna1347@126.com

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孙娜（1992-），女，讲师、硕导，研究方向为水文水资源、人工智能应用，E-mail：sunna1347@126.com

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