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In response to the problems of low accuracy and poor reliability of water consumption prediction due to the strong randomness and non-stationary state exhibited by the water consumption signal, this paper proposed a hybrid water consumption prediction model based on improved EEMD-WOA-SRU. Firstly, the LSTM prediction method was used to suppress the endpoint effect of the EEMD to obtain the improved intrinsic mode functions (IMF). Then the whale optimization algorithm (WOA) was used to optimize the simple recurrent unit (SRU) and predicted each component. Finally, the final prediction results were obtained by accumulation. The experimental results show that the decomposition error of the EEMD is reduced by 0.94% on average; Compared with the SRU, the average absolute error of EEMD-WOASRU model prediction is reduced by 45.42%, the root mean square error is reduced by 50.43%, and the reliability is improved by 52.38%. It can provide a basis for water resources decision making.

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针对用水量信号表现出的强随机性和非平稳性状态，用水量预测存在的精度低、可信度差等问题，提出了基于改进EEMD-WOA-SRU的混合用水量预测模型。首先采用长短期记忆网络（LSTM）预测法抑制集合经验模态分解（EEMD）的端点效应得到改进后的本征模态分量（IMF），然后使用鲸鱼算法（WOA）优化简单循环单元（SRU）并预测各分量，最后累加得到最终的预测结果。试验结果表明，EEMD的分解误差平均降低0.94%，相较于SRU，EEMD-WOA-SRU模型预测的平均绝对误差降低45.42%，均方根误差降低50.43%，可信度提高52.38%。研究结果可为水资源决策提供依据。

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^b.School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450046, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1223202685432742144, tenantId=1146029695717560320, journalId=1205116964453384197, articleId=1223202683494973523, authorId=1223202684975562979, language=CN, stringName=刘扬, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=^{a, b}, address=^a.华北水利水电大学黄河流域水资源高效利用省部共建协同创新中心，河南 郑州 450046
^b.华北水利水电大学信息工程学院，河南 郑州 450046, bio={"content":"

刘扬（1979-），女，博士、副教授，研究方向为深度学习、数据挖掘、水利大数据分析等，E-mail：ly_research@126.com

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刘扬（1979-），女，博士、副教授，研究方向为深度学习、数据挖掘、水利大数据分析等，E-mail：ly_research@126.com

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