Article(id=1223196808982155880, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223196803663778281, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2023.20222020, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1664294400000, receivedDateStr=2022-09-28, revisedDate=1666540800000, revisedDateStr=2022-10-24, acceptedDate=null, acceptedDateStr=null, onlineDate=1769562445159, onlineDateStr=2026-01-28, pubDate=1692892800000, pubDateStr=2023-08-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769562445159, onlineIssueDateStr=2026-01-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769562445159, creator=13701087609, updateTime=1769562445159, updator=13701087609, issue=Issue{id=1223196803663778281, tenantId=1146029695717560320, journalId=1205116964453384197, year='2023', volume='41', issue='8', pageStart='1', pageEnd='222', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769562443888, creator=13701087609, updateTime=1769563793740, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1223202465391166440, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223196803663778281, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1223202465391166441, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223196803663778281, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=73, endPage=76, ext={EN=ArticleExt(id=1223196809871348397, articleId=1223196808982155880, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=CNN-EA-ConvLSTM Water Quality Prediction Model Based on Evolutionary Algorithm Optimization, columnId=1222925279849341011, journalTitle=Water Resources and Power, columnName=HYDROLOGY, WATER RESOURCES AND ENVIRONMENT, runingTitle=null, highlight=null, articleAbstract=

Aiming at the problem that the traditional water quality prediction method is difficult to capture the spatial and temporal characteristics of the sample, this paper proposes to establish a CNN-EA-ConvLSTM based water quality prediction model. The convolutional neural network (CNN) was used to reduce the dimensionality of the data and extract the sample features. Then the hidden information among samples was explored by the external attention mechanism. The convolutional long and short-term memory network (ConvLSTM) was further used to capture the spatial characteristics of the data. To achieve optimal results of the model, a genetic algorithm was used to optimize the parameters of the model. The water quality test data of Qinghai Province was used as a sample to simulate and validate the model. The results show that the mean absolute error (M_MAE) of the model is 0.063, the root mean square error (R_RMSE) is 0.012, and the mean absolute percentage error is 2.6%, which are respectively reduced by 18% and 24%, 14% and 25%, 16% and 21% compared with the CNN-EA model and CNN-LSTM model. Therefore, the model can effectively obtain the spatial and temporal characteristics of water quality, attenuate the influence of different samples, and achieve the ideal prediction effect.

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针对传统水质预测方法难以捕捉样本中时空特征的问题，提出建立基于CNN-EA-ConvLSTM水质预测模型，即首先通过卷积神经网络（CNN）对数据降维处理，提取样本特征，然后使用外部注意力机制探索样本间的隐藏信息，以卷积长短期记忆网络（ConvLSTM）进一步捕捉数据的空间特性，为使模型能达到最优效果，使用遗传算法优化模型中的超参数，最后以青海省的水质监测数据为样本对模型进行仿真验证。结果表明，该模型的平均绝对误差（M_MAE）为0.063、均方根误差（R_RMSE）为0.012、平均绝对百分比误差（M_MAPE）为2.6%，与CNN-EA模型、CNN-LSTM模型相比M_MAE、R_RMSE、M_MAPE分别降低了18%和24%、14%和25%、16%和21%，模型可有效获取水质的时空特征，减弱不同样本间的影响，达到理想预测效果。

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王红晨（1995-），女，硕士研究生，研究方向为深度学习、电子信息技术，E-mail：chen-xiaojiu@qq.com

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王红晨（1995-），女，硕士研究生，研究方向为深度学习、电子信息技术，E-mail：chen-xiaojiu@qq.com

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