[基于不同人工神经网络的水质预测方法比较研究]。

Q2 Environmental Science
Ming-Jun Xiao, Yi-Chun Zhu, Wen-Yuan Gao, Yu Zeng, Hao Li, Shuo-Fu Chen, Ping Liu, Hong-Li Huang
{"title":"[基于不同人工神经网络的水质预测方法比较研究]。","authors":"Ming-Jun Xiao, Yi-Chun Zhu, Wen-Yuan Gao, Yu Zeng, Hao Li, Shuo-Fu Chen, Ping Liu, Hong-Li Huang","doi":"10.13227/j.hjkx.202310074","DOIUrl":null,"url":null,"abstract":"<p><p>The prediction of future data using existing data is an effective tool for regional planning and watershed management. The back propagation neural network (BPNN) and convolutional neural network (CNN) were used to construct a prediction model based on the water quality index of Hengyang in Xiangjiang River Basin from April to May 2022 and the results of permanganate index prediction by different models were compared. The prediction results displayed by BPNN could predict the water quality; however, overfitting occurred during the prediction. BPNN modified by particle swarm optimization (PSO) could avoid overfitting, which improved the parameter selection method of the BPNN mode. The CNN model had a better prediction effect, which had a more complex structure and a more scientific fitting method to avoid the model falling into the local extreme value during the fitting process and improve the accuracy of the model prediction results. The evaluation parameters including root-mean-square error (RMSE), coefficient of determination (<i>R</i><sup>2</sup>), and mean absolute error (MAE) were used to predict the accuracy of the network. Compared with that of the traditional BPNN model, PSO-BPNN reduced the RESM of the test set from 0.278 2 mg·L<sup>-1</sup> to 0.210 9 mg·L<sup>-1</sup>, reduced the MAE of the test set from 0.222 3 mg·L<sup>-1</sup> to 0.153 7 mg·L<sup>-1</sup> and increased the <i>R</i><sup>2</sup> of the test set from 0.864 0 to 0.921 8, which indicated that PSO-BPNN had more stable fitting ability. RMSE, MAE, and <i>R</i><sup>2</sup> of the test set in the CNN model were 0.122 0 mg·L<sup>-1</sup>, 0.092 7 mg·L<sup>-1</sup>, and 0.970 5, respectively, which showed that CNN had a better fitting and prediction effect than that of BPNN.</p>","PeriodicalId":35937,"journal":{"name":"Huanjing Kexue/Environmental Science","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"[Comparative Study of Water Quality Prediction Methods Based on Different Artificial Neural Network].\",\"authors\":\"Ming-Jun Xiao, Yi-Chun Zhu, Wen-Yuan Gao, Yu Zeng, Hao Li, Shuo-Fu Chen, Ping Liu, Hong-Li Huang\",\"doi\":\"10.13227/j.hjkx.202310074\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The prediction of future data using existing data is an effective tool for regional planning and watershed management. The back propagation neural network (BPNN) and convolutional neural network (CNN) were used to construct a prediction model based on the water quality index of Hengyang in Xiangjiang River Basin from April to May 2022 and the results of permanganate index prediction by different models were compared. The prediction results displayed by BPNN could predict the water quality; however, overfitting occurred during the prediction. BPNN modified by particle swarm optimization (PSO) could avoid overfitting, which improved the parameter selection method of the BPNN mode. The CNN model had a better prediction effect, which had a more complex structure and a more scientific fitting method to avoid the model falling into the local extreme value during the fitting process and improve the accuracy of the model prediction results. The evaluation parameters including root-mean-square error (RMSE), coefficient of determination (<i>R</i><sup>2</sup>), and mean absolute error (MAE) were used to predict the accuracy of the network. Compared with that of the traditional BPNN model, PSO-BPNN reduced the RESM of the test set from 0.278 2 mg·L<sup>-1</sup> to 0.210 9 mg·L<sup>-1</sup>, reduced the MAE of the test set from 0.222 3 mg·L<sup>-1</sup> to 0.153 7 mg·L<sup>-1</sup> and increased the <i>R</i><sup>2</sup> of the test set from 0.864 0 to 0.921 8, which indicated that PSO-BPNN had more stable fitting ability. RMSE, MAE, and <i>R</i><sup>2</sup> of the test set in the CNN model were 0.122 0 mg·L<sup>-1</sup>, 0.092 7 mg·L<sup>-1</sup>, and 0.970 5, respectively, which showed that CNN had a better fitting and prediction effect than that of BPNN.</p>\",\"PeriodicalId\":35937,\"journal\":{\"name\":\"Huanjing Kexue/Environmental Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-10-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Huanjing Kexue/Environmental Science\",\"FirstCategoryId\":\"1087\",\"ListUrlMain\":\"https://doi.org/10.13227/j.hjkx.202310074\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Environmental Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Huanjing Kexue/Environmental Science","FirstCategoryId":"1087","ListUrlMain":"https://doi.org/10.13227/j.hjkx.202310074","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Environmental Science","Score":null,"Total":0}
引用次数: 0

摘要

利用现有数据预测未来数据是区域规划和流域管理的有效工具。反向传播神经网络(BPNN)和卷积神经网络(CNN)构建了基于 2022 年 4-5 月湘江流域衡阳水质指数的预测模型,并比较了不同模型的高锰酸盐指数预测结果。结果表明,BPNN 能够预测水质,但在预测过程中出现了过拟合现象。经粒子群优化(PSO)改进的 BPNN可以避免过拟合,改进了 BPNN 模式的参数选择方法。CNN 模型的预测效果更好,其结构更复杂,拟合方法更科学,避免了模型在拟合过程中陷入局部极值,提高了模型预测结果的准确性。采用均方根误差(RMSE)、判定系数(R2)和平均绝对误差(MAE)等评价参数,预测模型预测结果的准确性。来预测网络的准确性。与传统的BPNN模型相比,PSO-BPNN将测试集的RESM从0.278 2 mg-L-1降低到0.210 9 mg-L-1,将测试集的MAE从0.222 3 mg-L-1降低到0.153 7 mg-L-1,将测试集的R2从0.864 0提高到0.921 8,这表明PSO-BPNN具有更稳定的拟合能力。CNN 模型测试集的 RMSE、MAE 和 R2 分别为 0.122 0 mg-L-1、0.092 7 mg-L-1 和 0.970 5,表明 CNN 比 BPNN 具有更好的拟合和预测效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
[Comparative Study of Water Quality Prediction Methods Based on Different Artificial Neural Network].

The prediction of future data using existing data is an effective tool for regional planning and watershed management. The back propagation neural network (BPNN) and convolutional neural network (CNN) were used to construct a prediction model based on the water quality index of Hengyang in Xiangjiang River Basin from April to May 2022 and the results of permanganate index prediction by different models were compared. The prediction results displayed by BPNN could predict the water quality; however, overfitting occurred during the prediction. BPNN modified by particle swarm optimization (PSO) could avoid overfitting, which improved the parameter selection method of the BPNN mode. The CNN model had a better prediction effect, which had a more complex structure and a more scientific fitting method to avoid the model falling into the local extreme value during the fitting process and improve the accuracy of the model prediction results. The evaluation parameters including root-mean-square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE) were used to predict the accuracy of the network. Compared with that of the traditional BPNN model, PSO-BPNN reduced the RESM of the test set from 0.278 2 mg·L-1 to 0.210 9 mg·L-1, reduced the MAE of the test set from 0.222 3 mg·L-1 to 0.153 7 mg·L-1 and increased the R2 of the test set from 0.864 0 to 0.921 8, which indicated that PSO-BPNN had more stable fitting ability. RMSE, MAE, and R2 of the test set in the CNN model were 0.122 0 mg·L-1, 0.092 7 mg·L-1, and 0.970 5, respectively, which showed that CNN had a better fitting and prediction effect than that of BPNN.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Huanjing Kexue/Environmental Science
Huanjing Kexue/Environmental Science Environmental Science-Environmental Science (all)
CiteScore
4.40
自引率
0.00%
发文量
15329
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信