基于黏菌算法的梯度增强决策树在污水处理系统中的应用

IF 4.3 4区 环境科学与生态学 Q2 ENGINEERING, ENVIRONMENTAL Water Reuse Pub Date : 2023-07-20 DOI:10.2166/wrd.2023.046
Jyoti Chauhan, R. M. Rani, V. Prashanthi, H. Almujibah, A. Alshahri, Koppula Srinivas Rao, A. Radhakrishnan
{"title":"基于黏菌算法的梯度增强决策树在污水处理系统中的应用","authors":"Jyoti Chauhan, R. M. Rani, V. Prashanthi, H. Almujibah, A. Alshahri, Koppula Srinivas Rao, A. Radhakrishnan","doi":"10.2166/wrd.2023.046","DOIUrl":null,"url":null,"abstract":"\n One way to improve the infrastructure, operations, monitoring, maintenance, and management of wastewater treatment systems is to use machine learning modelling to make smart forecasting, tracking, and failure prediction systems. This method aims to use industry data to treat the wastewater treatment model. Gradient-Boosted Decision Tree (GBDT) algorithms were used gradually to predict wastewater plant parameters. In addition, we used the Slime Mould Algorithm (SMA) for feature extraction and other acceptable tuning procedures. The input and effluent Chemical Oxygen Demand (COD) prediction for effluent treatment systems applies to the GBDT approaches employed in this study. GBDT-SMA employs artificial intelligence to provide precise method modelling for complex systems. Several training and model testing techniques were used to determine the best topology for the neural network models and decision trees. The GBDT-SMA model performed best across all methods. With 500 data, GBDT-SMA achieved an accuracy of 96.32%, outperforming other models like Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Deep Convolutional Neural Network (DCNN), and K-neighbours RF, which reached an accuracy of 82.97, 87.45, 85.98, and 91.45%, respectively.","PeriodicalId":34727,"journal":{"name":"Water Reuse","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Gradient-Boosted Decision Tree with used Slime Mould Algorithm (SMA) for wastewater treatment systems\",\"authors\":\"Jyoti Chauhan, R. M. Rani, V. Prashanthi, H. Almujibah, A. Alshahri, Koppula Srinivas Rao, A. Radhakrishnan\",\"doi\":\"10.2166/wrd.2023.046\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n One way to improve the infrastructure, operations, monitoring, maintenance, and management of wastewater treatment systems is to use machine learning modelling to make smart forecasting, tracking, and failure prediction systems. This method aims to use industry data to treat the wastewater treatment model. Gradient-Boosted Decision Tree (GBDT) algorithms were used gradually to predict wastewater plant parameters. In addition, we used the Slime Mould Algorithm (SMA) for feature extraction and other acceptable tuning procedures. The input and effluent Chemical Oxygen Demand (COD) prediction for effluent treatment systems applies to the GBDT approaches employed in this study. GBDT-SMA employs artificial intelligence to provide precise method modelling for complex systems. Several training and model testing techniques were used to determine the best topology for the neural network models and decision trees. The GBDT-SMA model performed best across all methods. With 500 data, GBDT-SMA achieved an accuracy of 96.32%, outperforming other models like Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Deep Convolutional Neural Network (DCNN), and K-neighbours RF, which reached an accuracy of 82.97, 87.45, 85.98, and 91.45%, respectively.\",\"PeriodicalId\":34727,\"journal\":{\"name\":\"Water Reuse\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2023-07-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Water Reuse\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://doi.org/10.2166/wrd.2023.046\",\"RegionNum\":4,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ENVIRONMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Water Reuse","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.2166/wrd.2023.046","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ENVIRONMENTAL","Score":null,"Total":0}
引用次数: 0

摘要

改善污水处理系统的基础设施、运营、监控、维护和管理的一种方法是使用机器学习建模来制作智能预测、跟踪和故障预测系统。该方法旨在利用工业数据对废水处理模型进行处理。逐步采用梯度增强决策树(GBDT)算法对污水厂参数进行预测。此外,我们使用黏菌算法(SMA)进行特征提取和其他可接受的调谐程序。污水处理系统的输入和流出化学需氧量(COD)预测适用于本研究中采用的GBDT方法。GBDT-SMA采用人工智能为复杂系统提供精确的方法建模。使用了几种训练和模型测试技术来确定神经网络模型和决策树的最佳拓扑。GBDT-SMA模型在所有方法中表现最好。在500个数据的情况下,GBDT-SMA的准确率达到96.32%,优于人工神经网络(ANN)、卷积神经网络(CNN)、深度卷积神经网络(DCNN)和k近邻RF等模型,后者的准确率分别为82.97、87.45、85.98和91.45%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Gradient-Boosted Decision Tree with used Slime Mould Algorithm (SMA) for wastewater treatment systems
One way to improve the infrastructure, operations, monitoring, maintenance, and management of wastewater treatment systems is to use machine learning modelling to make smart forecasting, tracking, and failure prediction systems. This method aims to use industry data to treat the wastewater treatment model. Gradient-Boosted Decision Tree (GBDT) algorithms were used gradually to predict wastewater plant parameters. In addition, we used the Slime Mould Algorithm (SMA) for feature extraction and other acceptable tuning procedures. The input and effluent Chemical Oxygen Demand (COD) prediction for effluent treatment systems applies to the GBDT approaches employed in this study. GBDT-SMA employs artificial intelligence to provide precise method modelling for complex systems. Several training and model testing techniques were used to determine the best topology for the neural network models and decision trees. The GBDT-SMA model performed best across all methods. With 500 data, GBDT-SMA achieved an accuracy of 96.32%, outperforming other models like Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Deep Convolutional Neural Network (DCNN), and K-neighbours RF, which reached an accuracy of 82.97, 87.45, 85.98, and 91.45%, respectively.
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
Water Reuse
Water Reuse Multiple-
CiteScore
6.20
自引率
8.90%
发文量
0
审稿时长
7 weeks
期刊最新文献
The use of innovative technologies to improve treated wastewater irrigation of olive trees in the Souss-Massa region, Morocco A review: The state-of-the-art of arsenic removal in wastewater Treatment of greywater using a non-aerated combined horizontal and vertical flow constructed wetland How much does reclaimed wastewater cost? A comprehensive analysis for irrigation uses in the European Mediterranean context Application of the multi-wavelength UV-LED/chlorine process to improve reverse osmosis membrane performance for reused water treatment in the steel industry
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1