Multi-parameter watershed water quality level prediction based on integrated algorithms

Multi-parameter watershed water quality level prediction based on integrated algorithms | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Multi-parameter watershed water quality level prediction based on integrated algorithms GAO MAN, Qian Yun, Zhang Qilin, Liu Yuyong, Zhang Zhuoshi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6973221/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract In the context of global water scarcity and increasing water pollution, accurate water quality assessment and prediction is crucial for water resources management and pro-tection. To address the shortcomings of traditional water quality assessment and pre-diction methods, this study constructed a multi-parameter watershed water quality level prediction model based on an integrated algorithm, which utilized principal component analysis (PCA), C4.5 decision tree, BP neural network, convolutional neural network (CNN), and long-short-term memory network (LSTM) to predict water quality classification. A total of 31296 samples were collected from 23 monitoring stations in a region of China from May to October 2023, covering nine key water quality indicators. After PCA dimensionality reduction, the data were input into the prediction models. The results show that the PCA-C4.5 decision tree model has a classification prediction accuracy of 88.13%; the PCA-BP neural network model has an overall accuracy of 94.53%, with excellent precision, recall and F1 value in categories 3 and 4; the PCA-CNN model has an accuracy of 93.65%, with high precision in categories 1 and 6; and the PCA-LSTM model is the best model, with an accuracy of 94.87%. The PCA-LSTM model has the best performance with an accuracy of 94.87%, and its recognition ability is outstanding as its precision and recall are over 94% for categories 3 and 4. This study confirms the feasi-bility of integrating algorithms for water quality prediction and provides a new path for dynamic monitoring of water quality in watersheds. In the future, we can incorporate transfer learning or attention mechanism to optimize the recognition ability of the model for small-sample categories, and explore the synergy between multi-source re-mote sensing and ground monitoring data to improve the generalization and timeliness of the model. Earth and environmental sciences/Environmental social sciences Earth and environmental sciences/Hydrology Full Text Additional Declarations No competing interests reported. Supplementary Files yzgivezhnormalized.xlsx Cite Share Download PDF Status: Published Journal Publication published 14 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 04 Aug, 2025 Reviews received at journal 27 Jul, 2025 Reviews received at journal 25 Jul, 2025 Reviewers agreed at journal 18 Jul, 2025 Reviews received at journal 17 Jul, 2025 Reviewers agreed at journal 17 Jul, 2025 Reviewers agreed at journal 17 Jul, 2025 Reviewers invited by journal 17 Jul, 2025 Editor assigned by journal 15 Jul, 2025 Editor invited by journal 15 Jul, 2025 Submission checks completed at journal 09 Jul, 2025 First submitted to journal 09 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6973221","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":487278444,"identity":"53fadb9f-3b39-4813-b022-0c8cce35b4d6","order_by":0,"name":"GAO MAN","email":"","orcid":"","institution":"Beihua University","correspondingAuthor":false,"prefix":"","firstName":"GAO","middleName":"","lastName":"MAN","suffix":""},{"id":487278445,"identity":"12eb6add-50a1-468b-b19c-ac9b48a63a96","order_by":1,"name":"Qian 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To address the shortcomings of traditional water quality assessment and pre-diction methods, this study constructed a multi-parameter watershed water quality level prediction model based on an integrated algorithm, which utilized principal component analysis (PCA), C4.5 decision tree, BP neural network, convolutional neural network (CNN), and long-short-term memory network (LSTM) to predict water quality classification. A total of 31296 samples were collected from 23 monitoring stations in a region of China from May to October 2023, covering nine key water quality indicators. After PCA dimensionality reduction, the data were input into the prediction models. The results show that the PCA-C4.5 decision tree model has a classification prediction accuracy of 88.13\\%; the PCA-BP neural network model has an overall accuracy of 94.53\\%, with excellent precision, recall and F1 value in categories 3 and 4; the PCA-CNN model has an accuracy of 93.65\\%, with high precision in categories 1 and 6; and the PCA-LSTM model is the best model, with an accuracy of 94.87\\%. The PCA-LSTM model has the best performance with an accuracy of 94.87\\%, and its recognition ability is outstanding as its precision and recall are over 94\\% for categories 3 and 4. This study confirms the feasi-bility of integrating algorithms for water quality prediction and provides a new path for dynamic monitoring of water quality in watersheds. In the future, we can incorporate transfer learning or attention mechanism to optimize the recognition ability of the model for small-sample categories, and explore the synergy between multi-source re-mote sensing and ground monitoring data to improve the generalization and timeliness of the model.","manuscriptTitle":"Multi-parameter watershed water quality level prediction based on integrated algorithms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-21 09:50:13","doi":"10.21203/rs.3.rs-6973221/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-04T07:45:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-27T09:01:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-25T12:32:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"187461374248676530903545190115344153515","date":"2025-07-18T07:30:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-17T13:45:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"34324271651794355978844155873098830234","date":"2025-07-17T11:59:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"171122754315160926983207675210716704716","date":"2025-07-17T04:43:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-17T04:07:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-15T10:41:48+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-15T10:29:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-09T13:50:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-07-09T13:21:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"337cf1ad-68da-4856-a3d4-e4d60e4e62ed","owner":[],"postedDate":"July 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":51732523,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":51732524,"name":"Earth and environmental sciences/Hydrology"}],"tags":[],"updatedAt":"2025-11-17T16:05:25+00:00","versionOfRecord":{"articleIdentity":"rs-6973221","link":"https://doi.org/10.1038/s41598-025-23775-5","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-11-14 15:56:58","publishedOnDateReadable":"November 14th, 2025"},"versionCreatedAt":"2025-07-21 09:50:13","video":"","vorDoi":"10.1038/s41598-025-23775-5","vorDoiUrl":"https://doi.org/10.1038/s41598-025-23775-5","workflowStages":[]},"version":"v1","identity":"rs-6973221","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6973221","identity":"rs-6973221","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}