Prediction of Eutrophic Water Quality in the Daluxi River Based on a Multi-Scale Feature Extraction and Hybrid Screening Strategy

Prediction of Eutrophic Water Quality in the Daluxi River Based on a Multi-Scale Feature Extraction and Hybrid Screening Strategy | 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 Research Article Prediction of Eutrophic Water Quality in the Daluxi River Based on a Multi-Scale Feature Extraction and Hybrid Screening Strategy Shuhan Yang, Ying Liu, Qingsong Chen, Zhiwei Ren, Zelin Jing, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6662259/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract As water environmental issues in watersheds shift from traditional pollution to eutrophication and degradation of aquatic ecosystems, accurately predicting water quality trends has become crucial for eutrophication control. This study focuses on the Daluxi sub-watershed, a primary tributary of the Yangtze River, to develop a model for predicting eutrophication trends. Using wavelet analysis and Pearson correlation analysis, we explored the interactions between meteorological and water quality factors, as well as the influence of upstream and downstream water quality factors, ensuring the explanatory power of the input variables and laying a foundation for the model's predictive accuracy and robustness. Through a comparative analysis of various machine learning models, Gradient Boosting Decision Tree (GBDT) was ultimately selected as the optimal predictive model. Feature importance and Partial Dependence Plot (PDP) analyses were employed to quantify the contribution of each influencing factor to the prediction results. The findings show that the model performs well in predicting downstream water quality indices such as TN, COD Mn , AN, and TP for the following day, with R² values of 0.90, 0.89, 0.88, and 0.89, respectively; Mean Absolute Error (MAE) values of 0.14, 0.27, 0.03, and 0.01, respectively; Root Mean Squared Error (RMSE) values of 0.22, 0.37, 0.06, and 0.01, respectively, demonstrating excellent predictive performance. This study enables early warnings of downstream water quality based on upstream water quality, facilitating the timely identification of potential water quality issues and providing a scientific basis for timely control measures, thereby effectively supporting water resource management and eutrophication control efforts. Wavelet Analysis Feature Recognition Machine Learning Water Quality Prediction Eutrophic Water Bodies Full Text Additional Declarations No competing interests reported. Supplementary Files attachmenttomanuscript.docx Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6662259","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":476267126,"identity":"aad0b993-92c7-43fb-bcd7-daa6784bb727","order_by":0,"name":"Shuhan Yang","email":"","orcid":"","institution":"Southwest Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Shuhan","middleName":"","lastName":"Yang","suffix":""},{"id":476267128,"identity":"e08287bc-2d71-4b4a-98f9-00bfd1356479","order_by":1,"name":"Ying Liu","email":"","orcid":"","institution":"Southwest Jiaotong 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This study focuses on the Daluxi sub-watershed, a primary tributary of the Yangtze River, to develop a model for predicting eutrophication trends. Using wavelet analysis and Pearson correlation analysis, we explored the interactions between meteorological and water quality factors, as well as the influence of upstream and downstream water quality factors, ensuring the explanatory power of the input variables and laying a foundation for the model's predictive accuracy and robustness. Through a comparative analysis of various machine learning models, Gradient Boosting Decision Tree (GBDT) was ultimately selected as the optimal predictive model. Feature importance and Partial Dependence Plot (PDP) analyses were employed to quantify the contribution of each influencing factor to the prediction results. The findings show that the model performs well in predicting downstream water quality indices such as TN, COD\u003csub\u003eMn\u003c/sub\u003e, AN, and TP for the following day, with R² values of 0.90, 0.89, 0.88, and 0.89, respectively; Mean Absolute Error (MAE) values of 0.14, 0.27, 0.03, and 0.01, respectively; Root Mean Squared Error (RMSE) values of 0.22, 0.37, 0.06, and 0.01, respectively, demonstrating excellent predictive performance. 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