Construction of a Stage-aware Water Quality Prediction Model Driven by the Temporal Evolution of Key Factors

Construction of a Stage-aware Water Quality Prediction Model Driven by the Temporal Evolution of Key Factors | 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 Construction of a Stage-aware Water Quality Prediction Model Driven by the Temporal Evolution of Key Factors Ying Liu, Zelin Jing, Zhengjiang Lin, Yurou Wang, Qingsong Chen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7298061/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Conventional unified modeling strategies often overlook the stage-specific variability in pollution source contributions, which may lead to biased identification of key factors and diminished predictive performance. To address this limitation, this study develops a stage-awaremodeling strategy based on stage-specific response mechanisms. Using the Daluxi River, a primary tributary of the upper Yangtze River, as a case study, the year was divided into two distinct periods based on combined Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA): the low-temperature nutrient-retention period (November to February) and the multi-source disturbance period (March to October). Feature selection for each stage was conducted through a combination of Hierarchical Clustering of Pearson Correlation coefficients and Support Vector Regression with Recursive Feature Elimination (SVR-RFE), followed by model construction with Long Short-Term Memory (LSTM) networks. Furthermore, Generalized Additive Models (GAM) combined with SHAP (SHapley Additive exPlanations) were employed to elucidate the nonlinear response mechanisms of variables across stages. The results show that the R² values of COD Mn , TN, and TP reached 0.962/0.928, 0.951/0.996, and 0.713/0.906 in different stages, with notable reductions in MSE and MAPE, confirming the superiority of stage-specific over full-period modeling. For AN, although R² was relatively low (0.202/0.826), it exceeded the full-period result (0.518), with near-zero MSE and low MAPE (8.35%/3.71%). This strategy aligns with the stage-specific characteristics of pollution evolution, enhancing the scientific rigor and interpretability of the model, and provides a new approach for transitioning water environment management from "static averaging" to "dynamic identification and period-specific control." Stage-aware modeling Water quality prediction SVR-RFE LSTM SHAP-GAM Full Text Supplementary Files Highlight.docx OnlineResource1.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 05 Sep, 2025 Reviewers invited by journal 05 Sep, 2025 Editor assigned by journal 06 Aug, 2025 First submitted to journal 05 Aug, 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. 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-7298061","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":510663008,"identity":"980896b9-209f-4a60-8252-e0eda23a35b3","order_by":0,"name":"Ying Liu","email":"","orcid":"","institution":"Southwest Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Liu","suffix":""},{"id":510663009,"identity":"3dfd688b-b18e-46a7-82d0-c7f74bae3b13","order_by":1,"name":"Zelin Jing","email":"","orcid":"","institution":"Southwest Jiaotong 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To address this limitation, this study develops a stage-awaremodeling strategy based on stage-specific response mechanisms. Using the Daluxi River, a primary tributary of the upper Yangtze River, as a case study, the year was divided into two distinct periods based on combined Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA): the low-temperature nutrient-retention period (November to February) and the multi-source disturbance period (March to October). Feature selection for each stage was conducted through a combination of Hierarchical Clustering of Pearson Correlation coefficients and Support Vector Regression with Recursive Feature Elimination (SVR-RFE), followed by model construction with Long Short-Term Memory (LSTM) networks. Furthermore, Generalized Additive Models (GAM) combined with SHAP (SHapley Additive exPlanations) were employed to elucidate the nonlinear response mechanisms of variables across stages. The results show that the R\u0026sup2; values of COD\u003csub\u003eMn\u003c/sub\u003e, TN, and TP reached 0.962/0.928, 0.951/0.996, and 0.713/0.906 in different stages, with notable reductions in MSE and MAPE, confirming the superiority of stage-specific over full-period modeling. For AN, although R\u0026sup2; was relatively low (0.202/0.826), it exceeded the full-period result (0.518), with near-zero MSE and low MAPE (8.35%/3.71%). This strategy aligns with the stage-specific characteristics of pollution evolution, enhancing the scientific rigor and interpretability of the model, and provides a new approach for transitioning water environment management from \"static averaging\" to \"dynamic identification and period-specific control.\"\u003c/p\u003e","manuscriptTitle":"Construction of a Stage-aware Water Quality Prediction Model Driven by the Temporal Evolution of Key Factors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-11 17:47:05","doi":"10.21203/rs.3.rs-7298061/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-09-05T09:31:19+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-05T08:47:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-06T11:43:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"Water Resources Management","date":"2025-08-05T04:22:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"water-resources-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"warm","sideBox":"Learn more about [Water Resources Management](https://www.springer.com/journal/11269)","snPcode":"11269","submissionUrl":"https://submission.nature.com/new-submission/11269/3","title":"Water Resources Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e3e6d9e8-5f24-4f14-a451-5129217e8979","owner":[],"postedDate":"September 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-11T17:47:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-11 17:47:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7298061","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7298061","identity":"rs-7298061","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}