A Mechanism-Data Fusion Model for Algal Growth in Hydrologically Constrained River Segments

A Mechanism-Data Fusion Model for Algal Growth in Hydrologically Constrained River Segments | 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 A Mechanism-Data Fusion Model for Algal Growth in Hydrologically Constrained River Segments Ying Liu, Zhiwei Ren, Zelin Jing, Jihong Liu, Qingsong Chen, Yurou Wang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8517267/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract The control of algal blooms remains a significant challenge in global water environment management. Addressing the lack of hydrodynamic data and small-scale constrained river segments, this study proposes a novel framework for an algal growth dynamics model that incorporates meteorological, water quality, and hydrodynamic factors. The model parameters were optimized using a genetic algorithm for construction. Water quality in the Jiuqu River, located in the upper reaches of the Yangtze River, was monitored through field investigations. By combining Gradient Boosting Decision Tree analysis with SHapley Additive exPlanations, the key environmental factors affecting algal growth in the watershed were identified, and the model’s effectiveness was validated. This method combines mechanistic approaches with data-driven optimization, reliance on large data sets is reduced while model generalization is enhanced. It provides an early warning system for algal bloom outbreaks in the Jiuqu River Basin. The model, optimized through time-lag correction, demonstrated a 7.2% improvement in predictive performance for algal biomass compared to the non-optimized model. Furthermore, its performance significantly surpassed that of traditional data-driven models, showcasing robust adaptability and scalability. Finally, using the Copula function, a joint distribution model of key environmental factors and algal biomass was constructed. The relationships between environmental factors and algal bloom outbreaks were explored, and the environmental thresholds for algal bloom outbreaks in the Jiuqu River Basin were determined (COD Cr : 18.78 mg/L, COD Mn : 5.2 mg/L, TP: 0.09 mg/L, T: 11.48°C, Runoff: 0.08 mm). These findings provide guidance for the timely and effective prevention and control of algal bloom pollution. Algal Bloom Genetic Algorithm Time-Lag Correction Copula Function Warning Thresholds Full Text Additional Declarations No competing interests reported. Supplementary Files AppendixEMA.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 16 May, 2026 Reviewers agreed at journal 25 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviews received at journal 12 Feb, 2026 Reviewers agreed at journal 25 Jan, 2026 Reviewers invited by journal 20 Jan, 2026 Editor assigned by journal 10 Jan, 2026 Submission checks completed at journal 10 Jan, 2026 First submitted to journal 05 Jan, 2026 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. 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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-8517267","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":577712497,"identity":"8d314d31-c819-4e22-8c94-0711f22d3b08","order_by":0,"name":"Ying Liu","email":"","orcid":"","institution":"Southwest Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Liu","suffix":""},{"id":577712498,"identity":"109a1c2b-6225-442e-8533-4e25e01d5563","order_by":1,"name":"Zhiwei Ren","email":"","orcid":"","institution":"Southwest Jiaotong 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Addressing the lack of hydrodynamic data and small-scale constrained river segments, this study proposes a novel framework for an algal growth dynamics model that incorporates meteorological, water quality, and hydrodynamic factors. The model parameters were optimized using a genetic algorithm for construction. Water quality in the Jiuqu River, located in the upper reaches of the Yangtze River, was monitored through field investigations. By combining Gradient Boosting Decision Tree analysis with SHapley Additive exPlanations, the key environmental factors affecting algal growth in the watershed were identified, and the model\u0026rsquo;s effectiveness was validated. This method combines mechanistic approaches with data-driven optimization, reliance on large data sets is reduced while model generalization is enhanced. It provides an early warning system for algal bloom outbreaks in the Jiuqu River Basin. The model, optimized through time-lag correction, demonstrated a 7.2% improvement in predictive performance for algal biomass compared to the non-optimized model. Furthermore, its performance significantly surpassed that of traditional data-driven models, showcasing robust adaptability and scalability. Finally, using the Copula function, a joint distribution model of key environmental factors and algal biomass was constructed. The relationships between environmental factors and algal bloom outbreaks were explored, and the environmental thresholds for algal bloom outbreaks in the Jiuqu River Basin were determined (COD\u003csub\u003eCr\u003c/sub\u003e: 18.78 mg/L, COD\u003csub\u003eMn\u003c/sub\u003e: 5.2 mg/L, TP: 0.09 mg/L, T: 11.48\u0026deg;C, Runoff: 0.08 mm). These findings provide guidance for the timely and effective prevention and control of algal bloom pollution.\u003c/p\u003e","manuscriptTitle":"A Mechanism-Data Fusion Model for Algal Growth in Hydrologically Constrained River Segments","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-22 18:06:50","doi":"10.21203/rs.3.rs-8517267/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-17T02:25:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"165499853105621138555539155692165997586","date":"2026-04-25T22:48:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"268454401888056760865933757511688980033","date":"2026-04-24T02:08:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-12T12:52:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"155093688718113063884218692113649901344","date":"2026-01-25T19:05:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-20T18:51:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-10T07:33:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-10T07:32:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Monitoring and Assessment","date":"2026-01-05T05:20:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d5363524-aacd-4f90-ba7a-0ab5381f83c4","owner":[],"postedDate":"January 22nd, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-17T02:25:16+00:00","index":60,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-22T18:06:50+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-22 18:06:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8517267","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8517267","identity":"rs-8517267","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}