Identification of spatially heterogeneous drivers of lake eutrophication in China with explainable automated machine learning

Identification of spatially heterogeneous drivers of lake eutrophication in China with explainable automated machine learning | 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 Identification of spatially heterogeneous drivers of lake eutrophication in China with explainable automated machine learning Wenjie Qin, Yuyi Zhang, Xianghan Zheng, Jingping Hu, Huijie Hou, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7379758/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 Lake eutrophication poses a pressing global environmental challenge, severely degrading freshwater ecosystems. This study presents an explainable automated machine learning (XAutoML) framework to identify and quantify the spatially heterogeneous drivers of lake eutrophication across China. AutoGluon was employed for robust predictive modeling and KernelSHAP for explainability, particularly focusing on the ecological divide represented by the Hu Huanyong Line (Hu Line). AutoGluon demonstrated superior predictive performance (R² = 0.8407) compared to traditional machine learning models. The SHAP analysis revealed a stark spatial dichotomy in eutrophication drivers aligning with the Hu Line: in western China, natural factors like soil organic matter content (SOM) and mean normalized difference vegetation index are the primary drivers, with their protective effects susceptible to extreme precipitation. In contrast, eutrophication in eastern China is predominantly driven by synergistic anthropogenic pressures, including industrial nitrogen emissions (TN_ID) and agricultural phosphorus inputs. The study quantifies these regional differences, demonstrating that TN_ID are the most influential predictor in the east, while SOM is key in the west. We also elucidated complex interaction effects, such as the amplification of nutrient loss by extreme precipitation when SOM is high in the west, and the compounding effects of industrial and agricultural pollution in the east. This XAutoML framework provides a scientific basis for developing precise, region-specific watershed management strategies, advocating for ecosystem conservation in the west and stringent control of industrial and agricultural pollution in the east. Eutrophication AutoML Explainability Environmental management Hu Huanyong Line Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.docx floatimage1.png 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-7379758","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":516047454,"identity":"7fb31303-60c8-44ef-941e-7f870f7d1a97","order_by":0,"name":"Wenjie Qin","email":"","orcid":"","institution":"Huazhong University of Science and Technology (HUST)","correspondingAuthor":false,"prefix":"","firstName":"Wenjie","middleName":"","lastName":"Qin","suffix":""},{"id":516047455,"identity":"beff9a28-c867-476a-b563-6e9f33401687","order_by":1,"name":"Yuyi Zhang","email":"","orcid":"","institution":"Huazhong University of Science and Technology 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