Interval prediction of TN based on the bidirectional long short-term memory- residual block-bayesian optimization model

Interval prediction of TN based on the bidirectional long short-term memory- residual block-bayesian optimization model | 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 Interval prediction of TN based on the bidirectional long short-term memory- residual block-bayesian optimization model Hanzhi Zhang, Guoqiang Niu, Qihang Huang, Xiaoyong Li, Mi Lin, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7147232/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 Based on multi-station water quality data in Guangzhou section of the Pearl River Basin, a bidirectional long short-term memory - residual block-Bayesian optimization model (Bidirectional long short-term memory - residual block-Bayesian optimization model) is designed by combining BI-LSTM, residual network and Bayesian optimization. The results show that compared with the reference model, the model converges faster and the prediction accuracy is higher. To further investigate the impact of socioeconomic and land use factors on water quality, a random forest algorithm is employed to quantify the relative importance of land use composition and landscape pattern indices in influencing TN concentrations. The results reveal that variables such as land use intensity, landscape fragmentation, and specific land cover types substantially affect TN levels, indicating a strong correlation between anthropogenic activities and nitrogen pollution. This integrated modeling approach not only improves prediction accuracy but also provides important insights into the spatiotemporal mechanisms underlying water quality variation. The findings offer valuable support for data-driven decision-making in watershed management and targeted pollution mitigation strategies in rapidly urbanizing catchments. Total nitrogen Interval prediction Deep learning BILSTM model Full Text Additional Declarations No competing interests reported. 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-7147232","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":492207908,"identity":"9440f61f-a0a7-4363-9483-1365f7dd1b33","order_by":0,"name":"Hanzhi Zhang","email":"","orcid":"","institution":"South China Normal University","correspondingAuthor":false,"prefix":"","firstName":"Hanzhi","middleName":"","lastName":"Zhang","suffix":""},{"id":492207909,"identity":"306ea818-cb7c-4501-bf16-669bc96d99c3","order_by":1,"name":"Guoqiang Niu","email":"","orcid":"","institution":"South China University of 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