Unveiling the Spatial Distribution and Temporal Trends of Total Phosphorus in the Yangtze River: Towards a Predictive Time-Series Modeling for Environmental Management

Unveiling the Spatial Distribution and Temporal Trends of Total Phosphorus in the Yangtze River: Towards a Predictive Time-Series Modeling for Environmental Management | 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 Unveiling the Spatial Distribution and Temporal Trends of Total Phosphorus in the Yangtze River: Towards a Predictive Time-Series Modeling for Environmental Management Tianqi Ma, Xing Chen, Fazhi Xie, Jiyang Zhao, Zihan Zhang, Xinyi Fu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5687983/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 The accurate prediction of total phosphorus in water quality is crucial for monitoring ecosystem stability and eutrophication status. However, the distribution of natural environmental data such as water quality total phosphorus (TP) often undergoes complex changes over time. Stable and reliable predictive outcomes not only necessitate a degree of stability and periodicity within the natural data, but also require that TP prediction models exhibit strong adaptability to the random fluctuations and distribution drifts of environmental data. Therefore, adapting predictive models to accommodate distribution drifts in natural environmental data presents a challenge. This study provides a detailed description of the spatiotemporal variations of TP in the Yangtze River from 2019 to 2023. Utilizing data cleaning and data mining techniques, time series data were analyzed to generate a predictive dataset, with a particular emphasis on investigating the stability and periodicity of TP fluctuations. By comparing various time series forecasting models, the MTS-Mixers was ultimately selected as the experimental baseline model, and different modes were employed for time series prediction. The results demonstrate that the model maintains relatively high prediction accuracy within 20 time steps. The research findings not only offer a comprehensive description and reliable prediction of TP variations in the Yangtze River, but also provide effective methods and tools for water quality monitoring and management. They serve as a scientific basis for environmental protection and water quality improvement in the Yangtze River Basin, facilitating the formulation and implementation of relevant policies and advancing the sustainable development of the Yangtze River water environment. Furthermore, the study also confirms the applicability of machine learning in hydrological forecasting, which can be utilized for addressing environmental changes. Future research directions include ensuring the stability of critical monitoring data and exploring time-domain sub-band reconstruction methods to better understand the frequency characteristics of time series data, revealing hidden information and features. TP Time series analysis Machine learning Yangtze River Full Text Additional Declarations No competing interests reported. Supplementary Files SupportingInformation.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. 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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-5687983","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":393846259,"identity":"941712ca-00c7-469d-8cc4-5a0673193c63","order_by":0,"name":"Tianqi Ma","email":"","orcid":"","institution":"Anhui University","correspondingAuthor":false,"prefix":"","firstName":"Tianqi","middleName":"","lastName":"Ma","suffix":""},{"id":393846260,"identity":"d5f831d5-04dc-4961-8260-fd1ef84da354","order_by":1,"name":"Xing Chen","email":"","orcid":"","institution":"Anhui Jianzhu 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