{"title":"Sustainable management strategy for phosphorus in large-scale watersheds based on the coupling model of substance flow analysis and machine learning","authors":"","doi":"10.1016/j.resconrec.2024.107897","DOIUrl":null,"url":null,"abstract":"<div><p>Clarifying the quantitative response relationship between socio-economic factors and water quality is key to developing a sustainable phosphorus (P) management strategy. we established a regulation framework for the feedback loop between socio-economic factors and water quality (i.e., P fluxes) from a spatial perspective based on a substance flow analysis (SFA) process model and machine learning (ML) model using a Bayesian optimization. The study demonstrated that utilizing long-term and intensive monitoring records, along with a ML algorithm to model the P response of the water body, resulted in good robustness and accuracy. Watershed P flows have a significant impact on P flux, and the response of P flux exhibits non-linear and non-lagged characteristics. The SFA–ML coupled model advances the current understanding of how P flows contribute to guiding P cycling in a watershed. P-SFA can serve as reliable feedback medium on the interaction between socio-economic activities and water quality in watersheds.</p></div>","PeriodicalId":21153,"journal":{"name":"Resources Conservation and Recycling","volume":null,"pages":null},"PeriodicalIF":11.2000,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Resources Conservation and Recycling","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0921344924004907","RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ENVIRONMENTAL","Score":null,"Total":0}
引用次数: 0
Abstract
Clarifying the quantitative response relationship between socio-economic factors and water quality is key to developing a sustainable phosphorus (P) management strategy. we established a regulation framework for the feedback loop between socio-economic factors and water quality (i.e., P fluxes) from a spatial perspective based on a substance flow analysis (SFA) process model and machine learning (ML) model using a Bayesian optimization. The study demonstrated that utilizing long-term and intensive monitoring records, along with a ML algorithm to model the P response of the water body, resulted in good robustness and accuracy. Watershed P flows have a significant impact on P flux, and the response of P flux exhibits non-linear and non-lagged characteristics. The SFA–ML coupled model advances the current understanding of how P flows contribute to guiding P cycling in a watershed. P-SFA can serve as reliable feedback medium on the interaction between socio-economic activities and water quality in watersheds.
阐明社会经济因素与水质之间的定量响应关系是制定可持续磷(P)管理策略的关键。我们基于物质流分析(SFA)过程模型和使用贝叶斯优化的机器学习(ML)模型,从空间角度建立了社会经济因素与水质(即磷通量)之间的反馈回路调节框架。研究表明,利用长期密集的监测记录和 ML 算法来模拟水体的 P 响应,具有良好的稳健性和准确性。流域 P 流量对 P 通量有重大影响,而 P 通量的响应表现出非线性和非滞后特征。SFA-ML 耦合模型加深了人们对 P 流量如何促进流域 P 循环的理解。P-SFA 可以作为流域中社会经济活动与水质之间相互作用的可靠反馈媒介。
期刊介绍:
The journal Resources, Conservation & Recycling welcomes contributions from research, which consider sustainable management and conservation of resources. The journal prioritizes understanding the transformation processes crucial for transitioning toward more sustainable production and consumption systems. It highlights technological, economic, institutional, and policy aspects related to specific resource management practices such as conservation, recycling, and resource substitution, as well as broader strategies like improving resource productivity and restructuring production and consumption patterns.
Contributions may address regional, national, or international scales and can range from individual resources or technologies to entire sectors or systems. Authors are encouraged to explore scientific and methodological issues alongside practical, environmental, and economic implications. However, manuscripts focusing solely on laboratory experiments without discussing their broader implications will not be considered for publication in the journal.