Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
Sustainable management strategy for phosphorus in large-scale watersheds based on the coupling model of substance flow analysis and machine learning. / Wei Liu; Tian Qin; Yuejin Chen; Junbao Yin; Zhiwen Li; Hanzhi Wang; Guangwei Ruan; Jiaqi Zhu; Huoqing Xiao, ; Абакумов, Евгений Васильевич; Yalan Zhang; Hu Du; Sunlin Chi; Jinying Xu; Yongdong Zhang ; Jianjun Dai; Xianchuan Xie.
в: Resources, Conservation and Recycling, Том 211, 107897, 01.12.2024.Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
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TY - JOUR
T1 - Sustainable management strategy for phosphorus in large-scale watersheds based on the coupling model of substance flow analysis and machine learning
AU - Wei Liu,
AU - Tian Qin,
AU - Yuejin Chen,
AU - Junbao Yin,
AU - Zhiwen Li,
AU - Hanzhi Wang,
AU - Guangwei Ruan,
AU - Jiaqi Zhu,
AU - Huoqing Xiao,
AU - Абакумов, Евгений Васильевич
AU - Yalan Zhang,
AU - Hu Du,
AU - Sunlin Chi,
AU - Jinying Xu,
AU - Yongdong Zhang ,
AU - Jianjun Dai,
AU - Xianchuan Xie,
PY - 2024/12/1
Y1 - 2024/12/1
N2 - 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.
AB - 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.
KW - Machine learning (ML) model
KW - Phosphorus cycle
KW - Poyang lake watershed
KW - Substance flow analysis (sfa)
KW - Sustainable management
UR - https://www.mendeley.com/catalogue/2209a1eb-cd3d-366d-a2bb-762aa868daff/
U2 - 10.1016/j.resconrec.2024.107897
DO - 10.1016/j.resconrec.2024.107897
M3 - Article
VL - 211
JO - Resources, Conservation and Recycling
JF - Resources, Conservation and Recycling
SN - 0921-3449
M1 - 107897
ER -
ID: 124286311