Deep learning-based optimal adaptive regulation pathway of algal blooms in urban rivers under long-term uncertainties

Changqing Xu; Tianyu Jia; Te Xu; Yuqiao Lan; Nan Li; Haifeng Jia

doi:10.1016/j.watres.2025.124677

Deep learning-based optimal adaptive regulation pathway of algal blooms in urban rivers under long-term uncertainties

Changqing Xu
, Tianyu Jia
, Te Xu
, Yuqiao Lan
, Nan Li^*
, Haifeng Jia

^*此作品的通讯作者

Beijing Institute of Technology
Ministry of Industry and Information Technology
Tsinghua University
Suzhou University of Science and Technology

科研成果: 期刊稿件 › 文章 › 同行评审

2 引用（Scopus）

摘要

Algal bloom control remains a critical challenge in urban water resource management, particularly under the intensifying impacts of global climate change. Effectively addressing this issue requires strategies that account for long-term climatic uncertainties. This study applies the Dynamic Adaptive Policy Pathways (DAPP) framework to enhance urban algal bloom management through the integration of system failure signals, a deep learning-based surrogate model, and optimization algorithm. Using Suzhou, China, as a case study, developed 8 climate change scenarios and 19 regulatory scenarios to identify adaptive strategies. A surrogate model incorporating AdaBoost and Bagging techniques was developed to simulate algal bloom dynamics, achieving simulation times of under 10 s per scenario while maintaining high accuracy (R²>0.95). The NSGA-II algorithm was applied to optimize trade-offs between environmental performance and economic costs, accounting for infrastructure lock-in effects through incremental constraints. Results show that temperature rise poses the greatest threat to urban aquatic ecosystems, while precipitation and irradiance changes have marginal impacts. Among the control measures, green infrastructure (GI) was identified as the most cost-effective strategy to mitigate thermal impacts. This research provides a scalable decision-support framework for enhancing urban water ecosystems resilience under climate uncertainty.

源语言	英语
期刊论文编号	124677
期刊	Water Research
卷	288
DOI	https://doi.org/10.1016/j.watres.2025.124677
出版状态	已出版 - 1 1月 2026
已对外发布	是

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title = "Deep learning-based optimal adaptive regulation pathway of algal blooms in urban rivers under long-term uncertainties",

abstract = "Algal bloom control remains a critical challenge in urban water resource management, particularly under the intensifying impacts of global climate change. Effectively addressing this issue requires strategies that account for long-term climatic uncertainties. This study applies the Dynamic Adaptive Policy Pathways (DAPP) framework to enhance urban algal bloom management through the integration of system failure signals, a deep learning-based surrogate model, and optimization algorithm. Using Suzhou, China, as a case study, developed 8 climate change scenarios and 19 regulatory scenarios to identify adaptive strategies. A surrogate model incorporating AdaBoost and Bagging techniques was developed to simulate algal bloom dynamics, achieving simulation times of under 10 s per scenario while maintaining high accuracy (R²>0.95). The NSGA-II algorithm was applied to optimize trade-offs between environmental performance and economic costs, accounting for infrastructure lock-in effects through incremental constraints. Results show that temperature rise poses the greatest threat to urban aquatic ecosystems, while precipitation and irradiance changes have marginal impacts. Among the control measures, green infrastructure (GI) was identified as the most cost-effective strategy to mitigate thermal impacts. This research provides a scalable decision-support framework for enhancing urban water ecosystems resilience under climate uncertainty.",

keywords = "Algal bloom, DAPP, Green infrastructure, Long-term uncertainty, Optimization pathway",

author = "Changqing Xu and Tianyu Jia and Te Xu and Yuqiao Lan and Nan Li and Haifeng Jia",

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year = "2026",

month = jan,

day = "1",

doi = "10.1016/j.watres.2025.124677",

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T1 - Deep learning-based optimal adaptive regulation pathway of algal blooms in urban rivers under long-term uncertainties

AU - Xu, Changqing

AU - Jia, Tianyu

AU - Xu, Te

AU - Lan, Yuqiao

AU - Li, Nan

AU - Jia, Haifeng

PY - 2026/1/1

Y1 - 2026/1/1

N2 - Algal bloom control remains a critical challenge in urban water resource management, particularly under the intensifying impacts of global climate change. Effectively addressing this issue requires strategies that account for long-term climatic uncertainties. This study applies the Dynamic Adaptive Policy Pathways (DAPP) framework to enhance urban algal bloom management through the integration of system failure signals, a deep learning-based surrogate model, and optimization algorithm. Using Suzhou, China, as a case study, developed 8 climate change scenarios and 19 regulatory scenarios to identify adaptive strategies. A surrogate model incorporating AdaBoost and Bagging techniques was developed to simulate algal bloom dynamics, achieving simulation times of under 10 s per scenario while maintaining high accuracy (R²>0.95). The NSGA-II algorithm was applied to optimize trade-offs between environmental performance and economic costs, accounting for infrastructure lock-in effects through incremental constraints. Results show that temperature rise poses the greatest threat to urban aquatic ecosystems, while precipitation and irradiance changes have marginal impacts. Among the control measures, green infrastructure (GI) was identified as the most cost-effective strategy to mitigate thermal impacts. This research provides a scalable decision-support framework for enhancing urban water ecosystems resilience under climate uncertainty.

AB - Algal bloom control remains a critical challenge in urban water resource management, particularly under the intensifying impacts of global climate change. Effectively addressing this issue requires strategies that account for long-term climatic uncertainties. This study applies the Dynamic Adaptive Policy Pathways (DAPP) framework to enhance urban algal bloom management through the integration of system failure signals, a deep learning-based surrogate model, and optimization algorithm. Using Suzhou, China, as a case study, developed 8 climate change scenarios and 19 regulatory scenarios to identify adaptive strategies. A surrogate model incorporating AdaBoost and Bagging techniques was developed to simulate algal bloom dynamics, achieving simulation times of under 10 s per scenario while maintaining high accuracy (R²>0.95). The NSGA-II algorithm was applied to optimize trade-offs between environmental performance and economic costs, accounting for infrastructure lock-in effects through incremental constraints. Results show that temperature rise poses the greatest threat to urban aquatic ecosystems, while precipitation and irradiance changes have marginal impacts. Among the control measures, green infrastructure (GI) was identified as the most cost-effective strategy to mitigate thermal impacts. This research provides a scalable decision-support framework for enhancing urban water ecosystems resilience under climate uncertainty.

KW - Algal bloom

KW - DAPP

KW - Green infrastructure

KW - Long-term uncertainty

KW - Optimization pathway

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DO - 10.1016/j.watres.2025.124677

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Deep learning-based optimal adaptive regulation pathway of algal blooms in urban rivers under long-term uncertainties

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