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

TY - JOUR

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

N1 - Publisher Copyright: © 2025 Elsevier Ltd

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

UR - https://www.scopus.com/pages/publications/105018663816

U2 - 10.1016/j.watres.2025.124677

DO - 10.1016/j.watres.2025.124677

M3 - Article

AN - SCOPUS:105018663816

SN - 0043-1354

VL - 288

JO - Water Research

JF - Water Research

M1 - 124677

ER -