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Deep-reinforcement-learning-based water diversion strategy.

Qingsong Jiang1, Jincheng Li1, Yanxin Sun1

  • 1State Environmental Protection Key Laboratory of All Materials Flux in River Ecosystems, College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, PR China.

Environmental Science and Ecotechnology
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Summary
This summary is machine-generated.

Dynamic water diversion optimization (DWDO) significantly improved eutrophic lake water quality by reducing nutrient levels. This novel approach also drastically cut water diversion volumes by 75%, offering a more sustainable solution.

Keywords:
Deep reinforcement learningDynamic water diversion optimizationExplainable decision-makingParameter uncertaintyProcess-based model

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Area of Science:

  • Environmental Science
  • Water Resource Management
  • Artificial Intelligence

Background:

  • Eutrophic lakes suffer from poor water quality due to excess nutrients.
  • Water diversion is used to improve lake water quality but optimizing it is challenging.
  • Balancing water quality improvement with minimizing diversion volume is a key issue.

Purpose of the Study:

  • To develop and validate a dynamic water diversion optimization (DWDO) approach.
  • To improve water quality in eutrophic lakes while minimizing water diversion.
  • To identify factors influencing optimal water diversion strategies.

Main Methods:

  • A comprehensive water quality model was integrated with a deep reinforcement learning algorithm.
  • The DWDO approach was applied and validated in a region of Lake Dianchi, China.
  • Interpretable machine learning was used to analyze the impact of various factors on diversion.

Main Results:

  • DWDO reduced total nitrogen by 7% and total phosphorus by 6% in Lake Dianchi.
  • Annual water diversion volume was decreased by 75% compared to previous operations.
  • Meteorological indicators and source/lake water quality were identified as key influencing factors.

Conclusions:

  • DWDO offers an effective strategy for improving eutrophic lake water quality and reducing water diversion.
  • The approach provides insights into real-time adjustment of water diversion based on multiple factors.
  • DWDO demonstrates robustness and potential for efficient water quality management decision-making.