Deep-reinforcement-learning-based water diversion strategy.

ABSTRACT: Water diversion is a common strategy to enhance water quality in eutrophic lakes by increasing available water resources and accelerating nutrient circulation. Its effectiveness depends on changes in the source water and lake conditions. However, the challenge of optimizing water diversion remains because it is difficult to simultaneously improve lake water quality and minimize the amount of diverted water. Here, we propose a new approach called dynamic water diversion optimization (DWDO), which combines a comprehensive water quality model with a deep reinforcement learning algorithm. We applied DWDO to a region of Lake Dianchi, the largest eutrophic freshwater lake in China and validated it. Our results demonstrate that DWDO significantly reduced total nitrogen and total phosphorus concen

SUBMITTER: Jiang Q 

PROVIDER: S-EPMC10405199 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature