基于深度强化学习的水分转移战略
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
|August 9, 2023
概括
动态水分转移优化 (DWDO) 通过降低营养水平,显著改善了环氧化的湖泊水质. 这种新的方法还大幅减少了75%的水分转移量,提供了更可持续的解决方案.
科学领域:
- 环境科学 环境科学
- 水资源管理 水资源管理
- 人工智能的人工智能
背景情况:
- 欧湖由于营养物质过多而导致水质不佳.
- 水分流是用来改善湖水质量的,但优化它是具有挑战性的.
- 将改善水质与最大限度地减少转水量之间的平衡是一个关键问题.
研究的目的:
- 开发和验证一个动态水分转移优化 (DWDO) 方法.
- 改善优质湖泊的水质,同时最大限度地减少水分的转移.
- 确定影响最佳水分转移策略的因素.
主要方法:
- 一个全面的水质模型与一个深度强化学习算法集成.
- 该DWDO方法在中国迪安奇湖的一个地区得到了应用和验证.
- 可解释机器学习被用来分析各种因素对转移的影响.
主要成果:
- 在迪安基湖,DWDO减少了7%的总和6%的总.
- 与以前的运营相比,每年转水量减少了75%.
- 气象指标和源/湖水质量被确定为关键影响因素.
结论:
- DWDO提供了一种有效的策略,以改善缩湖水质量并减少水分的转移.
- 该方法提供了基于多个因素的实时调整水分流的见解.
- 水质管理局 (DWDO) 展示了水质管理决策的稳定性和潜力.
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