通过使用可解释机器学习,识别了通过回收水充电的景观湖中的叶绿素a动态的驱动因素
Chenchen Wang1, Juan Liu2, Chunsheng Qiu3
1School of Environmental and Municipal Engineering, Tianjin Chengjian University, Tianjin 300384, China; Tianjin Key Laboratory of Aquatic Science and Technology, Tianjin Chengjian University, Tianjin 300384, China; Key Laboratory of Drinking Water Science and Technology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
The Science of the total environment
|October 13, 2023
概括
使用回收水的湖泊中识别藻类繁殖的原因是具有挑战性的. 根据这项研究,来自回收水的酸- (NO3−-N) 是主要的驱动因素,特别是在更高的温度下.
科学领域:
- 环境科学 环境科学
- 水质管理水质管理
- 欧洲化研究研究 欧洲化研究
背景情况:
- 用回收水充电的湖泊面临着复杂的水质挑战.
- 鉴定控制这些系统中藻类繁殖的关键因素是困难的,因为输入和湖内过程的波动.
研究的目的:
- 开发一种可解释的机器学习框架,用于预测叶绿素-a (Chl-a) 度.
- 为了确定收到回收水的湖泊中藻类繁殖的主要营养驱动因素.
主要方法:
- 利用来自典型景观湖泊的时空空间水质数据.
- 采用一种随机森林模型,其中包含营养差异指数.
- 应用特征重要性和部分依赖图表用于驾驶员识别.
主要成果:
- 该模型准确地预测了Chl-a,确定了营养投入作为关键因素.
- 从回收水中获得的酸 (NO3−-N) 被确定为藻类繁殖的主要驱动因素,特别是在高温下.
- 发现NO3−-N和Chl-a之间的负相关性是藻类繁殖的结果,而不是原因.
结论:
- 回收水的NO3−-N输入显著影响藻类繁殖的动态.
- 了解因果关系对于在回收水源湖泊中有效管理环保是至关重要的.
- 开发的框架提供了新的洞察力,用于识别优化因素.
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