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利用可解释的机器学习来加强湖水质量的管理
Sajad Soleymani Hasani1, Mauricio E Arias1, Hung Q Nguyen1
1Department of Civil and Environmental Engineering, University of South Florida, 4202 E Fowler Ave, Tampa, FL, 33620, USA.
Journal of environmental management
|October 15, 2024
概括
机器学习模型准确地预测湖水质量参数,如总和. 度是一个关键的预测因素,流入量,湖水阶段,风和温度都会影响它的水平.
科学领域:
- 环境科学 环境科学
- 水质管理水质管理
- 机器学习应用 机器学习应用
背景情况:
- 淡水湖中的缩是一个全球性问题,由废水和下水带来的营养物质 (和) 过量造成.
- 这对水生生态系统和公共卫生产生影响,需要有效的监测和管理策略.
研究的目的:
- 使用机器学习 (ML) 算法预测关键的水质参数 (总,总,酸盐+酸盐,度).
- 为了确定这些参数的空间模式,并确定影响大型亚热带湖泊度水平的环境驱动因素.
- 通过特定站点和全湖的建模方法来支持运营决策.
主要方法:
- 采用了四种ML算法:极端梯度增强 (XGB),轻梯度增强机 (LGBM),支向量回归 (SVR) 和随机森林 (RF).
- 使用易于测量的变量来预测总 (TP),总 (TN),酸盐+酸盐 (NOx-N) 和度.
- 应用K-means集群用于空间分析和SHapley添加式扩展 (SHAP) 来识别度驱动因素.
主要成果:
- 在预测水质参数方面,XGB表现出卓越的性能.
- 湖泊阶段,水温和度被确定为营养水平的重要预测因素.
- 空间分析显示,根据营养和度水平,有三个不同的湖区.
- 流入和湖面阶段是入口附近度的主要驱动因素;风速和空气温度影响了湖中央的度.
结论:
- 机器学习模型可以有效地预测湖水质量参数,帮助管理决策.
- 度是影响营养动态的关键因素,由各种环境因素驱动.
- 频繁监测度及其驱动因素对于有效的湖泊管理和缓解环氧化是必不可少的.
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