在地下水中使用在线传感器和机器学习进行水资源管理的重金属预测系统:典型工业园区的案例
Junfan Zhang1, Yuzhi Xuan1, Jingjing Lei1
1Zhejiang Huakun Geological Development Co., Ltd, Wenzhou, 325000, China.
Environmental pollution (Barking, Essex : 1987)
|April 24, 2025
概括
这项研究开发了一个自动化系统,使用pH和导电传感器与机器学习来预测地下水中的重金属. 该系统提供了一个实用的实时环境监测和风险评估方法.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 分析化学 分析化学
背景情况:
- 工业活动增加了地下水中的重金属污染.
- 地下水的监测是至关重要的,但由于其不可见性而具有挑战性.
- 现有的自动化传感器很难处理像重金属这样的复杂指标.
研究的目的:
- 开发一个实时的,自动化重金属预测系统,用于地下水.
- 将在线pH和导电性探针与机器学习算法集成在一起.
- 提高地下水监测的实用性和管理意义.
主要方法:
- 整合pH和导电性的在线监测探头.
- 机器学习算法的应用,包括随机森林,极端梯度提升 (XGB),多重线性回归 (MLR) 和反向传播神经网络 (BP).
- 利用间隔预测用于不确定性评估和实际风险管理.
主要成果:
- 为 (0.73), (0.78) 和铜 (0.87) 实现了高的R2值.
- 证明了随机森林和XGB模型的稳定性.
- XGB为预测间隔提供了最佳性能,其次是随机森林,MLR和BP.
结论:
- 自动化传感器与机器学习相结合,为长期环境监测提供先进的建议.
- 拟议的系统可以实时预测重金属和风险评估.
- 当预测的污染物间隔低于区域选水平时,地下水风险被认为是可以接受的.
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