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集成机器学习使用水文气象信息来改进原水供应处理厂质量参数的建模
Christian Ortiz-Lopez1, Christian Bouchard1, Manuel J Rodriguez2
1Centre de Recherche en Aménagement et Développement (CRAD), Université Laval, 2325 Allée des Bibliothèques, Québec City, QC, G1V 0A6, Canada.
Journal of environmental management
|June 5, 2024
概括
集成机器学习模型准确预测原水质量变化,使饮用水处理厂 (DWTP) 的早期预警系统 (EWS) 成为可能. 这通过预测度和紫外线吸收率波动来加强决策.
科学领域:
- 环境科学 环境科学
- 水资源管理 水资源管理
- 机器学习应用 机器学习应用
背景情况:
- 流域降雨和河流流动事件影响原水质量.
- 饮用水处理厂 (DWTP) 以时间滞后检测到这些变化.
- 准确的预测模型对于有效的预警系统至关重要.
研究的目的:
- 评估整体机器学习 (EML) 模型用于预测原水质量参数.
- 评估EML在预测度和紫外线吸收 (UV254) 的准确性.
- 确定EML是否适合在DWTP中增强EWS.
主要方法:
- 使用了三个基于决策树的EML模型:随机森林 (RF),梯度增强 (GB) 和极端梯度增强 (XGB).
- 采用降雨量和河流流量时间序列作为输入预测器.
- 模拟的原水度和紫外线吸收 (UV254).
主要成果:
- 这三种EML模型都在预测原水度方面表现出很高的准确性 (r2值在0.80至0.87之间).
- 此外,EML模型在预测原水的紫外线吸收率 (UV254) (r2值在0.85到0.89之间) 上也表现出色.
- 射频模型显示了这两个参数的最佳性能.
结论:
- 对于准确的原水质量建模,EML方法非常有效.
- 这些模型可以显著提高DWTP中EWS的性能.
- 在水处理业务中,EML提高了决策过程.
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