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基于时间序列的机器学习,用于预测各种试剂剂量的全规模饮用水处理中的多变量水质
Hongjiao Pang1, Yawen Ben2, Yong Cao3
1State Key Laboratory of Environmental Aquatic Chemistry, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
Water research
|November 18, 2024
概括
预测饮用水质量对于智能水资源管理至关重要. 一种优化的机器学习方法准确地预测水质,考虑试剂剂量效应,优于基线模型.
科学领域:
- 环境科学 环境科学
- 水处理技术 水处理技术
- 机器学习应用 机器学习应用
背景情况:
- 准确的饮用水质量预测对于智能供水管理和高效的水处理至关重要.
- 了解试剂剂量的时间依赖性影响对于提高预测准确性至关重要.
研究的目的:
- 提出一种优化的时间序列机器学习方法,用于预测多变量饮用水质量.
- 评估传统和深度学习模型的性能,考虑时间依赖的试剂剂量效应.
主要方法:
- 构建的特征工程时间序列数据集从一个全尺寸的处理厂,包括流入,试剂和废水数据.
- 开发和评估了七个预测模型 (ML和DL) 与一个天真的平均基线.
- 使用SHAP分析来确定模型的可解释性.
主要成果:
- 优化传统的机器学习模型与时间特征工程匹配或超过深度学习模型和基线.
- 一个XGBoost模型证明了在12小时的时间延迟下,对四个水质特征的优异预测准确性.
- 在平均绝对百分比误差 (MAPE) 中,XGBoost模型的表现超过了天真基线的3-4%.
结论:
- 纳入12小时间隔有效地捕捉了试剂剂量对水质预测的延迟影响.
- 优化的机器学习技术为增强水净化过程提供了巨大的潜力.
- 这项研究支持供水行业的数据驱动决策.
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