机器学习辅助预测和剩余衰变的优化动态建模,以改善水质管理
Iman Jafari1, Rongmo Luo2, Fang Yee Lim1
1Department of Civil & Environmental Engineering, National University of Singapore, 1 Engineering Drive 2, Singapore, 117576, Singapore.
Chemosphere
|September 1, 2023
概括
这项研究通过将总残留 (TRC) 度替换为TRC需求,改善了水质模型,提高了预测准确度. 还开发了一种新的机器学习方法,用于更好地预测水分系统中的残留.
科学领域:
- 环境工程环境工程
- 水质管理水质管理
- 化学动力学 化学动力学
背景情况:
- 水质在源泉到水龙头之间波动很大,这对一致的管理构成了挑战.
- 在配送系统中预测消毒剂残留损失和副产品的形成至关重要,但复杂.
- 预测残留衰变的现有模型显示性能和专家共识的变化.
研究的目的:
- 提高现有的基于工艺的散体衰变模型的性能,以预测残留.
- 开发一种使用机器学习的在线预测方法,用于剩余衰变速率.
- 提出一种新的方法来预测水分配系统中的总残留 (TRC).
主要方法:
- 修改了现有的散体衰变模型,将初始的总残留 (TRC) 度替换为TRC需求.
- 实施了一种机器学习 (ML) 算法,特别是高斯过程回归 (GPR),以预测第一阶级TRC批量衰变率.
- 开发了一种新方法,将源水特性,运营行动和水需求纳入TRC预测中.
主要成果:
- 在模型修改后,实现了平均平均平方误差 (MSE) 改进38.03% (FOM),28.02% (PFOM),23.11% (SOM) 和33.29% (PSOM).
- 该GPR模型准确地预测了第一阶段TRC批量衰变率预测的动力参数和测试集.
- 这种新的方法表明了高可靠性和可靠的水质预测的潜力.
结论:
- 修改与TRC需求的衰变模型可以显著提高预测准确性.
- 机器学习为在线预测残留衰变动力学提供了一种可行的方法.
- 拟议的综合方法加强了供水系统管理的运营决策支持,最终改善了公共卫生结果.
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