对废水处理厂废水质量预测监督学习模型的比较分析
Liu Bo-Qi1, Zhou Ding-Jie2, Zhao Yang1
1State Key Laboratory of Regional and Urban Ecology, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, Fujian, China.
PloS one
|June 10, 2025
概括
XGBoost和Support Vector Machine (SVR) 模型对预测废水废水质量的预测非常有希望. XGBoost为优化废水处理厂运营和环境可持续性提供了最佳的准确性和稳定性.
科学领域:
- 环境科学 环境科学
- 水处理工程水处理工程
- 机器学习应用 机器学习应用
背景情况:
- 废水质量预测对于废水处理厂 (WWTP) 运营,监管合规性和环境可持续性至关重要.
- 监督学习模型为准确的废水质量预测提供了潜力.
研究的目的:
- 评估和比较五个监督学习模型的排水质量预测性能.
- 通过废水质量指数 (EQI) 确定最有效的模式来优化WWTP运营.
主要方法:
- 利用来自中国珠海的WWTP (84条记录) 的月度数据集.
- 比较了AdaBoost,反向传播神经网络 (BP-NN),支向量机 (SVR),XGBoost和梯度增强 (GB) 模型.
- 使用R2,平均绝对百分比误差 (MAPE) 和平均偏差误差 (MBE) 评估模型性能,并通过GridSearchCV和交叉验证进行超参数优化.
主要成果:
- XGBoost展示了准确性和稳定性的最佳平衡,实现了最低的MAPE (6.11%) 和高的R2 (0.813).
- SVR表现出极好的准确度 (R2 = 0.826),但在错误控制方面存在局限性.
- 模型的性能各不相同,突出了根据特定的运营目标进行仔细选择的必要性.
结论:
- XGBoost 是一种非常有效的废水质量预测模型,为WWTPs提供强大的性能.
- 结果支持数据驱动的决策,用于废水管理中的智能流程优化.
- 未来的研究应该考虑更大的数据集和来自多个WWTP的数据,以提高概括性.
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