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一个特征选择和可解释性的混合框架,用于饮用水处理厂的溶氧预测
Reza Hoshyarzadeh1, Laleh Divband Hafshejani2, Parvaneh Tishehzan1
1Department of Environmental Engineering, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
Scientific reports
|February 2, 2026
概括
在饮用水处理中准确的溶解氧 (DO) 预测通过一种新的混合框架得到改善. 这种方法提高了可持续工厂运营的模型透明度和效率.
科学领域:
- 环境科学 环境科学
- 水处理工程水处理工程
- 数据科学数据科学数据科学
背景情况:
- 准确的溶氧 (DO) 预测对于可持续的饮用水处理厂运行至关重要.
- 使用单个特征选择的传统方法可能会导致有偏见的预测器识别.
研究的目的:
- 为饮用水处理中DO预测开发一个强大而透明的框架.
- 整合多个特征选择和可解释性技术,以提高准确性和理解.
主要方法:
- 一个连续的混合框架,结合了相互信息 (MI),不纯度平均降低 (MDI),换重要性和SHAP可解释性.
- 训练使用七个影响水质参数的随机森林 (RF) 和XGBoost (XGB) 模型.
- 应用维度减小来提高计算效率.
主要成果:
- 历史DO,水温和度始终被确定为关键预测因素.
- pH和NO2对DO预测的影响最小.
- 减小尺寸降低了高达70%的模型复杂性,而不会牺牲预测准确性.
- 两种RF (R2 = 0.928) 和XGB (R2 = 0.942) 模型都表现出高性能,RMSE<0.27 mg/L.
结论:
- 拟议的框架为实时DO监控提供了一个可靠,可解释和具有成本效益的解决方案.
- 该方法可用于其他环境建模应用.
- 增强的特征选择和可解释性提高了DO预测准确性和运营效率.
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