准确和可解释的化学氧气需求的预测使用可解释的增强算法与SHAP分析
Khaled Merabet1, Sungwon Kim2, Salim Heddam1
1Faculty of Science, Agronomy Department, Hydraulics Division, University 20 Août 1955 Skikda, Route El Hadaik, BP 26, Skikda, Algeria.
Scientific reports
|February 13, 2026
概括
预测化学氧气需求 (COD) 对水质至关重要. NGBoost和CatBoost模型在使用水质参数估计COD时显示出高准确度,NGBoost还量化了模型不确定性.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 水资源管理 水资源管理
背景情况:
- 有效的水质管理依赖于准确的化学氧气需求 (COD) 预测.
- 碳排放是水体有机污染的一个关键指标.
研究的目的:
- 为了比较六个集合增强模型的性能,用于COD估计.
- 确定用于COD预测的最有影响力的水质参数.
- 评估模型的解释性和不确定性.
主要方法:
- 采用了六种组合提升模型 (AdaBoost,CatBoost,XGBoost,LightGBM,HistGBRT,NGBoost) 的使用.
- 模型使用来自韩国两个监测站的水质数据进行训练和验证.
- 性能使用RMSE,MAE,R,NSE,PBIAS和SHAP进行了评估.
主要成果:
- 在Toilchun站 (R=0.979,NSE=0.958) 中,NGBoost显示了最高的准确性.
- 在Hwangji站,CatBoost表现最好 (R=0.861,NSE=0.733).
- SHAP分析显示,总有机碳 (TOC),生物化学氧气需求 (BOD5) 和悬浮固体 (SS) 是COD的关键预测因素.
结论:
- 整体提振模型,特别是NGBoost和CatBoost,对于COD预测是有效的.
- NGBoost提供概率分布的能力提高了COD可变性和模型不确定性的量化.
- 了解TOC,BOD5和SS等特定参数的影响对于控制水污染至关重要.
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