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使用新型混合分类和回归模型预测地下水质量
Mouigni Baraka Nafouanti1, Junxia Li2, Usman Sunusi Usman1
1School of Environmental Studies, China University of Geosciences, Wuhan 430074, China.
Journal of contaminant hydrology
|December 31, 2025
概括
新的混合模型准确地预测了北中国平原的地下水质量,识别了像Na+和HCO3-. .等关键因素. 这为管理这一重要资源提供了有效的解决方案.
科学领域:
- 环境科学 环境科学
- 水文地质学 水文地质学
- 机器学习应用 机器学习应用
背景情况:
- 地下水质量的恶化是北中国平原 (NCP) 的一个关键问题,影响了供水.
- 传统的地下水质量评估方法耗费大量资源和时间.
- 国家核电站严重依赖地下水,需要有效的质量监测和管理策略.
研究的目的:
- 开发和评估新的混合机器学习模型,用于预测州,NCP的地下水质量.
- 为了比较各种混合模型的性能,包括堆叠分类器,LightGBM-PSO,GA-RF和GA-LightGBM.
- 通过SHAP分析来确定影响地下水质量的关键水化参数.
主要方法:
- 从州收集和分析了460个地下水化学样本.
- 应用混合机器学习模型:XGBoost,LightGBM,LightGBM-PSO,GA-RF和GA-LightGBM用于分类和回归.
- 利用SHAP分析来确定特征对地下水质量预测的重要性.
主要成果:
- 混合型号LightGBM-PSO和GA-RF实现了高预测准确度 (高达99%).
- 该GA-RF模型表现出极好的性能,R2=0.99,RMSE=0.01和MAE=0.02.
- 水化学揭示了HCO3−,Cl−,Ca2+,和SO42−作为主导离子,水岩相互作用,蒸发和盐化作为关键影响因素. 水质指数 (WQI) 显示 60.28% 的水质很好/很好,40% 的水质很差/非常差.
结论:
- 混合机器学习模型对于准确地预测地下水质量非常有效.
- 影响地下水质量的关键因素包括Na+,HCO3−,F−,Cl−,Ca2+,SO42−,Mg2+,TDS和EC.
- 这项研究为NCP和全球可持续地下水资源管理提供了宝贵的见解.
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