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使用整体机器学习模型预测城市水质的加权算术水质指数
Usman Mohseni1, Chaitanya B Pande2, Subodh Chandra Pal3
1Civil Engineering Department, Indian Institute of Technology Roorkee, Roorkee, 247667, Uttarakhand, India.
Chemosphere
|February 7, 2024
概括
极端梯度提升 (XG-Boost) 模型准确预测城市地下水的加权算术水质指数 (WA-WQI). 这种机器学习方法有助于在乌贾市更好地管理水资源和控制污染.
科学领域:
- 环境科学 环境科学
- 水资源管理 水资源管理
- 机器学习应用 机器学习应用
背景情况:
- 城市地下水质量对于公共卫生和环境可持续性至关重要.
- 评估地下水质量需要强大的指数计算方法,如加权算术水质指数 (WA-WQI).
- 传统方法可能无法捕捉物理化学参数和水质之间的复杂关系.
研究的目的:
- 开发和比较机器学习模型,用于预测Ujjain市的WA-WQI.
- 确定最准确的城市地下水质量评估模型.
- 为水资源规划和污染减缓提供数据驱动的见解.
主要方法:
- 收集了来自乌贾市54个病房的地下水样本,分析了八个关键的物理化学参数.
- 开发和训练了五种模型:人工神经网络 (ANN),支持向量机 (SVM),随机森林 (RF),极端梯度增强 (XG-Boost) 和多重线性回归 (MLR).
- 使用R平方 (R2),平均绝对误差 (MAE) 和根平均平方误差 (RMSE) 评估模型性能,并与接收器操作特征 (ROC) 曲线进行验证.
主要成果:
- 在WA-WQI预测中,XG-Boost模型表现出卓越的性能,在测试组中实现R2 = 0.987,RMSE = 3.273和MAE = 2.727.
- 所有开发的机器学习模型都显示出显著的预测能力.
- 最好的模型 (XG-Boost) 的曲线下的面积 (AUC) 为0.9048,证实了它的准确性.
结论:
- 机器学习模型,特别是XG-Boost,是准确评估城市地下水质量的高效工具.
- 这些发现为决策者和水资源专家提供了有价值的信息,以改善水资源规划和污染控制战略.
- 实施这些预测模型可以帮助确保为整个乌贾市提供安全健康的饮用水.
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