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Updated: May 15, 2025

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结合集群和集体学习进行地下水质量监测:可持续水资源管理的数据驱动框架
Harjot Kaur1, Babankumar S Bansod2, Parth Khungar1
1Department of Material Science and Applications, CSIR-Central Scientific Instruments Organization, Chandigarh, 160030, India.
概括
印度旁遮普邦的地下水质量不佳,不适合饮用. 机器学习模型确定了GPI WQI与Ensemble软投票作为实时水质监测的高度准确工具.
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:
- 评估地下水质量对于公共卫生和可持续的水资源管理至关重要.
- 现有的水质指数 (WQI) 模型显示出显著的差异,需要针对印度次大陆量身定制的方法.
- 印度的旁遮普邦面临着地下水饮用能力低于既定标准的挑战.
研究的目的:
- 评估印度旁遮普邦的地下水质量,使用多个WQI模型和机器学习分类器.
- 确定最有效的WQI模型和机器学习组合,以进行准确的可饮性评估.
- 为实时地下水质量监测和决策支持开发数据驱动的框架.
主要方法:
- 使用六种WQI模型 (NSF-AM,NSF-GM,CCME,霍顿,西爪,GPI) 进行饮用性评估.
- 采用K-means集群用于数据预处理和特征精制,提高分类准确性.
- 集成和评估各种机器学习分类器,包括集体硬投票 (EHV) 和集体软投票 (ESV).
主要成果:
- 旁遮普省许多地区的地下水质量不佳,低于世卫组织和BIS标准.
- CCME WQI将地下水分类为贫困到边际的,不适合人类消费.
- 通过GPI WQI和ESV的结合,在计算成本低的情况下实现了卓越的性能指标 (例如99.13%准确率,100%精度/特异性,100%AUC).
结论:
- 由于模型差异,印度次大陆需要一个定制的WQI框架.
- 机器学习,特别是GPI WQI和ESV组合,为地下水质量评估提供了高效和准确的解决方案.
- 开发的框架作为一个有价值的决策支持工具,用于政策干预和资源有限的地区的可持续水资源管理.
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