建立一个创新的机器学习驱动的饮用水质量评估模型,并考虑健康因素
Sha Jin1, Kejia Zhang2, Tuqiao Zhang2
1College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China.
Journal of environmental sciences (China)
|December 29, 2025
概括
一个新的饮用水质量指数 (DWQI) 模型整合了健康,先进的参数和优化的权重,以更好地评估水. 该工具通过指导政策和处理改进,有助于确保更安全的饮用水.
科学领域:
- 环境科学 环境科学
- 公共卫生 公共卫生
- 水资源管理 水资源管理
背景情况:
- 居民收入的增长增加了人们对饮用水对健康的影响的关注.
- 全球评估健康饮用水质量的系统尚未得到充分发展.
- 现有的水质指数往往缺乏全面的健康考虑.
研究的目的:
- 开发一个创新的饮用水质量指数 (DWQI) 模型.
- 整合健康考虑,先进的参数选择和增强的方法.
- 为评估和改善饮用水质量提供一个框架.
主要方法:
- K-意味着聚类以确定16个关键的水质参数.
- 开发了四个以健康为重点的综合体重优化方案.
- 使用分析层次流程和随机森林以获得最佳权重 (CWAR方案).
- 介绍了健康影响整合的理想价值概念和子指数计算.
主要成果:
- 在江省,90.93%的成品水达到或超过了二级标准 (2018-2023年).
- 水质与初级工业比例 (-0.33) 和城市收入 (0.17) 相对相关.
- "五水共同生活"政策显著改善了DWQI.
- 与传统方法相比,先进的处理方法产生了优越的成品水质.
- 中国主要城市60.87%的成品水被归类为II类.
结论:
- 拟议的DWQI模型有效地评估饮用水质量,并综合健康影响.
- 该模型增强了水环境管理,指导政策和处理优化.
- 这一框架有助于确保社区获得更安全的饮用水.
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