使用机器学习优化水质指数:河流和水库系统的六年比较研究
Peng Yuan1, Hengchang Li1, Xianjie Yi2
1Pinglu Canal Group Co., Ltd., Nanning, 530004, China.
Scientific reports
|September 30, 2025
概括
这项研究使用机器学习和新方法来增强水质指数 (WQI) 模型,提高准确性并识别关键污染物,以便更好地管理水资源.
科学领域:
- 环境科学 环境科学
- 水资源管理 水资源管理
- 数据科学数据科学数据科学
背景情况:
- 气候变化和人类活动威胁着全球的水质.
- 现有的水质指数 (WQI) 模型在参数权重,聚合和透明度方面面临挑战.
- 强大的水质评估对于有效的水资源管理和安全的饮用水至关重要.
研究的目的:
- 开发和验证一个改进的水质指数 (WQI) 模型框架.
- 为了比较不同的机器学习算法,权重方法和聚合函数对WQI的性能.
- 确定关键的水质参数,优化地表水体的监测效率.
主要方法:
- 一个比较优化框架,整合了三个机器学习算法 (包括极端梯度提升 - XGBoost) 和各种权重/聚合方法.
- 对丹江口水库 (DJKR) 河流和水库系统的31个地点的六年 (2017-2022) 每月水质数据的分析.
- 开发了一种新的Bhattacharyya平均WQI模型 (BMWQI) 与排名顺序中位素 (ROC) 权重方法相结合.
主要成果:
- XGBoost 模型表现出卓越的性能,在河流地区实现了 97% 的准确性.
- 与其他WQI模型相比,建议使用ROC权重的BMWQI模型显著降低了不确定性 (河流为17.62%,水库为4.35%).
- 确定的主要污染物包括河流的总 (TP),酸盐指数和氨,以及DJKR系统中的水库的TP和水温.
结论:
- 机器学习和创新的聚合功能大大改善了地表水质量评估方法.
- 开发的框架为制定水质政策提供了实际价值,因为它允许有针对性的污染物识别.
- 可适应的框架在各种水生系统中具有广泛的适用性,以优化监测和管理.
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