在干旱地区评估地下水质量,使用主要成分分析,地理信息系统和机器学习技术
Mustafa El-Rawy1, Mohamed Wahba2, Heba Fathi3
1Civil Engineering Department, Faculty of Engineering, Minia University, Minia 61111, Egypt; Civil Engineering Department, College of Engineering, Shaqra University, Dawadmi 11911, Saudi Arabia.
Marine pollution bulletin
|June 26, 2024
概括
埃及的地下水质量
科学领域:
- 环境科学 环境科学
- 水文地质学 水文地质学
- 数据科学数据科学数据科学
背景情况:
- 干旱地区面临着严重的水资源短缺,需要对地下水质量进行严格的评估.
- 可持续的水资源管理对这些地区的公共卫生和经济发展至关重要.
- 了解地下水化学是识别污染源和确保安全使用的关键.
研究的目的:
- 为了评估埃及阿苏特省的地下水质量.
- 识别空间模式和地下水污染的潜在来源.
- 评估机器学习对预测地下水质量建模的适用性.
主要方法:
- 分析了来自217个井的12个水质参数.
- 主要组件分析 (PCA) 和地理信息系统 (GIS) 的应用.
- 使用机器学习算法 (回归,XGBoost,决策树,随机森林,KNN) 进行预测分析.
主要成果:
- 地下水质量在空间上有所不同,污染物度较高与工业和人口密区有关.
- 大多数地下水样本质量很好或很好,尽管很少一部分不适合饮用.
- 斜坡回归显示出优异的预测准确性,平均绝对百分比误差 (MAPE) 较低.
结论:
- 先进的机器学习技术是干旱环境中可持续地下水管理的有效工具.
- 这些发现为埃及的水资源管理者和政策制定者提供了可操作的见解.
- 这项研究支持制定保护重要地下水资源的法规.
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