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提供伊朗中部干旱地区地下水资源质量参数的预测模型:喀山平原的案例研究
Aysan Morovvati Zarajabad1, Mahdi Hadi2, Ramin Nabizadeh Nodehi1
1Department of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Heliyon
|June 6, 2024
概括
使用随机森林等机器学习模型预测地下水质量是高效和经济的. 这种方法有助于可持续的水资源管理和干旱地区的风险评估.
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:
- 来自自然和人为来源的地下水污染会降低水质,威胁生态系统和人类健康.
- 可持续的水资源管理需要确保水质和水量.
- 传统的地下水质量测试是昂贵和耗时的,强调了需要有效的预测方法.
研究的目的:
- 评估三种不同的建模方法在干旱环境中预测地下水质量的有效性.
- 确定评估地下水化学参数的最准确和最经济的方法.
主要方法:
- 利用R编程语言开发和比较三个预测模型:随机森林 (RF),多重线性回归 (MLR) 和决策树 (DT).
- 应用这些模型来预测干旱地区地下水资源的各种化学参数.
主要成果:
- 随机森林 (RF) 模型在预测总体地下水质量参数方面表现卓越.
- 多重线性回归 (MLR) 模型显示出强大的预测能力,特别是在Aran Va Bidgol地区的总硬度 (TH).
- 决策树 (DT) 模型的性能很好,但不如RF模型那么准确.
结论:
- 机器学习模型,特别是射频,为预测干旱地区地下水质量提供了有效和经济的解决方案.
- 这些预测方法支持有效的地下水资源管理,保护策略和风险评估.
- 该研究验证了计算建模的使用,作为在水文地质评估中的一个有价值的工具.
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