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加强沿海地区地下水质量评估:一种混合建模方法
Md Galal Uddin1,2,3,4, M M Shah Porun Rana5, Mir Talas Mahammad Diganta1,2,3,4
1School of Engineering, University of Galway, Ireland.
Heliyon
|July 19, 2024
概括
本研究引入了一种可靠的根平均平方 (RMS) -水质指数 (WQI) 模型,增强了极端梯度增强 (XGBoost) 机器学习,以评估沿海地下水质量. 该模型有效地评估了孟加拉国博拉地区的地下水,将其归类为"公平"并显示出高预测准确度.
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:
- 沿海地下水 (GW) 监测对人类需求,农业,生态系统和环境可持续性至关重要.
- 传统的水质指数 (WQI) 模型因结果不一致而受到批评,需要更可靠的评估方法.
- 孟加拉国波拉地区是一个沿海地区,需要有效地评估地下水质量 (GWQ),以实现可持续的资源管理.
研究的目的:
- 用数据驱动方法评估沿海博拉地区的地下水质量 (GWQ).
- 通过结合极端梯度增强 (XGBoost) 机器学习 (ML) 算法来提高水质指数 (WQI) 模型的可靠性.
- 为监测和管理沿海GW资源提供一个强大的方法.
主要方法:
- 采用数据驱动的根平均平方 (RMS) 模型来评估地下水质量 (GWQ).
- 集成了极端梯度增强 (XGBoost) 机器学习 (ML) 算法,以提高RMS-WQI模型的可靠性.
- 分析了11个关键指标:度 (TURB),电导率 (EC),pH,总溶解固体 (TDS),酸盐 (NO3-), (NH4+), (Na), (K), (Mg), (Ca) 和铁 (Fe).
主要成果:
- 博拉地区的地下水质量 (GWQ) 被归类为"公平",计算的RMS-WQI得分从54.3到72.1不等 (平均65.2).
- 在采样的地下水中, (K), (Ca) 和 (Mg) 的度超过了指导值.
- 在预测GWQ方面,XGBoost ML算法表现出高灵敏度 (R2 = 0.97),而RMS-WQI模型显示出最小的不确定性 (<1%).
结论:
- 用XGBoost增强的RMS-WQI模型对于准确评估沿海地区的地下水质量 (GWQ) 是有效的.
- 这些发现支持使用这种先进的模型来有效监测和可持续管理沿海地下水资源.
- 这种方法为区域环境管理者和战略规划者提供了可靠的工具.
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