用机器学习算法评估四个含水层中地下水对重金属的脆弱性
Gholamheidari Hamideh1, Entezari Mojgan2
1Post doctoral researcher of Department of Geography, University of Isfahan, Isfahan, Iran.
Journal of contaminant hydrology
|February 4, 2026
概括
机器学习模型准确地预测了伊斯法罕的重金属污染.
科学领域:
- 水文地质学 水文地质学
- 环境科学 环境科学
- 机器学习 机器学习
背景情况:
- 地下水受到重金属的污染对人类健康和生态系统构成重大风险.
- 了解重金属的空间分布和控制因素对于有效的水资源管理至关重要.
研究的目的:
- 研究伊斯法罕省地下水层中重金属 (铁,,,铜) 污染的空间分布和环境因素.
- 评估机器学习模型,特别是随机森林 (RF) 和支持矢量机器 (SVM) 在预测重金属污染方面的性能.
- 确定影响重金属度的关键水文地质和土壤特性.
主要方法:
- 在伊斯法罕省的四个地下水层中抽取150个井的样本.
- 使用ICP-MS,AAS,VGA和分析仪方法确定重金属度.
- 应用随机森林 (RF) 和支持矢量机器 (SVM) 模型,结合环境预测因素,如土壤特性,地下水位和含水层特征.
- 使用准确性,灵敏性和特异性指标评估模型性能.
主要成果:
- 铁,,和铜的峰值度分别为48,44.1,2.9和11.2毫克/升.
- 土壤特性和地下水位被确定为影响铁和分布的主要因素.
- 射频模型表现出优于SVM的性能,在预测重金属污染方面达到更高的准确性 (0.7874).
- 空间预测显示,布恩水层中铁和的度很高,在查迪根.
结论:
- 机器学习,特别是射频模型,对于预测地下水中的重金属污染非常有效.
- 水文地质因素对水层中的重金属分布表现出非线性控制.
- 该研究提供了一种可转移的方法,用于在干旱/半干旱地区评估地下水质量和可持续的含水层管理.
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