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Ata Joodavi^1,2, Hadi Sanikhani, Maysam Majidi¹

¹East Water and Environment Research Institute (EWERI), Mashhad, Iran.

|May 12, 2025

概括

地下水中的 (Cr) 是一个健康风险,由于测试有限,可能被低估. 像人工神经网络 (ANN) 这样的先进模型可以预测Cr污染,识别60多万有风险的个体.

科学领域:

环境科学环境科学
水文地质学水文地质学
计算科学计算科学

背景情况:

地下水受到 (Cr) 的污染对人类健康构成重大风险.
低估了Cr污染严重程度可能是由于井检测不足.
地质Cr构成威胁,需要准确的预测工具来进行风险评估.

研究的目的:

用各种预测模型估计地下水中的地质度.
为了比较软计算技术 (ANN,GEP,MARS,M5树) 与整体 (RF) 和线性 (MLR) 模型的性能.
为了确定伊朗东北部受Cr污染风险的人口.

主要方法:

使用了676个地下水Cr度测量的数据集.
采用并比较多种预测模型:人工神经网络 (ANN),随机森林 (RF),基因表达编程 (GEP),多变量自适应回归线 (MARS),M5树和多重线性回归 (MLR).
基于使用地质和地化学参数的预测准确性的评估模型.

主要成果:

人工神经网络 (ANN) 对地质Cr度的预测准确度最高.
随机森林 (RF) 提供了具有竞争力的结果,其次是GEP和MARS.
多重线性回归 (MLR) 显示了最低的准确性,表明模拟复杂的地球化学过程的局限性.
该ANN模型在中部和西部地区确定了超过60万名有风险的个人.

结论:

先进的预测模型,特别是ANN,对于估计地下水Cr污染是有效的.
这些模型是地下水质量管理和风险评估的宝贵工具.
研究结果强调了这些方法在面临类似地下水污染挑战的其他地区的潜在适用性.

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