利用深度学习进行基于聚变的地下水重金属污染指数预测
Ali Asghar Rostami1, Zahra Sedghi2, Ata Allah Nadiri3
1Department of Water Engineering, University of Tabriz, 29 Bahman Boulevard, Tabriz, East Azerbaijan, Iran.
Journal of contaminant hydrology
|July 17, 2025
概括
这项研究引入了一个深度学习框架,用于预测地下水重金属污染,达到高精度. 该方法增强了环境监测,并支持可持续的水资源管理.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 水文地质学 水文地质学
背景情况:
- 地下水受到重金属的污染对公共卫生和水资源可持续性构成重大风险.
- (Mn),铁 (Fe), (As) 和 (Pb) 是关键的重金属,在伊朗的Gultepe-Zarrinabad子盆地超过了允许的限制.
- 现有的水污染指数需要整合以进行全面评估.
研究的目的:
- 开发一种基于深度学习的数据融合框架,用于预测地下水重金属污染指数.
- 将五个水污染指数整合到一个统一的复合度量.
- 评估与传统机器学习模型相比,拟定框架的预测性能.
主要方法:
- 使用定制的根基数据融合和规范化方法,整合了五个水污染指数 (EHCI,HPI,HEI,MI,CI).
- 使用深度神经网络 (DNN) 来建模化污染指数.
- 该DNN模型与决策树 (DT),k-最近邻居 (KNN) 和人工神经网络 (ANN) 模型进行了基准测试.
主要成果:
- DNN 模型表现出卓越的预测准确性,R2 = 0.98.
- DNN模型实现了最小的误差 (RMSE和MAE=0.01) 和出色的概括能力.
- 拟议的基于融合的DNN方法在预测地下水重金属污染方面超过了传统的机器学习模型.
结论:
- 该研究成功地应用了基于聚变的DNN方法来进行全面的地下水重金属评估.
- 开发的框架显示了人工智能环境监测的巨大潜力.
- 这种方法可以为可持续的水资源管理做出重大贡献.
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