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深度学习框架用于在土地使用压力下绘制沿海水层中酸盐污染的地图

Morad Chahid¹, Jamal Eddine Stitou El-Messari¹, Ismail Hilal²

¹Laboratory of Applied and Marine Geosciences, Geotechnics and Geohazards (LR3G), Faculty of Sciences, Department of Geology, Abdelmalek Essaâdi University, Tetouan, Morocco.

Scientific reports

|October 7, 2025

概括

这项研究开发了一个人工智能框架,用于预测沿海地下水中的酸盐污染,确定风险最高的农业和城市地区. 该模型确定了主要的污染源,如废水和农业下水.

科学领域:

环境科学环境科学
水文地质学水文地质学
人工智能的人工智能

背景情况:

分散的酸盐污染对沿海地下水质量构成重大威胁,特别是在农业密集和土地利用变化的地区.
保护这些重要资源需要准确的方法来预测污染热点和了解污染驱动因素.

研究的目的:

开发和评估一个可解释的深度学习框架,用于预测沿海地下水中的酸盐度.
确定易受高酸盐污染的地区,并确定关键因素.

主要方法:

将水化学数据 (EC,Cl-,OM,FC) 和遥感指标 (NDVI,LU/LC) 整合到深度学习模型 (MLP,TabNet) 中.
采用基于注意力的架构TabNet,以其卓越的性能和可解释性.
利用LASSO回归来确定酸盐污染的主导预测因素.

主要成果:

TabNet实现了81.60%的准确性和84.13%的宏观平均回忆,表现优于MLP.
便大肠杆菌 (FC) 和电导率 (EC) 被确定为酸盐污染的主要预测因素.
风险地图显示了农业和郊区的污染热点,表明了混合污染源.

结论:

可解释的人工智能与地理空间分析相结合,为有针对性的地下水监测和管理提供了强大的工具.
该框架可应用于其他面临分散污染挑战的沿海水层,有助于可持续的地下水治理.

关键词:

沿海水源层中的水层.环境影响评估环境影响评估可解释的人工智能土地使用变化酸盐污染污染酸盐污染遥感是一种远程传感.在 TabNet TabNet 中使用.

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