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Updated: Jan 15, 2026

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深度学习模拟和决策支持系统用于地下水盐度风险评估,位于泰国Chao Phraya河流域下游,泰国
Mojtaba Heydarizad1,2, Zhongfang Liu3, Nathsuda Pumijumnong4
1State Key Laboratory of Marine Geology, Tongji University, Shanghai, 200092, China. mojtabaheydarizad@yahoo.com.
Environmental monitoring and assessment
|October 15, 2025
概括
由于提取的增加,地下水化正在沿海地区扩大. 这项研究使用机器学习来确定地下水提取是主要的驱动因素,为盐度风险评估提供工具.
科学领域:
- 环境科学环境科学
- 水文地质学 水文地质学
- 机器学习应用程序 机器学习应用程序
背景情况:
- 地下水的化威胁到沿海地区的淡水安全.
- 密集的地下水开采和气候变化加剧了这个问题.
- 查普拉雅河流域面临着严重的盐度挑战.
研究的目的:
- 分析地下水盐度的空间和时间演变.
- 通过机器学习识别盐度动态的关键驱动因素.
- 开发一个用于盐度风险管理的决策支持系统.
主要方法:
- 采用了多方法机器学习框架,包括SHAP和高斯配方模型.
- 开发了一个图形神经网络 (GNN) 来模拟总溶解固体 (TDS).
- 实施基于场景的决策支持系统 (DSS) 用于风险可视化.
主要成果:
- 地下水的提取被确定为盐度的主要驱动因素.
- 在2008年至2020年期间,观察到高盐度地区的显著扩张.
- GNN模型显示了TDS度的高预测精度.
结论:
- 该研究为实时地下水盐度风险评估提供了一个可扩展的框架.
- 这些发现突显了人为因素对沿海含水层盐化的影响.
- 综合方法支持基于证据的水资源管理在数据稀缺的地区.
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