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使用机器学习进行水库特定洪水预测:对三个不同的沉积水层进行比较分析
1Department of Civil and Environmental Engineering, Brunel University London, Uxbridge, UB8 3PH, United Kingdom.
The Science of the total environment
|October 30, 2025
概括
机器学习模型显示,根据英国的含水层地质学,洪水预测的准确性各不相同. 石灰岩含水层是高度可预测的,而绿沙含水层由于复杂的地下水与河流的相互作用而存在重大建模挑战.
科学领域:
- 水文学和水文地质学
- 地质科学 地质科学
- 机器学习应用 机器学习应用
背景情况:
- 准确的洪水预测对于减轻灾难性影响至关重要,但其准确性受地质条件的影响.
- 了解含水层特定的地下水河流动态对于有效的洪水预报至关重要.
研究的目的:
- 评估四种机器学习模型 (TFT,Informer,LSTM,XGBoost) 的性能,用于多地平线洪水预测 (1-4天).
- 评估英国泰士河流域不同类型的含水层 (石灰,石灰,绿砂) 对洪水预测准确性的影响.
- 调查地下水文学与基于机器学习的洪水预测可靠性的关系.
主要方法:
- 根据英国洪水风险地图,地质数据和环境署的水文数据,选择水文站.
- 采用了四种机器学习模型:变压器 (TFT),信息器,长期短期存储器 (LSTM) 和XGBoost.
- 分析模型性能,使用R平方值和相关系数 (r) 来评估地下水与河流的联系.
主要成果:
- 模型的准确性在各种水库类型之间有很大的差异:石灰石 (R 2 = 0.98-0.99), (R 2 = 0.77-0.80) 和绿沙 (低或负R 2).
- 变压器和LSTM模型的性能优于XGBoost,特别是在石灰岩含水层中,地下水位 (GWL) 与河流的快速相互作用.
- 相关性分析证实了石灰石 (r=0.84) 中GWL与河流的强烈联系,石灰石 (r=0.26) 中的中度联系,绿沙 (r=-0.14) 中的弱/负联系.
结论:
- 地下地质对基于机器学习的洪水预测的可靠性产生重大影响.
- 预测框架必须适应特定的地质环境,以改善洪水风险管理和弹性规划.
- 将地下水位数据与先进的变压器架构集成,为早期洪水预警系统提供了更具物理一致性的方法.
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