深度学习作为水力动力学模型在预测湖泊温度概况中的改进工具:在Ogouchi水库的一个案例研究
Hieu Ngoc Le1, Tetsuya Shintani1
1Department of Civil and Environmental Engineering, Tokyo Metropolitan University, 1-1 Minami-Osawa, Hachioji, Tokyo, 192-0397, Japan.
Journal of environmental management
|January 30, 2026
概括
一个新的混合模型结合了水力动力学和长短期记忆 (LSTM) 方法来准确预测水温配置文件. 这种数据驱动的方法可以提高预测,同时降低计算成本和校准工作.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 水文学的水文学
背景情况:
- 大数据分析为水资源管理提供了新的机会.
- 结合基于过程的和数据驱动的模型可以提高预测的准确性,并保持物理关系.
研究的目的:
- 开发和评估一种混合模型,用于预测Ogouchi水库的垂直温度概况 (VTP).
- 与独立模型相比,提高VTP预测准确度,减少计算需求.
主要方法:
- 通过将一个简单的水力动力学模型与长短期内存 (LSTM) 网络相结合,开发了一种混合模型.
- 使用了一种数据同化技术.
- 性能与基线水力动力学模型,参考水力动力学模型和混合模型进行了比较,用于每周,两周和三周的VTP预测.
主要成果:
- 混合模型成功地减轻了在不同深度的简单模型中观察到的系统偏差.
- 混合模型实现了与完全校准的参考模型可比的预测性能.
- 通过混合模型实现了计算成本和校准工作的显著降低.
结论:
- 混合模型代表了水资源预测的新范式,将物理过程与数据驱动的校正整合在一起.
- 这种方法有效地解决了独立模型中的差异,为VTP预测提供了更有效,更准确的解决方案.
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