物理限制的深度学习用于预测水库热结构:增强的解释性和推断能力
Jianying Song1, Jie Song1, Yujun Yi1
1State Key Laboratory of Regional Environment and Sustainability, School of Environment, Beijing Normal University, Beijing, 100875, China.
Water research
|December 4, 2025
概括
一个新的物理约束深度学习框架 (P-DL) 增强了水库热结构预测. 这种方法提高了生态保护策略的准确性和可靠性,优于传统模型.
科学领域:
- 环境科学 环境科学
- 水资源管理 水资源管理
- 机器学习 机器学习
背景情况:
- 准确的水库热结构预测对于生态保护和优化水库运营至关重要.
- 现有的数据驱动模型在有限的数据,不良的物理解释性和不可靠的推断方面扎.
- 挑战包括预测水温动态和理解分层.
研究的目的:
- 提出一个物理限制的深度学习框架 (P-DL),以克服当前数据驱动模型的局限性.
- 为了提高预测准确性,物理解释性和储水池热结构的外推稳定性.
- 为储水池中智能热管理提供可靠的工具.
主要方法:
- 开发了一个物理限制的深度学习框架 (P-DL).
- 使用机制驱动的流程模型增强培训数据,并确定了关键影响因素.
- 将垂直温度配置文件转换为可解释的参数 (A,B,D),以表示分层强度,并通过弱物理约束来改善外推.
- 与P-DL与随机森林 (RF),支持矢量机 (SVM) 和长短期内存 (LSTM) 进行了比较,使用的是江 (XJB) 水库数据.
主要成果:
- 与RF,SVM和LSTM相比,P-DL在预测短期局部波动方面表现出卓越的准确性.
- 可解释的参数 (A,B,D) 有效地捕获了分层强度,峰值时间和时间演变.
- 在SSP5-8.5情景下,P-DL在强分层过程中获得了表面温度的高精度 (RMSE:0.83-1.1°C;R2:0.88-0.9).
- 在本地和总体水平上,P-DL显示出优异的一致性 (KLD: 2.85-5.71;KSS: 0.2-0.4).
结论:
- 拟议的P-DL框架显著提高了预测准确性,物理解释性和储热结构的外推稳定性.
- 该框架为水库中的智能热管理和生态保护提供了有价值的参考.
- 混合模型和弱物理约束方法可以推进对其他环境因素的数据驱动预测.
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