Process‐Guided Deep Learning Predictions of Lake Water Temperature
Process‐Guided Deep Learning Predictions of Lake Water Temperature
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DOI：
10.1029/2019wr024922
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发表时间：
2019-11
影响因子：
5.4
通讯作者：
J. Read;X. Jia;J. Willard;A. Appling;Jacob Aaron Zwart;S. Oliver;A. Karpatne;Gretchen J. A. Hansen;P. Hanson;William Watkins;M. Steinbach;Vipin Kumar
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文献类型：
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作者：
J. Read;X. Jia;J. Willard;A. Appling;Jacob Aaron Zwart;S. Oliver;A. Karpatne;Gretchen J. A. Hansen;P. Hanson;William Watkins;M. Steinbach;Vipin Kumar
水资源数据的快速增长为利用先进的深度学习工具加速知识发现创造了新的机会。将理论与最先进的经验技术相结合的混合模型有可能改善预测，同时保持对物理定律的真实性。本文评估了过程引导的深度学习（PGDL）混合建模框架，并以预测特定深度的湖水温度为用例。PGDL模型有三个主要组成部分：具有时间意识的深度学习模型（长短期记忆重现），基于理论的反馈（违反能量守恒的模型惩罚），以及使用合成数据初始化网络的模型预训练（来自基于过程的模型的水温预测）。现场水温用于训练PGDL模型、深度学习（DL）模型和基于过程（PB）的模型。在各种条件下评估模型性能，包括当训练数据稀疏时以及当预测超出训练数据集的范围时。对于两个详细的研究湖泊，PGDL模型的性能（通过均方根误差（RMSE）测量）上级DL和PB，但仅当预训练数据包含比训练期更大的变异性时。当扩展到68个湖泊时，PGDL模型也表现良好，在测试期间的中位数RMSE为1.65 °C（DL：1.78 °C，PB：2.03 °C;在少数湖泊中，PB或DL模型更准确）。这个案例研究表明，将科学知识整合到深度学习工具中，有望改善许多重要环境变量的预测。
The rapid growth of data in water resources has created new opportunities to accelerate knowledge discovery with the use of advanced deep learning tools. Hybrid models that integrate theory with state‐of‐the art empirical techniques have the potential to improve predictions while remaining true to physical laws. This paper evaluates the Process‐Guided Deep Learning (PGDL) hybrid modeling framework with a use‐case of predicting depth‐specific lake water temperatures. The PGDL model has three primary components: a deep learning model with temporal awareness (long short‐term memory recurrence), theory‐based feedback (model penalties for violating conversation of energy), and model pretraining to initialize the network with synthetic data (water temperature predictions from a process‐based model). In situ water temperatures were used to train the PGDL model, a deep learning (DL) model, and a process‐based (PB) model. Model performance was evaluated in various conditions, including when training data were sparse and when predictions were made outside of the range in the training data set. The PGDL model performance (as measured by root‐mean‐square error (RMSE)) was superior to DL and PB for two detailed study lakes, but only when pretraining data included greater variability than the training period. The PGDL model also performed well when extended to 68 lakes, with a median RMSE of 1.65 °C during the test period (DL: 1.78 °C, PB: 2.03 °C; in a small number of lakes PB or DL models were more accurate). This case‐study demonstrates that integrating scientific knowledge into deep learning tools shows promise for improving predictions of many important environmental variables.