Predicting Water Temperature Dynamics of Unmonitored Lakes With Meta‐Transfer Learning
Predicting Water Temperature Dynamics of Unmonitored Lakes With Meta‐Transfer Learning
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DOI：
10.1029/2021wr029579
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发表时间：
2020-11
影响因子：
5.4
通讯作者：
J. Willard;J. Read;A. Appling;S. Oliver;X. Jia;Vipin Kumar
中科院分区：
文献类型：
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作者：
J. Willard;J. Read;A. Appling;S. Oliver;X. Jia;Vipin Kumar
大多数环境数据来自少数受到良好监测的地点。环境科学中的一个持续挑战是将知识从受监测地点转移到未受监测地点。在这里，我们展示了一种新的迁移学习框架，它通过借用监控良好的湖泊(源)的模型来准确预测未监控湖泊(目标)的深度特定温度。这种元迁移学习方法(MTL)建立了一个元学习模型，利用湖水属性和候选者的过去表现来预测从候选源模型到目标模型的迁移性能。我们使用校准的基于过程的(PB)建模和最近发展的称为过程引导的深度学习(PGDL)的方法，在145个监测良好的湖泊中构建了源模型。我们将MTL应用于PB或PGDL源模型(分别为PB-MTL或PGDL-MTL)，以预测美国中西部上部305个被视为未监测的目标湖泊的温度。与未校准的PB通用湖泊模型相比，我们的性能有了显著的改善，其中目标湖泊的中位数均方误差(RMSE)为2.52°C；PB-MTL的中位数均方误差(RMSE)为2.43°C；PGDL-MTL的中位数RMSE为2.16°C；而每个目标九个源的PGDL-MTL集成的RMSE为1.88°C。对于稀疏监测的目标湖泊，PGDL-MTL的表现通常优于针对目标湖泊本身训练的PGDL模型。震源和目标之间的最大深度差异始终是最重要的预测因素。我们的方法很容易扩展到美国中西部的数千个湖泊，表明MTL具有有意义的预测变量和高质量的源模型，对于许多类型的未监测系统和环境变量是一种有前途的方法。
Most environmental data come from a minority of well‐monitored sites. An ongoing challenge in the environmental sciences is transferring knowledge from monitored sites to unmonitored sites. Here, we demonstrate a novel transfer‐learning framework that accurately predicts depth‐specific temperature in unmonitored lakes (targets) by borrowing models from well‐monitored lakes (sources). This method, meta‐transfer learning (MTL), builds a meta‐learning model to predict transfer performance from candidate source models to targets using lake attributes and candidates' past performance. We constructed source models at 145 well‐monitored lakes using calibrated process‐based (PB) modeling and a recently developed approach called process‐guided deep learning (PGDL). We applied MTL to either PB or PGDL source models (PB‐MTL or PGDL‐MTL, respectively) to predict temperatures in 305 target lakes treated as unmonitored in the Upper Midwestern United States. We show significantly improved performance relative to the uncalibrated PB General Lake Model, where the median root mean squared error (RMSE) for the target lakes is 2.52°C. PB‐MTL yielded a median RMSE of 2.43°C; PGDL‐MTL yielded 2.16°C; and a PGDL‐MTL ensemble of nine sources per target yielded 1.88°C. For sparsely monitored target lakes, PGDL‐MTL often outperformed PGDL models trained on the target lakes themselves. Differences in maximum depth between the source and target were consistently the most important predictors. Our approach readily scales to thousands of lakes in the Midwestern United States, demonstrating that MTL with meaningful predictor variables and high‐quality source models is a promising approach for many kinds of unmonitored systems and environmental variables.