Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2021WR029579 |
Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning | |
Jared D. Willard; Jordan S. Read; Alison P. Appling; Samantha K. Oliver; Xiaowei Jia; Vipin Kumar | |
2021-06-16 | |
发表期刊 | Water Resources Research |
出版年 | 2021 |
英文摘要 | 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 modeling (PB) 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 process-based General Lake Model, where the median 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. This article is protected by copyright. All rights reserved. |
领域 | 资源环境 |
URL | 查看原文 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/330690 |
专题 | 资源环境科学 |
推荐引用方式 GB/T 7714 | Jared D. Willard,Jordan S. Read,Alison P. Appling,et al. Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning[J]. Water Resources Research,2021. |
APA | Jared D. Willard,Jordan S. Read,Alison P. Appling,Samantha K. Oliver,Xiaowei Jia,&Vipin Kumar.(2021).Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning.Water Resources Research. |
MLA | Jared D. Willard,et al."Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning".Water Resources Research (2021). |
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