Predicting Water Temperature Dynamics of Unmonitored Lakes With Meta‐Transfer Learning. Issue 7 (29th June 2021)

• Record Type: Journal Article
• Authors: Willard, Jared D.; Read, Jordan S.; Appling, Alison P.; Oliver, Samantha K.; Jia, Xiaowei; Kumar, Vipin
• Is Part Of: Water resources research. Volume 57:Issue 7(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-06-29
• Subjects: lake temperature -- meta learning -- transfer learning -- physics‐guided deep learning -- machine learning -- water resources; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2021WR029579
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: 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. Key Points: Meta‐transfer learning (MTL) learns from models trained on data‐rich systems to inform predictions in systems where no observations exist We use MTL with process‐based and process‐guided deep learning models to accurately predict lake temperatures in the Midwest United States The most important predictor of transfer model success is the difference in maximum depth between the data‐rich and unmonitored lake