Document Type
Article
Publication Date
5-31-2022
Department
Department of Civil, Environmental, and Geospatial Engineering; Great Lakes Research Center; Department of Mechanical Engineering-Engineering Mechanics; Department of Computer Science; College of Forest Resources and Environmental Science
Abstract
The Laurentian Great Lakes, one of the world’s largest surface freshwater systems, pose a modeling challenge in seasonal forecast and climate projection. While physics-based hydrodynamic modeling is a fundamental approach, improving the forecast accuracy remains critical. In recent years, machine learning (ML) has quickly emerged in geoscience applications, but its application to the Great Lakes hydrodynamic prediction is still in its early stages. This work is the first one to explore a deep learning approach to predicting spatiotemporal distributions of the lake surface temperature (LST) in the Great Lakes. Our study shows that the Long Short-Term Memory (LSTM) neural network, trained with the limited data from hypothetical monitoring networks, can provide consistent and robust performance. The LSTM prediction captured the LST spatiotemporal variabilities across the five Great Lakes well, suggesting an effective and efficient way for monitoring network design in assisting the ML-based forecast. Furthermore, we employed an explainable artificial intelligence (XAI) technique named SHapley Additive exPlanations (SHAP) to uncover how the features impact the LSTM prediction. Our XAI analysis shows air temperature is the most influential feature for predicting LST in the trained LSTM. The relatively large bias in the LSTM prediction during the spring and fall was associated with substantial heterogeneity of air temperature during the two seasons. In contrast, the physics-based hydrodynamic model performed better in spring and fall yet exhibited relatively large biases during the summer stratification period. Finally, we developed a statistical integration of the hydrodynamic modeling and deep learning results based on the Best Linear Unbiased Estimator (BLUE). The integration further enhanced prediction accuracy, suggesting its potential for next-generation Great Lakes forecast systems.
Publication Title
Remote Sensing
Recommended Citation
Xue, P.,
Wagh, A.,
Ma, G.,
Wang, Y.,
Yang, Y.,
Liu, T.,
&
Huang, C.
(2022).
Integrating Deep Learning and Hydrodynamic Modeling to Improve the Great Lakes Forecast.
Remote Sensing,
14(11).
http://doi.org/10.3390/rs14112640
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/16224
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Version
Publisher's PDF
Included in
Biology Commons, Civil and Environmental Engineering Commons, Computer Sciences Commons, Forest Sciences Commons, Mechanical Engineering Commons
Publisher's Statement
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Publisher’s version of record: https://doi.org/10.3390/math10122026