{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T17:50:36Z","timestamp":1785952236397,"version":"3.56.0"},"reference-count":65,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T00:00:00Z","timestamp":1653955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Michigan Tech Hydrodynamic Modeling Research Initiative","award":["NA17OAR4320152"],"award-info":[{"award-number":["NA17OAR4320152"]}]},{"name":"National Oceanic and Atmospheric Administration (NOAA) Office of Oceanic and Atmospheric Research (OAR)","award":["NA17OAR4320152"],"award-info":[{"award-number":["NA17OAR4320152"]}]},{"DOI":"10.13039\/100000192","name":"University of Michigan Cooperative Institute for Great Lakes Research (CIGLR)","doi-asserted-by":"publisher","award":["NA17OAR4320152"],"award-info":[{"award-number":["NA17OAR4320152"]}],"id":[{"id":"10.13039\/100000192","id-type":"DOI","asserted-by":"publisher"}]},{"name":"U.S. Department of Energy","award":["NA17OAR4320152"],"award-info":[{"award-number":["NA17OAR4320152"]}]},{"name":"Office of Science","award":["NA17OAR4320152"],"award-info":[{"award-number":["NA17OAR4320152"]}]},{"name":"Office of Biological and Environmental Research","award":["NA17OAR4320152"],"award-info":[{"award-number":["NA17OAR4320152"]}]},{"name":"Earth and Environmental Systems Modeling program","award":["NA17OAR4320152"],"award-info":[{"award-number":["NA17OAR4320152"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The Laurentian Great Lakes, one of the world\u2019s 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.<\/jats:p>","DOI":"10.3390\/rs14112640","type":"journal-article","created":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T21:43:42Z","timestamp":1654119822000},"page":"2640","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Integrating Deep Learning and Hydrodynamic Modeling to Improve the Great Lakes Forecast"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5702-421X","authenticated-orcid":false,"given":"Pengfei","family":"Xue","sequence":"first","affiliation":[{"name":"Department of Civil, Environmental and Geospatial Engineering, Michigan Technological University, Houghton, MI 49931, USA"},{"name":"Great Lakes Research Center, Michigan Technological University, Houghton, MI 49931, USA"},{"name":"Argonne National Laboratory, Environmental Science Division, Lemont, IL 60439, USA"},{"name":"Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aditya","family":"Wagh","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering-Engineering Mechanics, Michigan Technological University, Houghton, MI 49931, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gangfeng","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Old Dominion University, Norfolk, VA 23529, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yilin","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Michigan Technological University, Houghton, MI 49931, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongchao","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering-Engineering Mechanics, Michigan Technological University, Houghton, MI 49931, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5917-8303","authenticated-orcid":false,"given":"Tao","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI 49931, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenfu","family":"Huang","sequence":"additional","affiliation":[{"name":"Great Lakes Research Center, Michigan Technological University, Houghton, MI 49931, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,31]]},"reference":[{"key":"ref_1","unstructured":"U.S.EPA (2014). 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