{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T16:54:11Z","timestamp":1770137651790,"version":"3.49.0"},"reference-count":42,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,6]],"date-time":"2023-02-06T00:00:00Z","timestamp":1675641600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2019YFA0607104"],"award-info":[{"award-number":["2019YFA0607104"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["42130113"],"award-info":[{"award-number":["42130113"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["21JR7RA535"],"award-info":[{"award-number":["21JR7RA535"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Natural Sciences Foundation of China","award":["2019YFA0607104"],"award-info":[{"award-number":["2019YFA0607104"]}]},{"name":"National Natural Sciences Foundation of China","award":["42130113"],"award-info":[{"award-number":["42130113"]}]},{"name":"National Natural Sciences Foundation of China","award":["21JR7RA535"],"award-info":[{"award-number":["21JR7RA535"]}]},{"name":"Natural Science Foundation of Gansu Province of China","award":["2019YFA0607104"],"award-info":[{"award-number":["2019YFA0607104"]}]},{"name":"Natural Science Foundation of Gansu Province of China","award":["42130113"],"award-info":[{"award-number":["42130113"]}]},{"name":"Natural Science Foundation of Gansu Province of China","award":["21JR7RA535"],"award-info":[{"award-number":["21JR7RA535"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>As one of the critical indicators of the lake ecosystem, the lake surface water temperature is an important indicator for measuring lake ecological environment. However, there is a complex nonlinear relationship between lake surface water temperature and climate variables, making it difficult to accurately predict. Fortunately, satellite remote sensing provides a wealth of data to support further improvements in prediction accuracy. In this paper, we construct a new deep learning model for mining the nonlinear dynamics from climate variables to obtain more accurate prediction of lake surface water temperature. The proposed model consists of the variable correlation information module and the temporal correlation information module. The variable correlation information module based on the Self-Attention mechanism extracts key variable features that affect lake surface water temperature. Then, the features are input into the temporal correlation information module based on the Gated Recurrent Unit (GRU) model to learn the temporal variation patterns. The proposed model, called Attention-GRU, is then applied to lake surface water temperature prediction in Qinghai Lake, the largest inland lake located in the Tibetan Plateau region in China. Compared with the seven baseline models, the Attention-GRU model achieved the most accurate prediction results; notably, it significantly outperformed the Air2water model which is the classic model for lake surface water temperature prediction based on the volume-integrated heat balance equation. Finally, we analyzed the factors influencing the surface water temperature of Qinghai Lake. There are different degrees of direct and indirect effects of climatic variables, among which air temperature is the dominant factor.<\/jats:p>","DOI":"10.3390\/rs15040900","type":"journal-article","created":{"date-parts":[[2023,2,7]],"date-time":"2023-02-07T02:56:08Z","timestamp":1675738568000},"page":"900","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["A Novel Deep Learning Model for Mining Nonlinear Dynamics in Lake Surface Water Temperature Prediction"],"prefix":"10.3390","volume":"15","author":[{"given":"Zihan","family":"Hao","sequence":"first","affiliation":[{"name":"College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2288-4751","authenticated-orcid":false,"given":"Weide","family":"Li","sequence":"additional","affiliation":[{"name":"Laboratory of Applied Mathematics and Complex Systems, Center for Data Science, School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2388-3614","authenticated-orcid":false,"given":"Jinran","family":"Wu","sequence":"additional","affiliation":[{"name":"Institute for Learning Sciences & Teacher Education, Australian Catholic University, Brisbane 4001, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2806-2989","authenticated-orcid":false,"given":"Shaotong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Frontiers Science Center for Deep Ocean Multispheres and Earth System, Key Lab of Submarine Geosciences and Prospecting Techniques, MOE and College of Marine Geosciences, Ocean University of China, Qingdao 266100, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1756-434X","authenticated-orcid":false,"given":"Shujuan","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,6]]},"reference":[{"key":"ref_1","first-page":"10","article-title":"Rapid and highly variable warming of lake surface waters around the globe","volume":"42","author":"Sharma","year":"2015","journal-title":"Geophys. 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