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The rapid and accurate forecasting of MHWs is crucial for preventing and responding to the impacts they can lead to. However, the research on relevant forecasting methods is limited, and a dedicated forecasting system specifically tailored for the South China Sea (SCS) region has yet to be reported. This study proposes a novel forecasting system utilizing U-Net and ConvLSTM models to predict MHWs in the SCS. Specifically, the U-Net model is used to forecast the intensity of MHWs, while the ConvLSTM model is employed to predict the probability of their occurrence. The indication of an MHW relies on both the intensity forecasted by the U-Net model exceeding threshold T and the occurrence probability predicted by the ConvLSTM model surpassing threshold P. Incorporating sensitivity analysis, optimal thresholds for T are determined as 0.9 \u00b0C, 0.8 \u00b0C, 1.0 \u00b0C, and 1.0 \u00b0C for 1-, 3-, 5-, and 7-day forecast lead times, respectively. Similarly, optimal thresholds for P are identified as 0.29, 0.30, 0.20, and 0.28. Employing these thresholds yields the highest forecast accuracy rates of 0.92, 0.89, 0.88, and 0.87 for the corresponding forecast lead times. This innovative approach gives better predictions of MHWs in the SCS, providing invaluable reference information for marine management authorities to make well-informed decisions and issue timely MHW warnings.<\/jats:p>","DOI":"10.3390\/rs15164068","type":"journal-article","created":{"date-parts":[[2023,8,17]],"date-time":"2023-08-17T10:42:29Z","timestamp":1692268949000},"page":"4068","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Artificial Intelligence Forecasting of Marine Heatwaves in the South China Sea Using a Combined U-Net and ConvLSTM System"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7426-3175","authenticated-orcid":false,"given":"Wenjin","family":"Sun","sequence":"first","affiliation":[{"name":"School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China"},{"name":"State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310000, China"},{"name":"GEOMAR Helmholtz Centre for Ocean Research Kiel, 24105 Kiel, Germany"}]},{"given":"Shuyi","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7514-3212","authenticated-orcid":false,"given":"Jingsong","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China"},{"name":"State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310000, China"}]},{"given":"Xiaoqian","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China"}]},{"given":"Jinlin","family":"Ji","sequence":"additional","affiliation":[{"name":"School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China"}]},{"given":"Changming","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,17]]},"reference":[{"key":"ref_1","unstructured":"Pearce, A., Lenanton, R., Jackson, G., Moore, J., Feng, M., and Gaughan, D. 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