44 citations as recorded by crossref.

1. Machine learning techniques for marine heatwave prediction: a comprehensive review S. Welandawe et al. https://doi.org/10.1007/s44295-025-00076-1
2. Skillful subseasonal Indian Ocean marine heatwave forecasts using a neural network L. Howard et al. https://doi.org/10.1017/eds.2026.10033
3. Promoting best practices in ocean forecasting through an Operational Readiness Level E. Alvarez Fanjul et al. https://doi.org/10.3389/fmars.2024.1443284
4. Two-phase CNN for model data fusion: Predicting 3D chlorophyll-a in the Mediterranean Sea T. Tonelli et al. https://doi.org/10.1016/j.ocemod.2026.102707
5. Unveiling summer marine heatwave onset mechanisms in the South China sea using an explainable deep learning method M. Guo et al. https://doi.org/10.1007/s00382-025-07817-2
6. Resilient Anomaly Detection in Ocean Drifters with Unsupervised Learning, Deep Learning Models, and Energy-Efficient Recovery C. Guo et al. https://doi.org/10.3390/oceans7010005
7. Marine heatwaves disrupt germination and seedling physiology in Zostera marina R. Pieraccini et al. https://doi.org/10.1016/j.marenvres.2025.107789
8. A Dual-Branch Encoder–Decoder Network with Convolutional Long Short-Term Memory and Efficient Channel Attention for Forecasting Summer Marine Heatwaves in the East China Sea Y. Zhang et al. https://doi.org/10.3390/jmse14151421
9. Estimating the importance of environmental factors influencing the urban heat island for urban areas in Greece. A machine learning approach I. Petrou & P. Kassomenos https://doi.org/10.1016/j.jenvman.2024.122255
10. Physically constrained air–sea heat flux driven modeling of sea surface temperature using atmospheric and oceanographic variables with explainable AI O. Katipoğlu et al. https://doi.org/10.1016/j.jer.2026.01.002
11. Artificial intelligence, capability transformation, and marine green development: empirical evidence from Coastal China D. Li et al. https://doi.org/10.3389/fmars.2026.1816756
12. An Informer-based prediction model for extensive spatiotemporal prediction of sea surface temperature and marine heatwave in Bohai Sea J. He et al. https://doi.org/10.1016/j.jmarsys.2024.104037
13. TL-iTransformer: Revolutionizing sea surface temperature prediction through iTransformer and transfer learning W. Jia et al. https://doi.org/10.1007/s12145-024-01436-x
14. A Diffusion Weighted Ensemble Framework for Robust Short-Horizon Global SST Forecasting from Multivariate GODAS Data G. Yu et al. https://doi.org/10.3390/math14040740
15. Multi-Dilated Convolutional LSTM With U-Net for Global Sea Surface Temperature Forecasting M. Janmaijaya et al. https://doi.org/10.1109/ACCESS.2024.3486914
16. Review article: Harnessing data-driven methods for climate multi-hazard and multi-risk assessment D. Ferrario et al. https://doi.org/10.5194/nhess-26-2975-2026
17. Predicting Tomorrow: A Review of Machine Learning’s Role in Shaping Environmental Forecasts S. Ferebee https://doi.org/10.70389/PJS.100008
18. OceanMind: Heterogeneity-Aware Spatiotemporal Learning for Sea Surface Temperature Forecasting S. Yang et al. https://doi.org/10.1109/TGRS.2026.3711072
19. A Machine Learning-Based Bias Correction Scheme for the All-Sky Assimilation of AGRI Infrared Radiances in a Regional OSSE Framework X. Zhang et al. https://doi.org/10.1109/TGRS.2024.3427434
20. Best practices for AI-based image analysis applications in aquatic sciences: The iMagine case study E. Azmi et al. https://doi.org/10.1016/j.ecoinf.2025.103306
21. Design and experimental evaluation of a high-accuracy air temperature measurement instrument for meteorological applications K. Yuan et al. https://doi.org/10.1063/5.0313395
22. A multi-resolution diffusion network for sea surface temperature anomaly detection P. Yang et al. https://doi.org/10.1007/s44443-026-00882-5
23. Subsurface marine heatwaves: challenging to detect at the surface yet critically important S. Hu https://doi.org/10.1007/s00343-025-5225-7
24. A Lightweight Field-to-Site Coupled Framework for 15-Day Sea Surface Temperature Forecasting in Marine Ranching Areas: A Case Study in the Northern Yellow Sea B. Zhao et al. https://doi.org/10.3390/rs18142374
25. Decentralized control strategies with predictive disturbance rejection for OC-OTEC plant in Lakshadweep using deep learning S. Sutha et al. https://doi.org/10.1016/j.jwpe.2024.105539
26. Breaking the Heat Code with Transparent AI Models for Predicting Extreme Temperatures I. Muraina et al. https://doi.org/10.37394/23205.2025.24.25
27. Multi-horizon forecasting of coastal high water temperature events for operational marine-heatwave early warning in South Korean aquaculture: a walk-forward benchmark of eleven statistical and deep-learning models Y. Kang et al. https://doi.org/10.3389/fmars.2026.1887350
28. Improved coral bleaching prediction using downscaled degree heating weeks derived from integrated thermal remote sensing products across multiple spatiotemporal scales H. Mizuochi et al. https://doi.org/10.7717/peerj.21269
29. From climate data to regulatory decisions: integrating climate AI into marine EIAs X. Gong & L. Cheng https://doi.org/10.3389/fmars.2026.1870082
30. An in-depth investigation of global sea surface temperature behavior utilizing chaotic modeling M. Minaei et al. https://doi.org/10.1007/s11356-024-33790-0
31. Trends and variability of marine heatwaves in Portuguese coastal waters M. Monteiro et al. https://doi.org/10.1016/j.scitotenv.2025.179161
32. Extreme marine heatwave linked to mass fish kill in the Red Sea M. Tietbohl et al. https://doi.org/10.1016/j.scitotenv.2025.179073
33. Mediterranean summer marine heatwaves triggered by weaker winds under subtropical ridges G. Bonino et al. https://doi.org/10.1038/s41561-025-01762-9
34. Mapping anchovy species distribution and identifying potential fishing grounds in the Gulf of Thailand . Chatthong, S. et al. https://doi.org/10.63369/ijat.2026.22.1.71-88
35. What makes a marine heatwave forecast useable, useful and used? C. Spillman et al. https://doi.org/10.1016/j.pocean.2025.103464
36. MS-SSTNet: A Scale-Aware Spatiotemporal Learning Framework for Satellite SST Forecasting via Iterative Multiscale Decomposition and Dual-Window Modelling G. Hou et al. https://doi.org/10.3390/rs18162803
37. Improving marine heatwave statistics in global climate models using machine learning: a case study for the north–west European Shelf J. Becherer & T. Pohlmann https://doi.org/10.1007/s00382-026-08127-x
38. Marine heatwaves in the Mediterranean Sea: a convolutional neural network study for extreme event prediction A. Parasyris et al. https://doi.org/10.5194/os-21-897-2025
39. PISEM: A Physics-Informed Spatiotemporal Encoding Model for Sea Surface Temperature Prediction J. Wang et al. https://doi.org/10.1109/JSTARS.2026.3705088
40. Marine Heatwaves and NAO-Related Ocean–Atmosphere Variability in the North Atlantic B. Lopes et al. https://doi.org/10.3390/rs18142363
41. Factors Influencing Endangered Marine Species in the Mediterranean Sea: An Analysis Based on IUCN Red List Criteria Using Statistical and Soft Computing Methodologies D. Klaoudatos et al. https://doi.org/10.3390/environments11070151
42. The elusive spinetail devil ray (Mobula mobular): a multimethod approach to track its presence in the Spanish Mediterranean Sea P. Carrasco‐Puig et al. https://doi.org/10.1111/jfb.70241
43. Marine Heatwaves and Cold Spells in Global Coral Reef Regions (1982–2070): Characteristics, Drivers, and Impacts H. Jiang et al. https://doi.org/10.3390/rs17162881
44. Separating Sensor-like Anomalies from Regional Oceanographic Events: A Machine-Learning-Assisted, Physics-Guided, Event-Preserving Quality-Control Framework for Coastal Buoy Temperature Records H. Joo et al. https://doi.org/10.3390/jmse14161462