Open AccessArticle
Early Warning for Maritime Storm Formation Using Temporal Autoencoder-Based Anomaly Detection
1 University of Massachusetts Dartmouth, Department of Computer and Information Science, Dartmouth, MA 02747, USA
2 University of Massachusetts Dartmouth, Department of Mathematics, Dartmouth, MA 02747, USA
*whom correspondence should be addressed. E-mail: hxu@umassd.edu
Received: 2 April 2026, Revised: 20 May 2026, Accepted: 22 May 2026, Published Online: 31 May 2026
(This article belongs to the Special Issue on SP9 (Special Issue on Multidisciplinary Sciences & Advanced Technology (SI-MSAT 2026)) and the Section Artificial Intelligence – Computer Science (AIC))
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APA Style
Srivastava, S. , Xu, H. , Yan, D. and Balasubramanian, R. (2026). Early Warning for Maritime Storm Formation Using Temporal Autoencoder-Based Anomaly Detection. Journal of Engineering Research and Sciences, 5(5), 19–39. https://doi.org/10.55708/js0505003
Srivastava, S. , Xu, H. , Yan, D. and Balasubramanian, R. (2026). Early Warning for Maritime Storm Formation Using Temporal Autoencoder-Based Anomaly Detection. Journal of Engineering Research and Sciences, 5(5), 19–39. https://doi.org/10.55708/js0505003
Chicago/Turabian Style
Snehashish Srivastava, Haiping Xu, Donghui Yan and Ramprasad Balasubramanian. "Early Warning for Maritime Storm Formation Using Temporal Autoencoder-Based Anomaly Detection." Journal of Engineering Research and Sciences 5, no. 5 (May 2026): 19–39. https://doi.org/10.55708/js0505003
Snehashish Srivastava, Haiping Xu, Donghui Yan and Ramprasad Balasubramanian. "Early Warning for Maritime Storm Formation Using Temporal Autoencoder-Based Anomaly Detection." Journal of Engineering Research and Sciences 5, no. 5 (May 2026): 19–39. https://doi.org/10.55708/js0505003
IEEE Style
S. Srivastava, H. Xu, D. Yan and R. Balasubramanian, "Early Warning for Maritime Storm Formation Using Temporal Autoencoder-Based Anomaly Detection," Journal of Engineering Research and Sciences, vol. 5, no. 5, pp. 19–39, May. 2026, doi: 10.55708/js0505003.
S. Srivastava, H. Xu, D. Yan and R. Balasubramanian, "Early Warning for Maritime Storm Formation Using Temporal Autoencoder-Based Anomaly Detection," Journal of Engineering Research and Sciences, vol. 5, no. 5, pp. 19–39, May. 2026, doi: 10.55708/js0505003.
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Abstract
Storms remain a serious hazard at sea, exposing vessels to rapidly changing conditions that endanger human life and result in substantial economic losses. Satellite-based detection methods are widely used but require significant computational resources and depend on land-to-sea communication links, which may become unreliable during severe weather. Machine learning approaches offer strong potential for early detection; however, they typically rely on large labeled datasets that are often unavailable for rare or rapidly developing storms. Moreover, many existing models depend on predefined storm signatures, limiting their ability to adapt to previously unseen conditions in real time. To address these challenges, we propose a sensor-based framework for early detection of non-tropical maritime storms using key meteorological variables, including atmospheric pressure, humidity, wind speed, sea surface temperature, and near-surface air temperature. These variables can be measured directly by onboard sensors, enabling continuous monitoring without reliance on external communication infrastructure. The proposed method employs a temporal autoencoder trained exclusively on storm-free data to learn normal atmospheric patterns and detect anomalies associated with storm development. By identifying deviations from normal temporal behavior, the framework provides early warnings before storms fully develop. Comprehensive case studies using both synthetic and real-world meteorological data demonstrate that the proposed approach can detect developing storms in advance, achieving average lead times exceeding one hour while maintaining strong detection performance. These results highlight the potential of the framework as a practical and reliable solution for enhancing maritime safety.
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