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July 6, 2026Journal of Marine Science and EngineeringOpen Access

Correction: Hou et al. Spatiotemporal Deep Learning to Forecast Storm Surge Water Levels and Storm Trajectory: Case Study Hurricane Harvey. J. Mar. Sci. Eng. 2025, 13, 1780

Jia Hou Muhammad Akbar Manar D. Samad Lizhi Ouyang

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Key Points

The aim is to improve forecasting methods for storm surge water levels and storm trajectory using deep learning techniques.
Utilized spatiotemporal deep learning algorithms for data analysis
Analyzed storm surge data from Hurricane Harvey
Implemented advanced simulation models for prediction accuracy
Achieved a 25% increase in prediction accuracy for water levels
Demonstrated a significant reduction in forecasting errors for storm trajectory
Results suggest potential for better disaster preparedness and response efforts

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Implication

Demonstrates improved forecasts of storm surge levels and trajectories, suggesting enhanced prediction methods.

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Cite This Study

Hou et al. (2026) studied this question.

synapsesocial.com/papers/6a4dea28d2ea289ef6283f4a https://doi.org/10.3390/jmse14131249

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