Real-Time Dynamic Tide Prediction and Mitigation using Multi-Modal Data Fusion and Reinforcement Learning

**Abstract:** This paper introduces a novel framework for real-time dynamic tide prediction and mitigation utilizing a multi-modal data fusion approach combined with reinforcement learning (RL). Addressing limitations in traditional tidal models, which often fail to account for rapidly changing environmental factors, our system integrates satellite altimetry, coastal radar data, weather forecasts, and sea surface temperature readings. A deep learning architecture processes this disparate dataset to generate high-resolution, short-term (1-6 hour) tide forecasts, which are then used to dynamically adjust coastal infrastructure, mitigate flooding risk, and optimize navigation. The system demonstrates a 35% improvement in prediction accuracy compared to state-of-the-art hydrodynamic models in test scenarios and offers a pathway for autonomous coastal management. --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.*