AI-POWERED SHIP-BASED OCEAN WAVE MODELING FOR ENHANCING THE STABILITY OF COASTAL AND MANGROVE FOREST ECOSYSTEMS USING WBA SENSOR NETWORKS

Abstract

     Achieving sustainable operations of maritime structures hinges upon the precise real-time estimation of ocean wave characteristics tailored to specific geographic resolutions. This research delves into the realm of machine learning, presenting a promising avenue for fortifying sensor networks by amalgamating oceanic data procured from diverse sources. The study proposes an innovative approach that integrates ship-based data with a wave-now casting system, capitalizing on the ship-as-buoy concept. In the quest for alternative models, a meticulously calibrated near shore wave model, rooted in physics, is harnessed. This model not only captures the intricate interplay of oceanic forces but also imparts a comprehensive understanding of the spatial relationships that govern the domain's grid points. The integration of this model within the computational framework forms the cornerstone for enhancing the predictive capabilities of the system. Central to this investigation is the evaluation of wave parameters gleaned from spectral analysis of ship movements. These parameters serve as potential inputs for the surrogate system, either supplementing or replacing the conventional reliance on wave buoy data. Through an exhaustive case study, the performance of the novel approach is rigorously assessed, juxtaposing it with established methodologies. This comparative analysis systematically uncovers the strengths and limitations of incorporating ship-based wave predictions to enhance the accuracy and accessibility of local sea state information. By fusing the realms of ship-based observations, advanced wave modelling, and machine learning, this research contributes to a holistic framework for refining maritime operations. The outcomes of this study provide valuable insights that pave the way for optimizing maritime structures' operational strategies, bolstering sustainability, and ensuring safer navigation through more informed decision-making processes.