A Comprehensive Review of AI and ML Applications in Coastal Engineering

This manuscript reviews the current state and advancements in applying Artificial Intelligence (AI) within coastal engineering. It encapsulates some journal articles, emphasizing the role of Machine Learning (ML) algorithms in fostering sustainable management of marine and coastal environments. The review delves into various aspects of ML applications, including data preprocessing, modeling algorithms for distinct phenomena, model evaluation, and utilizing dynamic and integrated models. The study highlights the significance of coastal areas, underlining their role in biodiversity, economic activity, cultural heritage, climate regulation, food security, recreational opportunities, and strategic importance. It addresses the challenges in ensuring the sustainability of these areas, particularly in the face of climate change, beach protection, and water quality management. The manuscript underscores the necessity of effective data analysis and augmentation for informed decision-making and sustainable management of coastal systems, noting the recent surge in data availability related to coastal systems. Furthermore, the review examines the efficacy of different ML methods in predicting wave heights and other oceanographic parameters. It identifies areas where ML has been successfully applied, such as data collection and analysis, pollutant and sediment transport, image processing and deep learning, and identifying potential regions for aquaculture and wave energy activities. Additionally, it discusses the application of ML in structural design and optimization. Despite the potential benefits, the manuscript notes that dynamic and integrated ML models are still underutilized in marine projects. It concludes with insights into the application of ML in marine and coastal sustainability and calls for future research to explore the untapped potential of ML in this domain.