Machine Learning in Coastal Engineering: Applications, Challenges, and Perspectives

Abstract

The integration of machine learning (ML) techniques in coastal engineering marks a paradigm shift in how coastal processes are modeled and understood. While traditional empirical and numerical models have been stalwarts in simulating coastal phenomena, the burgeoning complexity and computational demands have paved the way for data-driven approaches to take center stage. This review underscores the increasing preference for ML methods in coastal engineering, particularly in predictive tasks like wave pattern prediction, water level fluctuation, and morphology change. Although the scope of this review is not exhaustive, it aims to spotlight recent advancements and the capacity of ML techniques to harness vast datasets for more efficient and cost-effective simulations of coastal dynamics. However, challenges persist, including issues related to data availability and quality, algorithm selection, and model generalization. This entails addressing fundamental questions about data quantity and quality, determining optimal methodologies for specific problems, and refining techniques for model training and validation. The reviewed literature paints a promising picture of a future where ML not only complements but significantly enhances our ability to predict and manage the intricate dynamics of coastal environments.

Simulating Future Exposure to Coastal Urban Flooding Using a Neural Network–Markov Model

“…According to the literature, most urban growth modeling studies focus on estimating the potential impacts of urban expansion and determining the best direction for future urban growth [8,[28][29][30][31][32][33][34]. While the effects of climate change on future urban expansion have received little attention, only a few studies have looked at how climate change, especially sea level rise, affects future urban development.…”