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

• Type: Another Publication in an International Scientific Journal
• Date: 2024-04

Authors

Mahmoud Abouhalima (Author) Other; Luciana das Neves (Author) FEUP; Francisco Taveira Pinto (Author) FEUP; Paulo Rosa Santos (Author) FEUP

Journal

Title: Journal of Marine Science and Engineering

Vol. 12

Pages: 1-638

Publisher: MDPI

Indexing

ISI Web of Knowledge - 0 Citations

ISI Web of Science

Scopus - 0 Citations

Scientific classification

CORDIS: Technological sciences

FOS: Engineering and technology

Other information

Authenticus ID: P-010-CV1

DOI: 10.3390/jmse12040638

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 31