Artificial intelligence applications in coastal engineering and its challenges – A review

Abstract

In the late 1980s and early 1990s, the application of Artificial Intelligence (AI) algorithms in coastal engineering began to develop, offering promising solutions to the complexity and variability of coastal environments. While traditional approaches struggle to keep up with the complexity of tackling coastal problems, particularly considering the growing concerns about climate change. In the face of climate change and other increasing natural and manmade concomitances, coastal areas contend with challenges like sea-level rise, sediment transport, water quality problems, coral reef degradation, and other coastal characteristics such as wind and tide. The paper delves into these complexities and how AI presented different techniques in a trial to enhance the predictive abilities that offer early warning systems, enhancing coastal resilience. This paper also shows Deep Learning (DL) and Machine Learning (ML) algorithms showing the different supervised and unsupervised algorithms such as Linear Regression, Long Short-Term Memory (LSTM), Support Vector Machines (SVM) etc. It also critically examines the AI-coastal engineering integration showing its drawback such as the need of more data and its limited transferability. This work also provides an overview of the current landscape of its application in coastal systems enhancing its cost-effectiveness and sustainability and considering ethical and privacy concerns.