Machine learning application in modelling marine and coastal phenomena: a critical review
Ali Pourzangbar, Mahdi Jalali, Maurizio Brocchini
Abstract
This study provides an extensive review of over 200 journal papers focusing on Machine Learning (ML) algorithms’ use for promoting a sustainable management of the marine and coastal environments. The research covers various facets of ML algorithms, including data preprocessing and handling, modeling algorithms for distinct phenomena, model evaluation, and use of dynamic and integrated models. Given that machine learning modeling relies on experience or trial-and-error, examining previous applications in marine and coastal modeling is proven to be beneficial. The performance of different ML methods used to predict wave heights was analyzed to ascertain which method was superior with various datasets. The analysis of these papers revealed that properly developed ML methods could successfully be applied to multiple aspects. Areas of application include data collection and analysis, pollutant and sediment transport, image processing and deep learning, and identification of potential regions for aquaculture and wave energy activities. Additionally, ML methods aid in structural design and optimization and in the prediction and classification of oceanographic parameters. However, despite their potential advantages, dynamic and integrated ML models remain underutilized in marine projects. This research provides insights into ML’s application and invites future investigations to exploit ML’s untapped potential in marine and coastal sustainability.