ABSTRACT
Climate change and its impacts, combined with unchecked human activities, intensify pressures on coastal environments, resulting in modification of the coastal morphodynamics. Coastal zones are intricate and constantly changing areas, making the monitoring and interpretation of data a challenging task, especially in remote beaches and regions with limited historical data. Traditionally, remote sensing and numerical methods have played a vital role in analysing earth observation data and supporting the monitoring and modelling of complex coastal ecosystems. However, the emergence of artificial intelligence-based techniques has shown promising results, offering the additional advantage of filling data gaps, predicting data in data-scarce regions, and analysing multidimensional datasets collected over extended periods of time and larger spatial scales. The main objective of this study is to provide a comprehensive review of the existing literature, discussing both traditional methods and various emerging artificial intelligence-based approaches used in studying the coastal dynamics, shoreline change analysis, and coastal monitoring. Ultimately, the study proposes a climate resilience framework to enhance coastal zone management practices and policies, fostering resilience among coastal communities. The outcome of this study aligns with and supports particularly SDG 13 of the UN (Climate Action) and advances it by identifying relevant methods in coastal erosion studies and proposing integrated management plans informed by real-time data collection and analysis/modelling using physics-based models.
