About this Research Topic
Coastal areas are highly dynamic environments, constantly subjected to natural and anthropogenic forces such as wave action, tides, sediment transport, and climate change. These processes contribute to shoreline erosion, habitat loss, and an increased vulnerability to natural disasters such as storms and tsunamis. With the growing concerns over the impacts of climate change, there has been a heightened focus on understanding and mitigating the adverse effects on coastal regions. To address these challenges, Artificial Intelligence (AI) and machine learning (ML) techniques are extensively applied, exploring their potential in providing valuable insights into complex coastal systems. AI and ML have the capability to analyze large datasets, predict coastal changes, and facilitate the development of adaptive strategies for sustainable coastal management and protection.
The primary goal of this Research Topic is to leverage the capabilities of ML to better understand and predict the dynamics of coastal systems. One of the key challenges is to develop predictive models that can accurately forecast a wide range of coastal changes, including erosion, sediment transport, shoreline evolution, coastline nourishment strategies, the implementation of nature-based solutions, and the dynamic impacts of climate change. By integrating ML techniques with high-resolution data from remote sensing, satellite imagery, and in-situ sensors, robust predictive models can be created that can simulate various scenarios and assess the potential impacts of climate change and human interventions on coastal regions.
The scope of this Research Topic encompasses interdisciplinary studies focusing on the application of ML techniques in understanding and managing coastal dynamics. We invite contributions that explore various themes, including but not limited to:
• the development of AI-based predictive models for coastal erosion, sediment transport, shoreline evolution
• explore coastline nourishments’ impact and temporal and spatial evolution using ML models
• understand the dynamics surrounding the nature-based solutions (e.g., sand engines, sea grasses, etc.) using ML models
• explore the impact of climate change on coastal resilience using ML models
• the use of ML algorithms for analyzing complex environmental data sets related to coastal systems
• application of AI-driven autonomous sensing technologies for real-time data collection and monitoring of coastal processes.
We encourage submissions of original research articles, reviews, case studies, and perspectives that provide novel insights, methodologies, and practical solutions for addressing the challenges associated with coastal dynamics using ML approaches.
The primary goal of this Research Topic is to leverage the capabilities of ML to better understand and predict the dynamics of coastal systems. One of the key challenges is to develop predictive models that can accurately forecast a wide range of coastal changes, including erosion, sediment transport, shoreline evolution, coastline nourishment strategies, the implementation of nature-based solutions, and the dynamic impacts of climate change. By integrating ML techniques with high-resolution data from remote sensing, satellite imagery, and in-situ sensors, robust predictive models can be created that can simulate various scenarios and assess the potential impacts of climate change and human interventions on coastal regions.
The scope of this Research Topic encompasses interdisciplinary studies focusing on the application of ML techniques in understanding and managing coastal dynamics. We invite contributions that explore various themes, including but not limited to:
• the development of AI-based predictive models for coastal erosion, sediment transport, shoreline evolution
• explore coastline nourishments’ impact and temporal and spatial evolution using ML models
• understand the dynamics surrounding the nature-based solutions (e.g., sand engines, sea grasses, etc.) using ML models
• explore the impact of climate change on coastal resilience using ML models
• the use of ML algorithms for analyzing complex environmental data sets related to coastal systems
• application of AI-driven autonomous sensing technologies for real-time data collection and monitoring of coastal processes.
We encourage submissions of original research articles, reviews, case studies, and perspectives that provide novel insights, methodologies, and practical solutions for addressing the challenges associated with coastal dynamics using ML approaches.
Keywords: Coastal Dynamics, Artificial Intelligence, Machine Learning, Coastal Resilience, Climate Change
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
