Applications of Machine Learning and Deep Learning in Coastal Process Modelling
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".
Deadline for manuscript submissions: 20 February 2026 | Viewed by 58
Special Issue Editor
Special Issue Information
Dear Colleagues,
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 have been 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 the current Special Issue 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 Special Issue encompasses interdisciplinary studies focusing on the application of ML techniques in understanding and managing coastal dynamics. Contributions are invited that explore various themes, including but not limited to the following:
- The development of AI-based predictive models for coastal erosion, sediment transport, shoreline evolution, sea level rise, salt intrusion, extreme natural events, and hydrodynamic and morphodynamic processes.
- Explorations of coastline nourishments’ impact and temporal and spatial evolution using ML models.
- Understanding the dynamics surrounding the nature-based solutions (e.g., sand engines, sea grasses, etc.) using ML models.
- Exploring 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.
Submissions of original research articles, reviews, case studies, and perspectives are encouraged, particularly those that offer novel insights, methodologies, and practical solutions for addressing challenges related to coastal dynamics using machine learning approaches.
Dr. Pavitra Kumar
Guest Editor
Manuscript Submission Information
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Keywords
- coastal dynamics
- artificial intelligence
- machine learning
- coastal resilience
- climate change
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