Use of artificial intelligence and other data-driven technologies for coastal management
DOI:
https://doi.org/10.33175/mtr.2027.289835Keywords:
Artificial intelligence; Coastal management; Deep learning; Machine learningAbstract
Biogeochemical cycles occur naturally in coastal regions. Estuaries, wetlands, and tidal creeks assist these cycles. Since increased anthropogenic activities and naturally occurring events act as stressors, they impact physical, chemical, and biological processes in maritime spaces and hence these cycles. This makes existing ecosystems susceptible to trophic imbalance. Resultantly, researchers began monitoring and mapping coastal environments to understand how different stressors impact these biogeochemical processes using numerical and empirical models. With advances in computation infrastructure, use of data-driven techniques such as Artificial Intelligence, Machine Learning, and Deep Learning in different hydrological systems have become commonplace. This has invigorated the use of predictive and decision-making frameworks in coastal management, making habitat conservation more scientific. Such frameworks balance ecological health and human activities through hard and soft engineering, nature-based solutions, and managed retreat. Though substantial literature on the use of Artificial Intelligence and Machine Learning in the coastal environment exists, they focus on independent domains, with limited discussion of their combined impact on coastal management. Accordingly, this desk-based qualitative review discusses recent developments in Artificial Intelligence, Machine Learning, Deep Learning, and other data-driven techniques used for coastal management. In addition, it will identify future directions for such studies, especially with combined stressors.
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Cite this article:
APA Style:
Agarwala, N. (2027). Use of artificial intelligence and other data-driven technologies for coastal management. Maritime Technology and Research, 9(1), 289835. https://doi.org/10.33175/mtr.2027.289835
MDPI Style:
Agarwala, N. Use of artificial intelligence and other data-driven technologies for coastal management. Marit. Technol. Res. 2027, 9, 289835. https://doi.org/10.33175/mtr.2027.289835
Vancouver Style:
Agarwala N. (2027). Use of artificial intelligence and other data-driven technologies for coastal management. Marit. Technol. Res. 9(1):289835. https://doi.org/10.33175/mtr.2027.289835
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Highlights
- Provides an overview tying disparate AI/ML to cohesive coastal management frameworks
- Shows how coastal modeling has transitioned from traditional linear systems to modern multi-physics data-driven frameworks
- Shows how PINNs addresses AI limitations like hallucinations and physical anomalies
- Shows how AI optimizes hard gray infrastructure while scaling up soft green solutions
- Showcases how Decision Support Systems use spatial-temporal algorithms to balance ecosystem preservation with economic priorities
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