Published: Sept. 11, 2023
Language: Английский
Fundamental Research, Journal Year: 2024, Volume and Issue: unknown
Published: June 1, 2024
The coastal zone represents a critical intersection of naturally ecological and socio-economic processes. abundance data, models, knowledge derived from various sources in zones facilitates us to integrate them better understand the evolution environments. This paper proposes comprehensive framework Coastal Zone Information Model (CZIM) multi-domain information. core idea CZIM is multi-discipline for standardized governance, so as carry, express, apply information by digital system approaching twin. includes four aspects: data model integration, engineering, construction. We perform detailed literature review illustrate demands challenges related those four. components each aspect their interlinks are introduced subsequently, future constructing twins relying on discussed. aims strengthen ability organize, manage refined support more efficient, scientific, intelligent decision-making response gradually volatile forces both human activities natural events, now future. provides valuable reference next generation digitization target
Language: Английский
Citations
2
Published: April 29, 2024
Abstract Machine learning techniques offer the potential to revolutionize provision of metocean forecasts critical safe and successful operation offshore infrastructure, leveraging asset-level accuracy point-based observations in conjunction with benefits extended coverage (both temporally spatially) numerical modelling satellite remote sensing data. Here, we adapt apply a promising framework – originally proposed by present authors for prediction wave conditions on European North West Shelf waters Gulf Mexico. The approach consists using an attention-based long short-term memory recurrent neural network learn temporal patterns from available buoy observations, that is then combined random forest based spatial nowcasting model, trained reanalysis data, develop complete spatiotemporal basin. By way demonstration, new method applied short-range up 12 hours ahead, in-situ sparse National Data Buoy Center locations as input, corresponding mapping learned physics-based Met Office WAVEWATCH III global hindcast. full forecast system assessed independent measurements vicinity Louisiana Offshore Oil Port, previously unseen machine model. Results show accurate real-time, rapidly updating predictions are possible, at fraction computational cost traditional methods. success approach, flexibility framework, further suggest its utility related challenges. While still early stage development into fully relocatable capability, it intended this contribution provides foundation stimulate series subsequent efforts help support improved planning workability including (but not limited to) applications linked better resolving variability across renewable energy sites, predicting ocean current regimes proximity oil & gas platforms, well informing adaptive sampling strategies conducted autonomous vessels where adoption such can be run laptop computer, having data-driven decision-making industry.
Language: Английский
Citations
1
Published: June 25, 2024
Language: Английский
Citations
1
Journal of Geoscience and Environment Protection, Journal Year: 2023, Volume and Issue: 11(09), P. 118 - 132
Published: Jan. 1, 2023
The article discusses the application of artificial intelligence (AI) and automation in marine conservation, specifically relation to protection ecosystems definition protected areas (MPAs). It highlights threats that face due human activities emphasizes importance effective management conservation efforts. By improving data gathering, processing, monitoring, analysis, intelligence, automation, they can revolutionize research. In conclusion, this study AI responsibly ethically. order integrate these technologies into decision-making processes, stakeholders professionals must collaborate. Through use efforts be transformed by establishing new methods collecting analyzing data, making informed decisions, managing ecosystems.
Language: Английский
Citations
3
Published: Dec. 6, 2023
Marine toxins present considerable challenges to public health due their intricate biochemical profiles that complicate effectual analysis. In addressing this, our study utilizes a pioneering Ecoinformatics method, employing artificial intelligence meticulously examine the hepatotoxic effects of stonefish venom on murine models. The convergence assays, histopathological scrutiny, and cutting-edge machine learning algorithms is strategically designed unravel complex modalities venom-induced toxicity. Our findings offer unprecedented insight into dynamics marine venoms, underscoring utility AI in advancing toxin research. This multifaceted research not only deepens comprehension pathology but also forges pathway toward enhanced antivenom solutions, thereby reinforcing measures coastal ecosystems.
Language: Английский
Citations
2
Applied Ocean Research, Journal Year: 2024, Volume and Issue: 153, P. 104299 - 104299
Published: Nov. 6, 2024
Language: Английский
Citations
0
Environmental Modelling & Software, Journal Year: 2024, Volume and Issue: unknown, P. 106271 - 106271
Published: Nov. 1, 2024
Language: Английский
Citations
0
Journal of Coastal Conservation, Journal Year: 2024, Volume and Issue: 28(6)
Published: Dec. 1, 2024
Language: Английский
Citations
0
Coastal Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 104691 - 104691
Published: Dec. 1, 2024
Language: Английский
Citations
0
Published: Sept. 11, 2023
Language: Английский
Citations
0