Ship wakes and their potential shoreline impact in Tampa Bay

Meyers, Steven D.; Luther, Mark E.; Ringuet, Stephanie; Raulerson, Gary; Sherwood, Edward T.; Conrad, Katie; Basili, Gianfranco

doi:10.1016/j.ocecoaman.2021.105749

Cited by 4 publications

(4 citation statements)

References 57 publications

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“…Therefore, it is crucial to analyze and use AIS data to discover the valuable information hidden in fishing vessel operations. Recently, with the wide application of AIS systems, the continuous development of communication technology and the improvement in AIS data processing methods, AIS data have become an important part of marine big data, which has attracted extensive attention from academia in related fields at home and abroad [12], and has been increasingly applied to the study of fishing vessel behavior. In particular, with the help of AI algorithms to analyze and mine AIS data, a series of achievements have been made in the fields of maritime traffic safety, fishing vessel positioning, fishing ground research, fishing vessel classification, fishing behavior identification and detection of ship anomalies [13].…”

Section: Automatic Identification Systemmentioning

confidence: 99%

Application of Artificial Intelligence in the Study of Fishing Vessel Behavior

Cheng,

Zhang,

Chen

et al. 2023

Fishes

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Monitoring and understanding the behavior of fishing vessels are important in facilitating effective management, preventing illegal fishing, informing fishing grounds and evaluating effects of harvests on fishery resources. In recent decades, a large quantity of real-time data of fishing vessels have become available with the development of vessel-tracking systems, making it possible to study the behavior of fishing vessels in high spatial and temporal resolutions. To effectively and efficiently deal with the large amount of data, algorithms from artificial intelligence (AI) are increasingly applied in the study of fishing vessel behavior. In this paper, we first introduce the various data sources for studying fishing vessel behavior and compare their pros and cons. Secondly, we review the AI methods that have been used to monitor and extract the behavior of fishing vessels from big data. Then, studies on the physical, ecological and social mechanisms affecting the behavior of fishing vessels were synthesized. Lastly, we review the applications of fishing vessel behavior in fishery science and management.

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Section: Automatic Identification Systemmentioning

confidence: 99%

Application of Artificial Intelligence in the Study of Fishing Vessel Behavior

Cheng,

Zhang,

Chen

et al. 2023

Fishes

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“…2017; Meyers et al. 2021). Thereby, how to quantify the impact of ship wakes on the coastal environments is an open and essential question for coastal management and protection.…”

Section: Introductionmentioning

confidence: 99%

“…They can not only erode the bank of the waterway and coastal line, but also pose a potential hazardous impact on coastal structures or berthed floating bodies (Bourne 2000;Soomere 2007;Rapaglia et al 2015;Gabel, Lorenz & Stoll 2017;Scarpa et al 2019). An advancing vessel can act as a source of propagating wake wash, generating wakes that force sediment transport and drive vertical mixing, which is important to aquatic ecosystems (Rapaglia et al 2015;Gabel et al 2017;Meyers et al 2021). Thereby, how to quantify the impact of ship wakes on the coastal environments is an open and essential question for coastal management and protection.…”

Section: Introductionmentioning

confidence: 99%

An Earth-fixed observer to ship waves

Liang,

Li,

Chen

2024

J. Fluid Mech.

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This work deals with the linear surface waves generated by a vessel advancing at a constant forward speed. These waves, known as ship waves, appear stationary to an observer on the vessel. Rather than exploring the well-studied stationary ship waves, this work delves into the physical properties of ship waves measured at Earth-fixed locations. While it might have been expected that analysing these waves in an Earth-fixed coordinate system would be a straightforward transformation from existing analytical theories in a moving coordinate system, the reality proves to be quite different. The properties of waves measured at fixed locations due to a passing ship turn out to be complex and non-trivial. They exhibit unique characteristics, being notably unsteady and short crested, despite appearing stationary to an observer on the generating vessel. The analytical expressions for the physical properties of these unsteady waves are made available in this work, including the amplitude, frequency, wavenumber, direction of propagation, phase velocity and group velocity. Based on these newly derived expressions and two-point measurements, an inverse method has been presented for determining the advancing speed and the course of motion of the moving ship responsible for the wave generation. The results from this study can be used in a wide range of applications, such as interpreting data from point measurements and assessing the roles of ship waves in transporting ocean particles.

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“…Implementing improved farming techniques and adopting best management practices are vital for minimizing these impacts. Additionally, boat traffic in bodies of water introduces potential pollutants, such as oil and fuel residues, into the water (Meyers et al, 2021). The disturbance caused by boats can also contribute to sediment resuspension, further influencing water quality dynamics like turbidity and habitat preservation.…”

Section: Introductionmentioning

confidence: 99%

Integrating Conversational AI Agents for Enhanced Water Quality Analytics: Development of a Novel Data Expert System

Vald,

Sermet,

Mount

et al. 2024

Preprint

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Despite advancements in environmental monitoring, the gap between data collection and user-friendly data interpretation remains a significant challenge, especially in the domain of water quality management. This paper introduces the Artificial Intelligence Data Expert (AI-DE), a novel data analytics system that is designed to facilitate on-demand analysis of time-series sensor data related to water quality using natural language queries. The AI-DE leverages features of ChatGPT, including Named Entity Recognition, geocoding, and sentiment analysis, to enable intuitive and natural language-based data analysis. This system transformation allows for immediate, ad-hoc querying and interpretation of environmental data, tailored to the needs of diverse user groups. Key features include chat controls that customize user interaction, a chat bypass enabling seamless integration with an integrated information system, and a data interpretation mode for detailed analysis. The AI-DE enhances user engagement and comprehension of water quality data, thereby supporting informed decisions and actions for environmental management. The AI-DE represents a step forward in increasing access to complex environmental data through conversational AI technologies.

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Ship wakes and their potential shoreline impact in Tampa Bay

Cited by 4 publications

References 57 publications

Application of Artificial Intelligence in the Study of Fishing Vessel Behavior

Application of Artificial Intelligence in the Study of Fishing Vessel Behavior

An Earth-fixed observer to ship waves

Integrating Conversational AI Agents for Enhanced Water Quality Analytics: Development of a Novel Data Expert System

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