Fingerprinting Mediterranean hurricanes using pre-event thermal drops in seawater temperature

Scardino, Giovanni; Miglietta, Mario Marcello; Kushabaha, Alok; Casella, Elisa; Rovere, Alessio; Besio, Giovanni; Borzì, Alfio Marco; Cannata, Andrea; Mazza, Gianfranco; Sabato, Gaetano; Scicchitano, Giovanni

doi:10.1038/s41598-024-58335-w

Cited by 2 publications

(1 citation statement)

References 55 publications

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“…Moreover, they explored the effectiveness of an Integrated Coastal Vulnerability Index in their study. Scardino et al [14] delved into the relationship between sea surface temperatures (SSTs) and Mediterranean hurricane development [14]. Their research revealed a distinct pattern wherein SSTs exhibited a decrease before the formation of each Mediterranean hurricane, contrasting with strong temperate systems.…”

Section: Current Status and Challenges Of Marine Regulation By Coasta...mentioning

confidence: 99%

Priority Setting and Resource Allocation in Coastal Local Government Marine Regulatory Reform: Application of Machine Learning in Resource Optimization

Tian,

Wang

2024

Water

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This study investigates the prioritization and resource allocation strategies adopted by the coastal local governments of Qingdao, Dalian, and Xiamen in the context of marine regulatory reform aimed at enhancing regulatory efficiency. Data on relevant opinions, departmental requirements, and existing resource allocations were collected through a questionnaire survey. A backpropagation (BP) neural network was then applied to analyze the survey data, prioritize regulatory tasks, and propose resource allocation schemes. The findings demonstrate that integrating machine learning into marine regulation can significantly improve resource utilization efficiency, optimize task execution sequences, and enhance the scientific and refined nature of regulatory work. The BP neural network model exhibited strong predictive capabilities on the training set and demonstrated good generalization abilities on the test set. The performance of the BP neural network model varied slightly across different management levels. For the management level, the accuracy, precision, and recall rates were 85%, 88%, and 82%, respectively. For the supervisory level, these metrics were 81%, 83%, and 78%, respectively. At the employee level, the accuracy, precision, and recall rates were 79%, 81%, and 76%, respectively. These results indicate that the BP neural network model can provide differentiated resource allocation recommendations based on the needs of different management levels. Additionally, the model’s performance was assessed based on the employees’ years of experience. For employees with 0–5 years of experience, the accuracy, precision, and recall rates were 82%, 84%, and 79%, respectively. For those with 5–10 years of experience, the metrics were 83%, 86%, and 80%, respectively. For employees with over 10 years of experience, the accuracy, precision, and recall rates were 85%, 88%, and 82%, respectively. These data further confirm the applicability and effectiveness of the BP neural network model across different experience groups. Thus, the adoption of machine learning technologies for optimizing marine regulatory resources holds significant practical value, aiding in the enhancement of regulatory capacity and effectiveness within coastal local governments.

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Section: Current Status and Challenges Of Marine Regulation By Coasta...mentioning

confidence: 99%

Priority Setting and Resource Allocation in Coastal Local Government Marine Regulatory Reform: Application of Machine Learning in Resource Optimization

Tian,

Wang

2024

Water

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ARCHIMEDE—An Innovative Web-GIS Platform for the Study of Medicanes

Kushabaha,

Scardino,

Sabato

et al. 2024

Remote Sensing

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Mediterranean hurricanes, also known as medicanes, can cause significant damage to coastal communities. Consequently, they have been the subject of intense study in recent decades. Geographic Information Systems (GISs) and Web-GIS applications are essential for processing and analyzing geographic data from various sources, particularly in the field of spatial planning. Due to their ability to integrate heterogeneous information, these tools can provide experts with a comprehensive overview of relevant geospatial data. Here, we present ARCHIMEDE, a relational geodatabase connected to an open-source Web-GIS platform focused on Mediterranean hurricanes that contains data from recent research on these extreme weather phenomena. These datasets comprise climatic and oceanographic data obtained from remote sensing techniques as well as seismic and geomorphological data obtained from field observations. Consequently, this Web-GIS platform can enhance our understanding of Mediterranean cyclones by relating the physical properties of these natural phenomena with their impacts on coastal regions. ARCHIMEDE is an innovative tool for the dissemination of geographic information to stakeholders, researchers, and decision-makers, offering valuable support for the development of robust coastal management strategies aimed at mitigating the challenges posed by Mediterranean hurricanes.

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Fingerprinting Mediterranean hurricanes using pre-event thermal drops in seawater temperature

Cited by 2 publications

References 55 publications

Priority Setting and Resource Allocation in Coastal Local Government Marine Regulatory Reform: Application of Machine Learning in Resource Optimization

Priority Setting and Resource Allocation in Coastal Local Government Marine Regulatory Reform: Application of Machine Learning in Resource Optimization

ARCHIMEDE—An Innovative Web-GIS Platform for the Study of Medicanes

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