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.

A field assessment was carried out in May 2023 in a natural protected area in South Italy to investigate the real-time behavior and fate of common marine litter items at different depths at the nearshore and foreshore under low energetic conditions. The field data were also compared with theoretical values calculated using theoretical formulations considering the Stokes drift current, wind-induced current, and current at breaking position. The wind approached from N to W, with values ranging from 2.4 to 3.8 m/s. The propagated significant wave height ranged from 14 to 21 cm, with an associated period of 3 s. Items with a positive buoyancy recorded different velocities. Very protruding (above water surface) items were greatly affected by windage and recorded velocities from 0.23 to 0.33 m/s; higher than the theoretical values that do not take into account windage. Theoretical values correctly predicted the velocity of other less-protruding items but slightly underestimated the velocity of neutral buoyancy items, evidencing in both cases the low relevance of the Stokes drift current. Items with a negative buoyancy moved (or not) in the bottom with different velocities. Different behaviors were observed at the breaking point, where items surfed (or not) spilling breaking waves, and at the foreshore, where items moved along the plunge step or in a “zig–zag” movement due to a swash–backwash process.

The latest progress in deep learning approaches has garnered significant attention across a variety of research fields. These techniques have revolutionized the way marine parameters are measured, enabling automated and remote data collection. This work centers on employing a deep learning model for the automated evaluation of tide and surge, aiming to deliver accurate results through the analysis of surveillance camera images. A mode of deep learning based on the Inception v3 structure was applied to predict tide and storm surges from surveillance cameras located in two different coastal areas of Italy. This approach is particularly advantageous in situations where traditional tide sensors are inaccessible or distant from the measurement point, especially during extreme events that require accurate surge measurements. The conducted experiments illustrate that the algorithm efficiently measures tide and surge remotely, achieving an accuracy surpassing 90% and maintaining a loss value below 1, evaluated through Categorical Cross-Entropy Loss functions. The findings highlight its potential to bridge the gap in data collection in challenging coastal environments, providing valuable insights for coastal management and hazard assessments. This research contributes to the emerging field of remote sensing and machine learning applications in environmental monitoring, paving the way for enhanced understanding and decision-making in coastal regions.

Advances in machine learning and deep learning approaches have drawn substantial interest across diverse research domains, including environmental studies. These innovative techniques have transformed the approaches to measuring marine parameters by facilitating automated and remote data collection. This study focuses on the implementation of a deep learning model to automatically assess tide and surge, aiming for precise outcomes through the analysis of surveillance camera imagery.Utilizing the Inception v3 structure, the deep learning model was applied to predict tide and storm surge from surveillance cameras strategically positioned in two distinct coastal regions, namely Santa Lucia in southeastern Sicily and Lignano Sabbiadoro in Friuli Venezia Giulia, Italy. The deep learning model is based on classification methods to assign a value of water level to a given frame. This approach is particularly advantageous in scenarios where traditional tide sensors face inaccessibility or are distant from measurement points, especially during extreme events demanding accurate surge measurements. The dataset used for the training and validation of the deep learning model covers the entire tide values that could be observed in the study areas. Predictions of the deep learning model were compared with tide gauge values in order to assess the system accuracy.The conducted experiments demonstrate the efficiency of the model in remotely and effectively measuring tide and surge, achieving an accuracy exceeding 90% while maintaining a loss value below 1 for the deep learning model. These findings underscore its potential to fill the data collection gap in challenging coastal environments, offering valuable insights for coastal management and hazard assessment. This study makes an important contribution to the rapidly growing field of remote sensing and machine learning applications in environmental monitoring, facilitating greater comprehension and decision-making in coastal areas.

Several cyclogenesis processes affect the Mediterranean Sea, causing significant impacts along its coastline. This study focuses on mapping the effects of cyclones in the Mediterranean Sea, particularly Mediterranean hurricanes, which cause severe damage to coastal areas. Advanced remote sensing and Geographic Information System (GIS) techniques are used to analyse climatic data and observe geomorphological evidence, such as flooding, coastal erosion, landslides, alluvial flooding and debris flow. By integrating climate features and geomorphological evidence, this research establishes a connection with the occurrence of Mediterranean cyclones. The study specifically examines the south-eastern coasts of Sicily, where Mediterranean Hurricanes have caused extensive damages, including flooding, erosion, and storm surges. Pre and post-storm morpho-topographical surveys were conducted to assess coastal flooding and erosion through aerial photogrammetry and Terrestrial Laser Scanner surveys. The collected data were stored in a geodatabase, allowing for the display of climate features and geomorphological evidence. Additionally, the development of an open-source Web-GIS platform integrated with the geodatabase can facilitate the dissemination of geographic information to stakeholders and researchers, promoting collaboration and informed decision-making. This study contributes to a better understanding of Mediterranean cyclones, enabling the development of effective coastal management strategies to mitigate the challenges posed by Mediterranean Hurricanes.