An integrated approach for coastal cliff susceptibility: The case study of Procida Island (southern Italy).

Procida Island, located in the Gulf of Naples (southern Italy), is characterized by steep cliffed coasts, articulated in a succession of headlands and small embayments with narrow pocket beaches, such as Ciraccio and Chiaia, often characterized by instability. In this study, a methodology for coastal cliff susceptibility assessment has been conceived based on hydraulic and geomorphological characteristics, which supported the construction of a Cliff Stability Index (CSI). The geomorphological characteristics are related to the whole cliff face, the cliff material resistance, and the cliff failure mechanisms. The hydraulic actions on the cliff are related to the wave impact which is exerted by the breaking waves once the wave run-up distance exceeds the beach width. The index takes into account the slope of the cliff, the rock strength, the wave energy at the cliff base produced by the broken wave and the presence of defence structures at the cliff base. The resulting index classification, obtained by addition of the partial sub-indices, has been compared with the observed coastal cliff evolution from 1954 to 2021.

Image Sensors for Wave Monitoring in Shore Protection: Characterization through a Machine Learning Algorithm.

Waves propagating on the water surface can be considered as propagating in a dispersive medium, where gravity and surface tension at the air-water interface act as restoring forces. The velocity at which energy is transported in water waves is defined by the group velocity. The paper reports the use of video-camera observations to study the impact of water waves on an urban shore. The video-monitoring system consists of two separate cameras equipped with progressive RGB CMOS sensors that allow 1080p HDTV video recording. The sensing system delivers video signals that are processed by a machine learning technique. The scope of the research is to identify features of water waves that cannot be normally observed. First, a conventional modelling was performed using data delivered by image sensors together with additional data such as temperature, and wind speed, measured with dedicated sensors. Stealth waves are detected, as are the inverting phenomena encompassed in waves. This latter phenomenon can be detected only through machine learning. This double approach allows us to prevent extreme events that can take place in offshore and onshore areas.

Physical and geochemical characteristics of land mud volcanoes along Colombia's Caribbean coast and their societal impacts.

The Caribbean coast is characterized by the presence of mud volcanoes, a secondary phenomenon of volcanism similar to mud diapirs for its development and evolution, but different in terms of geological features and forms. These mud volcanoes are often located close to tectonic faults and oil and gas deposits. Their geological context is dominated by the presence of clay sediments and brackish water, that favors the decomposition of organic material and the formation of methane. Mud volcanoes can thus be an important reservoir of hydrocarbons. This paper aims to fill the existing gap in the knowledge of mud volcanoes (MVs) of Colombia. We analyze the physical and geochemical characteristics of nine onshore mud volcanoes located in the Departments of Atlántico (La Laguna), Bolívar (Las Palomas, La Bonga, Santa Catalina, Yerbabuena, Clemencia, and Membrillal), Cordóba (Los Olivos), and Magdalena (Cañaveral). These structures present a kaolinitic composition, except for La Laguna mud volcano in which smectite is predominant. Apart from tectonic processes, this influences the shape and size of MVs and, also, the type and frequency of eruptions. The abundance of methane in all sites confirms the thermogenic origin of these structures. MVs are often considered landscape attractions as well as a therapeutic resources, but unfortunately they also represent a serious risk for the local communities, due to the frequent unexpected, eruptions, sometimes accompanied by the release of toxic gases or by landslides, that can damage the infrastructures and hurt the population living in the area. The MVs are classified into five vulnerability classes using a novel synthetic index which could improve the understanding of risks associated with the presence of MVs in proximity to towns and infrastructures.