Automated characterisation of Deep-sea imagery using Machine Learning: implications for future conservation and mineral extraction

This thesis aimed to develop a methodology using Machine Learning (ML) techniques for the interpretation of deep-sea resources. The deep-sea hosts diverse ecosystems and valuable resources, but potential environmental implications, particularly from mining activities, necessitate effective management strategies. Detailed maps of the sea floor are therefore a necessity, yet such maps have to date only been produced based on manual interpretation which is time consuming and subjective. The study focused on assessing the potential of ML methods to map deep-sea features based on photomosaic and bathymetry data in order to take the first steps in developing an automated, objective, and time-saving technique. This thesis’s method accurately identified and classified features like chimneys at the hydrothermal vent fields, providing insights for resource interpretation and conservation. Integrating ML methods into deep-sea resource management is crucial. The methodology enhances understanding of complex techniques, such as Convolutional Neural Networks (CNN) and Object-Based Image Analysis (OBIA) to overcome a seabed characterization. Simultaneously describing the parameters utilised to achieve a meaningful classification. ML algorithms analyze large data volumes, extract patterns, and predict feature distributions, aiding targeted conservation measures and sustainable resource exploitation. The methodology successfully mapped hydrothermal chimneys in two study areas yet producer accuracies (0,7%) were higher than user accuracies (0,64%), indicating that there were other landforms that shared similar features. The methodology also helps assess potential environmental implications of future mining, supporting informed decision-making and mitigation strategies. It serves also as a foundation for future research to aim at overcoming problems related to incomplete spatial coverage, attempt to better utilize shape and spatial parameters within the OBIA refinement, try to identify more background classes for excluding them from the model, etc.Master's Thesis in Earth ScienceGEOV399MAMN-GEO

Innovative big data integrationand analysis techniques for urban hazard management

PhD ThesisModern early warning systems (EWS) require sophisticated knowledge of natural hazards, the urban context and underlying risk factors to enable dynamic and timely decision making (e.g., hazard detection, hazard preparedness). Landslides are a common form of natural hazard with a global impact and are closely linked to a variety of other hazards. EWS for landslide prediction and detection relies on scienti c methods and models which require input from the time-series data, such as the earth observation (EO) and ancillary data. Such data sets are produced by a variety of remote sensing satellites and Internet of Things sensors which are deployed in landslide-prone areas. Besides, social media-based time-series data has played a signi cant role in modern disaster management. The emergence of social media has led to the possibility of the general public contributing to the monitoring of natural hazard by reporting incidents related to hazard events. To this end, the data integration and analysis of potential time-series data sources in EWS applications have become a challenge due to the complexity and high variety of data sources. Moreover, sophisticated domain knowledge of natural hazards and risk management are also required to enable dynamic and timely decision making about serious hazards. In this thesis, a comprehensive set of algorithmic techniques for managing high varieties of time series data from heterogeneous data sources is investigated. A novel ontology, namely Landslip Ontology, is proposed to provide a knowledge base that establishes the relationship between landslide hazard and EO and ancillary data sources to support data integration for EWS applications. Moreover, an ontology-based data integration and analytics system that includes human in the loop of hazard information acquisition from social media is proposed to establish a deeper and more accurate situational awareness of hazard events. Finally, the system is extended to enable an interaction between natural hazard EWS and electrical grid EWS to contribute to electrical grid network monitoring and support decision-making for electrical grid infrastructure management

The development of a knowledge-based system for the preliminary investigation of contaminated land.

Available from British Library Document Supply Centre-DSC:DXN048691 / BLDSC - British Library Document Supply CentreSIGLEGBUnited Kingdo

Rising groundwater levels in the Neapolitan area and its impacts on civil engineering structures, agricultural soils and archaeological sites

The rise of groundwater levels (GWLr) is a worldwide phenomenon with several consequences for urban and rural environment, cultural heritage and human health. In this thesis the phenomenon and its effects are analysed in two sectors of the Metropolitan City of Naples (southern Italy). These areas are the central sector of the eastern plain of Naples and the Cumae archaeological site in the western coastal sector of Phlegraean Fields. The triggering mechanism of GWLr is attributed to anthropogenic and natural causes, as the groundwater rebound (GR) process and the relative sea level rise due to volcano-tectonic subsidence of coastal areas. In the eastern plain of Naples, the interruption of pumping for public and private purposes occurred in 1990, leading to a progressive increase of piezometric levels with values up to 16.54 m. Since the end of 2000s, episodes of groundwater flooding (GF) have been registered on underground structures and agricultural soils. The historical piezometric levels and a comprehensive conceptual model of the aquifer have been reconstructed, as well as a first inventory of GF episodes and the hydrogeological controlling factors of GF occurrence have been detected. The economic consequences of GF have been analysed for an experimental building of study area, in which a sharp increment of expenditures has been registered. These costs include technical and legal support, construction and maintenance of GF mitigation measures and electricity consumption. Others GWLr-induced phenomena have been recognised, as ground vertical deformation and variations of the groundwater contamination. A relationship between GWLr and ground uplift emerges from the coupled analysis of piezometric and interferometric data, referred to the 1989-2013 period. The ground deformation occurs in response to the recovery of pore-pressure in the aquifer system, reaching an uplift magnitude up to 40-50 mm. In the 1989-2017 period, the piezometric levels and the concentrations of some natural contaminants in groundwater (Fe, Mn, fluorides) show opposite trends, conversely the same rising trend has been observed with nitrates. These different responses to piezometric rise are related to the lack of mobilization of deep fluids due to the interruption of pumping and to the reduction of the surficial contaminants' time travel caused by a shorter thickness of the vadose zone. In the western sector of Phlegraean Fields, the naturally triggered GWLr has caused GF in the Cumae archaeological site for the last decade, threatening safeguard and conservation of the archaeological heritage. From an integrated hydrogeological, hydrochemical and isotopic survey, a considerable contamination of groundwater resulted, due to the presence of rising highly mineralized fluids, mobilized during pumping periods, and others anthropogenic sources of contamination. Lastly, a novel methodology for groundwater flooding susceptibility (GFS) assessment has been developed by using machine learning techniques and tested in the eastern plain of Naples. Points of GF occurrence have been connected to environmental predisposing factors through Spatial Distribution Models' algorithms to estimate the most prone areas' distribution. Ensemble Models have been carried out to reduce the uncertainty associated with each algorithm and increase its reliability. Mapping of GFS has been realized by dividing occurrence probability values into five classes of susceptibility. Results show an optimal correspondence between GF points' location and the highest classes (93% of GF points falls into high and very high classes). The results of this research provide new knowledge on the GWLr phenomenon that has impacted a large territory of the Metropolitan City of Naples. The methodological approach used can be exported in others hydrogeological contexts to characterize GWLr and its impacts. In addition, the implemented GFS methodology represents a new tool to assist local government authorities, planners and water decision-makers in addressing the problems deriving from GF, and a first step for the evaluation of GF risk as required by Italian and European legislation