https://www.um.edu.mt/library/oar/handle/123456789/120827 Sun, 12 Apr 2026 03:36:58 GMT 2026-04-12T03:36:58Z https://www.um.edu.mt/library/oar/handle/123456789/132495 Title: Identifying and quantifying user conflicts within the Marine Protected Area (MT101) of the Maltese Islands Abstract: Marine Protected Areas (MPAs) are essential for preserving marine biodiversity, yet they face challenges from various human pressures, including vessel activities. This study examines the impact of vessel density on the Southwest Marine Protected Area (MPA) in Malta, known as MT0000101. Vessel activities, including anchoring, mooring, and discharge, pose significant threats to benthic habitats such as sandbanks and seagrass meadows. Despite protective measures, vessels continue to frequent these MPAs, raising concerns about habitat degradation. Using data spanning from 2017 to 2022, this research analyses vessel density distribution and its implications for marine habitats within MPA MT0000101. Results reveal distinct patterns of vessel activity, with passenger vessels showing consistent high impacts along bay areas and fishing vessels demonstrating dispersed yet intensified presence. Proposed mitigation measures include enhanced enforcement of MPA regulations and habitat-specific management strategies. The findings emphasise the importance of collaborative efforts among stakeholders to ensure the long-term sustainability of marine ecosystems within MPA MT0000101. This study contributes valuable insights into the understanding of vessel impacts on MPAs and provides guidance for conservation and management strategies in similar marine environments globally. Description: M.Sc.(Melit.) Mon, 01 Jan 2024 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/132495 2024-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/132256 Title: A re-evaluation of the seismic hazard for the Maltese Islands Abstract: This thesis is organised into eight chapters to provide a comprehensive seismic hazard assessment of the Maltese Islands. Chapter 1 outlines the study's motivations, emphasising the need for an updated seismic hazard assessment due to increasing population and building density in Malta. It reviews prior research and contextualises the significance of this study for earthquake-resistant planning and design. Chapter 2 outlines the seismotectonic features and seismogenic sources in the Mediterranean, needed for the definition of seismic source zones. It also introduces the Malta Seismic Network and provides an overview of the seismic history of the Maltese Islands. Chapter 3 details the methodology for seismic hazard assessment, focusing on probabilistic seismic hazard analysis (PSHA). It explains the key concepts, steps involved, and the logic tree approach used to incorporate uncertainties in the analysis. Chapter 4 discusses the development of ground motion prediction equations (GMPEs) using ridge regression and neural networks. These equations are calibrated for the Central Mediterranean region and compared with established models to ensure accuracy. Chapter 5 explains the use of R-CRISIS and OpenQuake software for conducting PSHA. It details the input parameters, source models, and the incorporation of site effects to provide a thorough seismic hazard assessment. Chapter 6 presents the PSHA results at bedrock conditions, followed by Chapter 7 which analyses and discusses these results. This chapter compares findings from CRISIS and OpenQuake, evaluates them against previous studies, and contrasts results based on bedrock versus site conditions. Chapter 8 summarises the main findings and offers recommendations for future research based on the results obtained. Each chapter progressively builds towards a comprehensive evaluation of seismic risk for the Maltese Islands, integrating advanced methodologies and tools to inform safer construction and planning practices. Description: Ph.D.(Melit.) Mon, 01 Jan 2024 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/132256 2024-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/130599 Title: Geomatics, geophysical and environmental analysis in Mellieha Bay (Malta) Abstract: This study investigates the elemental composition and granulometry of sand samples collected from PB Mellieha Bay (Ghadira Bay), Malta, aiming to assess environmental quality to inform sustainable coastal management strategies. The primary objectives are to identify the presence and concentrations of elements using Energy Dispersive X-ray Fluorescence (EDXRF) spectroscopy, a method that provides detailed insights into elemental composition, and to characterize particle size distribution, particularly in relation to coastal erosion and sediment dynamics. Particle size analysis was conducted using the sieve analysis method with a mechanical shaker, where dried sand was passed through a series of standard sieves (1.18 mm, 710 μm, 600 μm, 300 μm, 150 μm, 106 μm) arranged in decreasing mesh size. This process involved agitating the sand for approximately 15 minutes to allow particles to pass through based on size. The residue on each sieve was weighed to determine particle size distribution, providing a detailed classification of sediment particles and insights into the granulometry of the bay's sediment. Results were represented as bar graphs to facilitate quick identification of trends, variations, and dominant particle sizes. In addition to EDXRF analyses, statistical analyses were performed using Kruskal-Wallis tests to determine associations between metal levels across different samples. Pairwise Correlation tests were also conducted to identify frequently co-occurring metals. Furthermore, Principal Components Analysis (PCA) was utilized to reveal patterns and intercorrelations within the data, supported by supplementary funnel graphs indicating the percentage abundance of metal compounds. The findings enhance understanding of the bay's sedimentary environment and its ecological and geological implications. This research is designed to aid in the overall perception of this coastal ecosystem and is particularly valuable for future monitoring and management efforts at PB Mellieha Bay. Description: M.Sc.(Melit.) Mon, 01 Jan 2024 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/130599 2024-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/130597 Title: Satellite-derived bathymetry for the Maltese islands, and new insights for analysing salinity and temperature from multispectral sensors Abstract: Given the dynamic nature of coastal zones, it is imperative to gain an understanding of their evolutionary patterns. Safeguarding these ecosystems necessitates the ability to observe their physical features and controlling processes with precision in both space and time. This demands the acquisition of precise and up-to-date information regarding several coastal parameters. Thus, to gain a comprehensive understanding of these ecosystems, this study employs remote sensing techniques, machine learning algorithms and traditional in-situ approaches. Together, these serve as valuable tools to help comprehend the characteristics and mechanisms occurring within these transitional regions of the Maltese archipelago. An empirical workflow was implemented to predict the spatial and temporal variations in bathymetry, sea surface salinity (SSS), and sea surface temperature (SST) from 2022 to 2024. This was achieved by leveraging Sentinel-2 satellite platforms, the Random Forest (RF), and Linear Regression (LR) machine learning algorithms, as well as in-situ data collected from multi-beam echo sounder surveys, sea gliders, and floats. Subsequently, the numerical data generated by the various machine learning algorithms were validated with an error metric and converted into visual representations to illustrate the parametric variations across the Maltese Islands. The RF algorithm outperformed the LR model in predicting accurate bathymetric information for all three years, yielding highly precise bathymetric data for the entire Maltese archipelago. Furthermore, the RF algorithm demonstrated strong performance in predicting SSS and SST, indicating its capability to handle more dynamic parameters effectively. Lastly, the parametric maps generated for all three years provided a clear understanding of both the spatial and temporal changes within these three coastal parameters. Therefore, this study effectively combined satellite data, in-situ measurements, and machine learning algorithms to accurately predict bathymetry, SSS, and SST across three years within the Maltese Islands. Description: M.Sc.(Melit.) Mon, 01 Jan 2024 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/130597 2024-01-01T00:00:00Z