Ecological distribution of semi-demersal fishes in space and time on the shelf of Antalya Gulf

In this study we provide a baseline data on semidemersal fish assemblages and biology in a heterogeneous and yet less studied portion of the shelf of Antalya Gulf. The distribution of fish abundance in three transects subjected to different fisheries regulations (fishery vs non fishery areas), and including depths of 10, 25, 75, 125, 200 m, was studied between May 2014 and February 2015 in representative months of winter, spring, summer and autumn seasons. A total of 76 fish species belonging to 40 families was collected and semidemersal species distribution was analyzed in comparison with the whole community. Spatial distribution of fish was driven mainly by depth and two main assemblages were observed: shallow waters (10-25; 75 m) and deep waters (125-200 m). Significant differences among transects were found for the whole community but not for the semidemersal species. Analysis showed that this was due to a strong relation of these species with local environmental characteristics rather than to a different fishing pressure over transects. Firstly all species distribute according to the bathymetrical gradient and secondly to the bottom type structure. Semidemersal species were then found more related to zooplankton and suspended matter availability. The main morphological characteristics, sex and size distribution of the target semidemersal species Spicara smaris (Linnaeus, 1758), Saurida undosquamis (Richardson, 1848), Pagellus acarne (Risso, 1827) were also investigated.

Near Real-Time Detection Of Pipe Bursts in Water Distribution Systems

The research work presented in the thesis describes a new methodology for the automated near real-time detection of pipe bursts in Water Distribution Systems (WDSs). The methodology analyses the pressure/flow data gathered by means of SCADA systems in order to extract useful informations that go beyond the simple and usual monitoring type activities and/or regulatory reporting , enabling the water company to proactively manage the WDSs sections. The work has an interdisciplinary nature covering AI techniques and WDSs management processes such as data collection, manipulation and analysis for event detection. Indeed, the methodology makes use of (i) Artificial Neural Network (ANN) for the short-term forecasting of future pressure/flow signal values and (ii) Rule-based Model for bursts detection at sensor and district level. The results of applying the new methodology to a District Metered Area in Emilia- Romagna’s region, Italy have also been reported in the thesis. The results gathered illustrate how the methodology is capable to detect the aforementioned failure events in fast and reliable manner. The methodology guarantees the water companies to save water, energy, money and therefore enhance them to achieve higher levels of operational efficiency, a compliance with the current regulations and, last but not least, an improvement of customer service.