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AUTHOR
Giovanni Giacalone
Identifying small pelagic Mediterranean fish schools from acoustic and environmental data using optimized artificial neural networks
Abstract The Common Fisheries Policy of the European Union aims to exploit fish stocks at a level of Maximum Sustainable Yield by 2020 at the latest. At the Mediterranean level, the General Fisheries Commission for the Mediterranean (GFCM) has highlighted the importance of reversing the observed declining trend of fish stocks. In this complex context, it is important to obtain reliable biomass estimates to support scientifically sound advice for sustainable management of marine resources. This paper presents a machine learning methodology for the classification of pelagic species schools from acoustic and environmental data. In particular, the methodology was tuned for the recognition of an…
Pelagic species identification by using a PNN neural network and echo-sounder data
For several years, a group of CNR researchers conducted acoustic surveys in the Sicily Channel to estimate the biomass of small pelagic species, their geographical distribution and their variations over time. The instrument used to carry out these surveys is the scientific echo-sounder, set for different frequencies. The processing of the back scattered signals in the volume of water under investigation determines the abundance of the species. These data are then correlated with the biological data of experimental catches, to attribute the composition of the various fish schools investigated. Of course, the recognition of the fish schools helps to produce very good results, that is very clo…
Artificial neural networks for fault tollerance of an air-pressure sensor network
A meteorological tsunami, commonly called Meteotsunami, is a tsunami-like wave originated by rapid changes in barometric pressure that involve the displacement of a body of water. This phenomenon is usually present in the sea cost area of Mazara del Vallo (Sicily, Italy), in particular in the internal part of the seaport canal, sometimes making local population at risk. The Institute for Coastal Marine Environment (IAMC) of the National Research Council in Italy (CNR) have already conducted several studies upon meteotsunami phenomenon. One of the project has regarded the creation of a sensors network composed by micro-barometric sensors, located in 4 different stations close to the seaport …
Pattern Classification from Multi-beam Acoustic Data Acquired in Kongsfjorden
Climate change is causing a structural change in Arctic ecosystems, decreasing the effectiveness that the polar regions have in cooling water masses, with inevitable repercussions on the climate and with an impact on marine biodiversity. The Svalbard islands under study are an area greatly influenced by Atlantic waters. This area is undergoing changes that are modifying the composition and distribution of the species present. The aim of this work is to provide a method for the classification of acoustic patterns acquired in the Kongsfjorden, Svalbard, Arctic Circle using multibeam technology. Therefore the general objective is the implementation of a methodology useful for identifying the a…
Automatic classification of acoustically detected krill aggregations: A case study from Southern Ocean
Acoustic surveys represent the standard methodology to assess the spatial distribution and abundance of pelagic organisms characterized by aggregative behaviour. The species identification of acoustically observed aggregations is usually performed by taking into account the biological sampling and according to expert-based knowledge. The precision of survey estimates, such as total abundance and spatial distribution, strongly depends on the efficiency of acoustic and biological sampling as well as on the species identification. In this context, the automatic identification of specific groups based on energetic and morphological features could improve the species identification process, allo…
Unsupervised Classification of Acoustic Echoes from Two Krill Species in the Southern Ocean (Ross Sea)
This work presents a computational methodology able to automatically classify the echoes of two krill species recorded in the Ross sea employing scientific echo-sounder at three different frequencies (38, 120 and 200 kHz). The goal of classifying the gregarious species represents a time-consuming task and is accomplished by using differences and/or thresholds estimated on the energy features of the insonified targets. Conversely, our methodology takes into account energy, morphological and depth features of echo data, acquired at different frequencies. Internal validation indices of clustering were used to verify the ability of the clustering in recognizing the correct number of species. Th…
A novel method to simulate the 3D chlorophyll distribution in marine oligotrophic waters
Abstract A 3D advection-diffusion-reaction model is proposed to investigate the abundance of phytoplankton in a difficult-to-access ecosystem such as the Gulf of Sirte (southern Mediterranean Sea) characterized by oligotrophic waters. The model exploits experimentally measured environmental variables to reproduce the dynamics of four populations that dominate phytoplankton community in the studied area: Synechococcus, Prochlorococcus HL, Prochlorococcus LL and picoeukaryotes. The theoretical results obtained for phytoplankton abundances are converted into chl-a and Dvchl-a concentrations, and the simulated vertical chlorophyll profiles are compared to the corresponding experimentally acquir…
A pattern recognition approach to identify biological clusters acquired by acoustic multi-beam in Kongsfjorden
The Svalbardsis one of the most intensively studied marine regions in the Artic; here the composition and distribution of marine assemblages are changing under the effect of global change, and marine communities are monitored in order to understand the long-term effects on marine biodiversity. In the present work, acoustic data collected in the Kongsfjorden using multi-beam technology was analyzed to develop a methodology for identifying and classifying 3D acoustic patterns related to fish aggregations. In particular, morphological, energetic and depth features were taken into account to develop a multi-variate classification procedure allowing to discriminate fish species. The results obta…
Distribution and spatial structure of pelagic fish schools in relation to the nature of the seabed in the Sicily Channel (Central Mediterranean).
Hydroacoustic data collected during two echosurveys carried out in the Sicily Channel in 1998 and 2002 were analysed to investigate the distribution and spatial structure of small pelagic fish species in relation to the sedimentological nature of the sea bottom. The study was carried out on two contiguous areas (labelled ZONE 1 and ZONE 2) of the continental shelf off the southern coast of Sicily, characterised by different dominant texture, ‘sand’ for ZONE 1 and ‘clayey-silt’ for ZONE 2. Simultaneous information on small pelagic fish schools and the seabed was obtained using a quantitative echo-sounder (SIMRAD EK500) that measures echoes due to the scattering from both fish schools and the…