AN ANALYSIS OF THE FISH POPULATIONS BY USING ANN AND WAVELET TECHNIQUES

Abstract

Air - sea climate, environmental and biological conditions show various differences on several spatio-temporal scales. Climate change associated with anthropogenic activity and natural global multi-decadal climate variations effects on air-sea interactions and water surface–atmosphere–biosphere climate system. In the first part of this paper is related with Artificial Neuro Network analyses for prediction of fish stocks in Marmara and Black Sea. The second part of this study is based on wavelet analyses and, the results were compared with former wavelet and harmonic analyses to explain seasonal effects of NAO and ENSO on fish population. The influence of climatic oscillations (based on NAO and ENSO) on monthly catch rates of fish population such as sea bass, Atlantic bonito,blue fish sea (pomatomus population between 1991-2012) in Black Sea and Marmara have been analyzed by discrete wavelet transform (DWT) with Meyer and Daubechie's. Wavelet analysis is an efficient method of time series analysis to study non-stationary data. Wavelet analyses allowed us to quantify both the pattern of variability in the time series and non-stationary associations between fish population and climatic signals. Phase analyses were carried out to investigate dependency between the two signals. We reported strong relations between fish stock and climate series for the 4- and 5-yr periodic modes, i.e. the periodic band of the El Niño Southern Oscillation signal propagation in the Black Sea and Marmara Sea. These associations were non-stationary, evidenced from 1995 to 2012. It is recognized that other factors in small, meso and large scales may modulate fish stocks beginning from 1995 and more clearly from 2005