A Comparison between Functional Networks and Artificial Neural Networks for the Prediction of Fishing Catches

A. Iglesias, B. Arcay, J.M. Cotos, and A. Gómez (Spain)


Neural Network (ANN), Functional Network, Remote Sensing.


In recent years, Functional Networks have emerged as an extension of the Artificial Neural Networks (ANNs). In this article, we apply both network techniques to predict the catches of the Prionace Glauca (a class of shark) and the Katsowonus Pelamis (a variety of tuna, more commonly known as Skipjack). We have developed an application that will help reduce the search time for good fishing zones and thereby increase the fleet’s competitivity. Our results show that, thanks to their superior learning and generalization capacity, Functional Networks are more efficient than ANNs. Our data proceed from remote sensors. Their spectral signature allows us to calculate products that are useful for ecological modelling. After an initial phase of digital image processing, we created a database that provides all the necessary patterns to train both network types.

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