Modeling the Selectivity of the Cod-end of a Trawl Using Chaotic Fish Behavior and Neural Networks

摘要

Using empirical data of fish performance and physiological limits as well as physical stimuli and environmental data, a cod-end selectivity model based on a chaotic behavior model using the psycho-hydraulic wheel and neural-network approach was established to predict fish escape or herding responses in trawl and cod-end designs. Fish responses in the cod-end were categorized as escape or herding reactions based on their relative positions and reactions to the net wall. Fish movements were regulated by three factors: escape time, a visual looming effect, and an index of body girth-mesh size. The model was applied to haddock in a North Sea bottom trawl including frequencies of movement components, swimming speed, angular velocity, distance to net wall, and the caught-fish ratio; simulation results were similar to field observations. The ratio of retained fish in the cod-end was limited to 37-95% by optomotor coefficient values of 0.3-1.0 and to 13-67% by looming coefficient values of 0.1-1.0. The selectivity curves generated by this model were sensitive to changes in mesh size, towing speed, mesh type, and mesh shape.