USING NEURAL NETWORK FOR TRAWL MANAGEMENT

The article highlights the problems of managing trawl fishing, increasing the operation efficiency and reducing the influence of the human factor. There has been considered using a neural network in combination with a mathematical model and BigData technologies for predictive modeling in the process of automatic control of trawl fishing in order to increase its efficiency (to reduce energy and labor costs, to increase fishing productivity). Advantages of the proposed approach are the possibility to account for the above factors neglected in the mathematical model due to the complexity of their mathematical description (e.g. time of the day, time of the year, weather conditions, density, congestion, availability and distribution of food resources, other aquatic species), as well as the possibility of collecting and accumulating data obtained in many fishing operations and from different fishers for their subsequent consideration in the fishery management in the future. There has been proposed a solution based on the corrected output data obtained from a mathematical model and on the output data of a neural network. The weight coefficients of the neural network are extracted from a centralized database using BigData technologies before fishing with a selection criterion for the area and object of fishing. In the course of fishing the input data of the neural network and the final (adjusted) output data of the control are recorded. At the end of fishing the saved data is used in the process of training the neural network, followed by updating the weight coefficients in a centralized database. The neural network learning process occurs between the fisheries on a centralized shared neural network. The adjusted weight coefficients are updated in the general database of fishers.