Prediction of fishing vessel operation mode based on Stacking model fusion

Abstract

Due to the continuous upgrading and optimization of fishing technology and tools, and the diversification of fishing vessel operation methods, marine fishery resources are continuously depleted. Precise prediction of the operation methods of marine fishing vessels is helpful to realize effective supervision of fishing behavior of fishing vessels. In order to improve the prediction accuracy, when doing feature engineering, this paper uses a vector encoding scheme based on trajectory sequence, and uses text vectors to train the word2vec model to calculate the embedding features of each position. At present, the single method needs to be improved in terms of forecasting accuracy. This paper proposes a forecasting method based on Stacking model fusion in order to further improve the forecasting accuracy of marine fishing vessel operations. The experimental results show that the Stacking fusion model using the vector coding scheme based on the trajectory sequence has a greater improvement in prediction accuracy than a single model.