Study on the medium and long term of fishery forecasting based on neural network

The forecasting system for medium to long term fishery resources is based on historical production data of specified fish types and those marine environmental factors. As these systems give a macro level prediction of fishery resources in the coming years they provide indispensable references for the planning and management of catching seasons. This paper introduces a new model for the prediction using Windows XP platform and Visual Studio 2010 development environment with C# programming language. Combining correlation analysis and BP neural network, the new model analyzes marine environmental data and fishery historical production data to forecast fisheries in medium to long terms. Experiments applying this model to forecast the squid production in the Pacific Northwest result in an average relative error of about 13.5% as compared with 23.2% error using linear regression analysis. This result proves that the new model has the potential to provide better forecasts for fisheries.