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


Yuan, H and Gu, Y and Wang, J and Chen, Y and Chen, X, Study on the medium and long term of fishery forecasting based on neural network, Proceedings 4th International Conference on Artificial Intelligence and Computational Intelligence AICI2012, 26-28 October 2012, Chengdu, China, pp. 626-633. ISBN 9783642334771 (2012) [Refereed Conference Paper]

Copyright Statement

Copyright 2012 Springer

DOI: doi:10.1007/978-3-642-33478-8_77


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.

Item Details

Item Type:Refereed Conference Paper
Keywords:pelagic fisheries, correlation analysis, BP neural network, bedium to long term forecasting.
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Modelling and simulation
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Information systems, technologies and services not elsewhere classified
UTAS Author:Chen, Y (Ms Ying Chen)
ID Code:83200
Year Published:2012
Deposited By:Information and Communication Technology
Deposited On:2013-03-05
Last Modified:2014-12-09

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