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Study on the medium and long term of fishery forecasting based on neural network
conference contribution
posted on 2023-05-23, 07:26 authored by Yuan, H, Gu, Y, Wang, J, Chen, Y, Chen, XThe 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.
History
Publication title
Proceedings 4th International Conference on Artificial Intelligence and Computational Intelligence AICI2012Volume
7530Editors
J Lei, F Wang, H Deng and D MiaoPagination
626-633ISBN
9783642334771Department/School
School of Information and Communication TechnologyPublisher
Springer VerlagPlace of publication
Heidelberg, GermanyEvent title
4th International Conference on Artificial Intelligence and Computational Intelligence AICI2012Event Venue
Chengdu, ChinaDate of Event (Start Date)
2012-10-26Date of Event (End Date)
2012-10-28Rights statement
Copyright 2012 SpringerRepository Status
- Restricted