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A model for fishery forecast based on cluster analysis and nonlinear regression


Yuan, HC and Tan, MX and Gu, YT and Chen, Ying, A model for fishery forecast based on cluster analysis and nonlinear regression, Proceedings of the 2015 International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2015), 26-27 July 2015, Phuket, Thailand, pp. 415-418. ISBN 978-1-5108-0645-0 (2015) [Refereed Conference Paper]

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Copyright 2015 The authors

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There has been an increasing amount of research in the relationship between environmental factors and fishing yield. This paper adds to the body of knowledge by developing a new model for forecasting fishing yield. The model combines fishery domain expert knowledge, marine environmental factor data such as water temperature, chlorophyll concentration and sea surface level as base data and applies cluster analysis that incorporates function fitting and nonlinear regression for data analysis and processing. The model is tested for forecast accuracy and the test result is compared with those using RBF and SVM, the two methods commonly used for similar purposes. The comparison result reveals this new model increases both the accuracy in fishery forecast and the reliability in guiding fishery production and related activities. It can also help explore and discover the distribution of fishing grounds.

Item Details

Item Type:Refereed Conference Paper
Keywords:pelagic fishing; fisheries forecasting; cluster analysis; nonlinear regression
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Artificial life and complex adaptive systems
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the information and computing sciences
UTAS Author:Chen, Ying (Ms Ying Chen)
ID Code:108818
Year Published:2015
Deposited By:Information and Communication Technology
Deposited On:2016-05-06
Last Modified:2018-01-17

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