DocumentCode :
3023606
Title :
Prediction of probable Tuna fishing grounds based on Bayesian theorem
Author :
Zhou, Sufang ; Fan, Wei ; Wu, Jianping
Author_Institution :
Geogr. Dept., East China Normal Univ., Shanghai, China
Volume :
4
fYear :
2009
fDate :
7-8 Nov. 2009
Firstpage :
156
Lastpage :
162
Abstract :
Highly migratory tuna is one of economically important harvesting objects of the world. It is practically significant to forecast the probable fishing grounds. Based on satellite data of SST supplied by NASA and historical tuna catch data provided by SPC, relationship between catchability and SST was studied. And then using the Bayesian theorem, a tuna probable fishing grounds prediction expert system was set up. The result of 40-years-hindcasting experiments shows that the predicting accuracy of skipjack fishing grounds in West Pacific is over 70%, which is significant to guide fishing operations. However, now fishing grounds transcendental probability and conditional probability are computed every month, it must be modified according to field survey data for future fishing grounds prediction every week.
Keywords :
Bayes methods; expert systems; fishing industry; ocean temperature; probability; Bayesian theorem; SPC; SST; catchability; conditional probability; expert system; historical tuna catch data; probable tuna fishing grounds prediction; satellite data; skipjack fishing grounds; transcendental probability; Aquaculture; Artificial neural networks; Bayesian methods; Decision making; Economic forecasting; Environmental economics; Expert systems; Geography; Marine animals; Predictive models; Bayesian probability; Fishing grounds; Prediction model; Tuna;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Artificial Intelligence and Computational Intelligence, 2009. AICI '09. International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-3835-8
Electronic_ISBN :
978-0-7695-3816-7
Type :
conf
DOI :
10.1109/AICI.2009.530
Filename :
5376398
Link To Document :
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