Prediction of Probable Tuna Fishing Grounds Based on Bayesian Theorem

文献类型： 外文期刊

第一作者： Zhou, Sufang

作者： Zhou, Sufang;Wu, Jianping;Fan, Wei

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期刊名称：2009 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND COMPUTATIONAL INTELLIGENCE, VOL IV, PROCEEDINGS

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年卷期： 2009 年

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收录情况： SCI

摘要： 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 catchbility 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 7096, 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: Tuna; Fishing grounds; Bayesian probability; Prediction model

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