Fishing ground forecasting of <em>Thunnus alalung</em> in Indian Ocean based on random forest

CHEN Xuezhong; FAN Wei; CUI Xuesen; ZHOU Weifeng; TANG Fenghua

doi:10.3969/j.issn.0253-4193.2013.01.018

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CHEN Xuezhong, FAN Wei, CUI Xuesen, ZHOU Weifeng, TANG Fenghua. Fishing ground forecasting of Thunnus alalung in Indian Ocean based on random forest[J]. Haiyang Xuebao, 2013, 35(1): 158-164. doi: 10.3969/j.issn.0253-4193.2013.01.018

Citation:

CHEN Xuezhong, FAN Wei, CUI Xuesen, ZHOU Weifeng, TANG Fenghua. Fishing ground forecasting of Thunnus alalung in Indian Ocean based on random forest[J]. Haiyang Xuebao, 2013, 35(1): 158-164. doi: 10.3969/j.issn.0253-4193.2013.01.018

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Fishing ground forecasting of Thunnus alalung in Indian Ocean based on random forest

doi: 10.3969/j.issn.0253-4193.2013.01.018

Key Laboratory of East China Sea and Oceanic Fishery Resources Exploitation, East China Sea Fisheries Research Institute, Shanghai 200090, China

Received Date: 2012-05-05
Rev Recd Date: 2012-09-26

Abstract

Abstract

To improve the forecasting accuracy of pelagic fishing ground and meet the needs of the practical fishery production, a method of albacore fishing ground forecast model in the Indian Ocean based on random forest was proposed.The Indian Ocean fishery environmental and spatio-temporal data by 5°×5° grid of every month from 2002 to 2009 were taken as predictor variables and the CPUE(catch per unit effort,unit∶inds per 1 000 hooks)of albacore,classified into high CPUE, moderate CPUE and low CPUE by tertile, was selected as the response variable in the random forest training. The training result indicated that the OOB(out-of-bag)misclassification rate tends to be steady when the number of decision trees reached 100. The random forest obtained through the training was applied to the albacore fishing ground monthly forecast in 2010.The isosurface chart on forecasted probabilities was overlapped on the practical fishery production and a comparison was made between them.The result demonstrated that the high CPUE fishing ground probability distribution and the CPUE of actual fishery ground tally well. Through the ROC analysis, AUCs (Area Under ROC Curve)of high CPUE, moderate CPUE and low CPUE were 0.847,0.743 and 0.803 respectively, indicating that the forecast precision was high. Finally the reason of the relatively low precision of medium CPUE probability in the forecast was analyzed.
- random forest,
- Thunnus alalung,
- fishing ground forecast,
- Indian Ocean

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