Abstract：Since traditional fishery forecasting methods cannot accurately analyze the key factors in the face of increasingly complex ocean data, the light gradient boosting machine (LightGBM), an integrated learning model, was used to predict albacore tuna Thunnus alalunga fishery via the albacore tuna longline fishing production data in the south Pacific ocean from 2000 to 2015, combined with the sea surface temperature, concentration of chlorophyll a and sea surface height, latitude and longitude in marine environment factor and spatio-temporal data, and then the results were compared with naive bayes, XGBoost and BP neural networks. Meanwhile, the optimal parameters of LightGBM model were obtained by grid search method, and the stability of the model was verified by cross validation method. Results showed that the best prediction accuracy of the LightGBM model for albacoren tuna fishery in the south Pacific reached 72.6%, and the accuracy of the LightGBM model was significantly improved compared with other models. Finally, analysis of the importance of each input factor in the prediction of fishery revealed that sea surface height and sea surface temperature were important factors influencing the distribution of fisheries.
宫鹏, 王德兴, 袁红春, 陈冠奇, 吴若有. 基于LightGBM的南太平洋长鳍金枪鱼渔场预报模型研究[J]. 水产科学, 2021, 40(5): 762-767.
GONG Peng, WANG Dexing, YUAN Hongchun, CHEN Guanqi, WU Ruoyou. Fishing Ground Forecast Model of Albacore Tuna Based on LightGBM in the South Pacific Ocean. 水产科学, 2021, 40(5): 762-767.
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2021, Vol. 40 
