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An accurate forecast can be greatly contributed to the yellowfin tuna fishing ground in the western and Central Pacific. However, a large amount of fishery data, and high feature dimension have posed a great over-fitting on the various classification in recent years. The random forest parallel integration can be expected to achieve the excellent performance of the extreme gradient boosting decision tree algorithm. In this study, a hybrid integration model was proposed to combine the Xgboost with Random Forest (XGBRF) with the random forest and extreme gradient lifting decision tree. The fishery production data was also collected from the operation data of 43 distant-water longline fishing vessels of China Aquatic Group in the western and Central Pacific (0°-30°S; 110°E-170°W) from 2008 to 2019, including catch information, such as amount, job date, as well as the job latitude and longitude. A comparison was performed on the fishery data, including the concentration of chlorophyll a, eddy kinetic energy, sea surface height anomalies, temperature and salinity of the 0-500 m mixed water layer. A total of 36 variable combinations were used as the original data set, including the Southern Oscillation Index (SOI), the Arctic Oscillation Index (AOI), the Pacific Decadal Oscillation Index (PDOI), and North Pacific Gyre Oscillation Index (NPGOI). The original data set was divided into the training set and test set after the screening and normalization of the variance expansion factor, accounting for 80% and 20%, respectively. The training set was used to train eight models, including classification and regression, logistic regression, k-nearest neighbor, adaptive boosting, gradient boosting decision tree, xgboost, random forest, and XGBRF. The five-fold cross-validation was used for each model to determine the optimal parameters. Finally, the model was verified to superimpose the actual fishing ground of the test set. The experimental results showed that: 1) There was a significant correlation between the catch per unit fishing effort and various variable factors. There was also a great decrease in the degree of collinearity between the variables that were filtered by variance inflation factor. 2) The XGBRF hybrid ensemble model also significantly improved the performance of XGBoost and RF models. Specifically, the highest accuracy rate and Area Under Curve (AUC) were 75.39%, and 79.48%, respectively. The Receiver Operator Characteristic (ROC) curve of the XGBRF model was closer to the upper left, indicating the best performance of the forecasting model than before. 3) The sea surface temperature was the most important factor to dominate the distribution of yellowfin tuna fishing ground, accounting for 7.573%. The temperature of the 300 m water layer was equally important for the yellowfin tuna, which was 7.369%. In addition, the greater impact was also found in the salinity of the 50-meter water layer, the SOI, the concentration of chlorophyll a, and the surface salinity. There was a relatively low influence of other large-scale climatic factors, except for the SOI. 4) There was only a small deviation between the fishing ground predicted by the XGBRF model and the actual fishing ground, indicating the high accuracy and reliability of the prediction. Overall, the XGBRF ensemble model performed the best on the fishing ground forecast of yellowfin tuna in the western and Central Pacific. The finding can also provide a strong reference for the fishing ground forecast. [ABSTRACT FROM AUTHOR]

In order to provide relevant information for natural resource management, impact assessment and marine spatial planning, high-resolution estimation of longline fishing gear is needed. Because the use of satellite vessel monitoring system (VMS) data is limited by data access and receiving offshore ship location information. In this paper, based on the data of automatic identification system (AIS) and logbook data of Chinese longline fishing from October to November 2017, a model of fishing detection based on support vector machine (SVM) learning method is established to classify the fishing and non fishing activities in the central and Western Pacific Ocean, to carry out longline fishing and draw fishing effort map in the Western and Central Pacific Ocean. Before constructing a fishing detection model, each AIS point was classified and pre-labeled as potential fishing and non-fishing events by an expert based on information on information on fisheries characteristics as obtained from literature, analysis of the tracks and logbook data. The performance of the fishing detection model was evaluated by the overall accuracy, precision, sensibility and specificity. Fishing intensities were computed from the known fishing positions and the estimated fishing positions were compared with correlation coefficient. The spatial correlation coefficient of fishing intensities and catch data was computed to quantify the extent to which the distribution of longline vessels describes tuna distribution. The results showed that the overall accuracy and the Kappa coefficient of the model training dataset were 95.24% and 0.9, respectively. The precision, sensibility and specificity were 94.74%, 93.44%, 96.56%, respectively. The overall accuracy and the Kappa coefficient of the model testing dataset were 93.85% and 0.87 respectively. The precision, sensibility and specificity were 93.49%, 90.67%, 95.91%, respectively. Then the constructed model was used to identify all the AIS data for 12 longline fishing vessels in the Western and Central Pacific in October and November 2017, with an overall accuracy rate of 83.3% and the Kappa coefficient was 0.67. The precision, sensibility and specificity were 82.33%, 88.32%, and 77.27%, respectively. The longline fishing distinct in October and November 2017 mainly located in 168°E-173°E, 12°S-18°S, and there were three obvious high fishing intensity areas on the map. The spatial distribution of fishing effort was significantly different between October and November. The distribution of fishing intensity in November was closer to the island than in October and the value of fishing intensity in October was lower than November. The fishing intensity information based on the SVM model and logbook data records was highly correlated (r>0.98). The spatial distribution characteristics of the fishing effort of the fishing vessel identified by the SVM model were similar to the known fishing positions. But the fishing intensities were calculated from known fishing positions was higher than that of estimated fishing positions. The spatial correlation coefficients of cumulative fishing effort and catch per unit of effort (CPUE), catch tail, catch weight and number of hooks were 0.68, 0.93, 0.93 and 0.94, respectively. The fishing capacity of fishing vessels based on AIS information mining can also be used as an alternative method for fishery resource analysis. [ABSTRACT FROM AUTHOR]