Prediction of High-Abundance Fishing Grounds for Chub Mackerel (Scomber japonicus) in the Northwest Pacific Ocean and Its Environmental Drivers Based on Interpretable Machine Learning Model

Accurate prediction of fishing grounds plays a crucial role in supporting the efficient operation of ocean-going fishing vessels. Based on catch data of Chub Mackerel (Scomber japonicus) and multiple concomitant oceanographic variables from 2014 to 2022 in the Northwest Pacific Ocean, we employed four machine learning methods, including Random Forest (RF; scikit-learn v1.7.2), Extreme Gradient Boosting (XGBoost; xgboost v3.1.3), Light Gradient Boosting Machine (LightGBM; lightgbm v4.6.0) and Categorical Boosting (CatBoost; catboost v1.2.8), to construct a prediction model for high-abundance fishing grounds of Chub Mackerel. After selecting the optimal model through evaluation metrics, we applied the SHapley Additive exPlanations (SHAP; shap v0.44.1) method to visualize and interpret the optimal model, quantifying the importance of environmental factors on high-abundance fishing grounds, thus enhancing the interpretability and credibility of the machine learning model. The results indicated that the catch exhibited significant fluctuations at both interannual and intramonthly scales (p < 0.05). The annual catch showed a phased increasing trend, peaking in 2017 and 2018. Monthly catches were highest in September and October. Evaluated against established performance metrics, the RF model demonstrated the highest predictive performance with the highest values of accuracy and F1-score, 76.33% and 77.73%, Precision 72.81%, Recall 83.36%, ROC-AUC 0.8393, respectively, and was therefore selected as the most suitable for predicting Chub Mackerel fishing grounds. SHAP analysis identified the temporal variables year and month as the most influential predictors, followed by chlorophyll-a concentration (Chl-a), sea surface salinity (SSS), and sea surface temperature (SST). SHAP analysis can comprehensively reveal the degree and direction of influence of each variable at both global and local levels. These findings indicate that integrating machine learning with explainability techniques can enhance the scientific robustness and transparency of fishing ground forecasts, providing data-driven support for ecosystem-based fishery management.