Mapping anchovy species distribution and identifying potential fishing grounds in the Gulf of Thailand
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Abstract
The approach of this study utilized Random Forest Regression of 0.7443, Extreme Gradient Boosting (XGBoost) of 0.8800, and Extra Trees Regression (Extra Tree) of 0.7205. The results indicated that the XGBoost performed better than other approaches in accurately modeling species distribution. The consequence showed an R-squared (R2) ranging from 0.5574 to 0.8836, demonstrating strong predictive performance across different scenarios. Furthermore, the Root Mean Square Error (RMSE) varied between 0.4821 to 0.5153 kg catch weight, while the Mean Absolute Error (MAE) ranged from 0.1926 to 0.2293. In particular, the variables contributing to model accuracy. In conclusion, the findings of this study demonstrated that the spatiotemporal dynamics of the anchovies distribution in the Gulf of Thailand are found to be accurately represented through the XGBoost model. This capacity is required the ability to assess environmental variables discovered through satellite data to identify the probability of occurrence illustrated the spatial patterns of anchovies, demonstrating that variations in the environment influenced the distribution of anchovies. The results suggested that anchovy offers into the seasonal distribution. For fisheries management. The approach might help to reduce overfishing pressure in ecologically valuable regions by discovering suitable fishing grounds in the Gulf of Thailand.
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