STATISTICAL AND MACHINE-LEARNING APPROACHES FOR MAPPING BIGEYE TUNA FISHING OPTIMALITY IN THE BANDA SEA

Akhmatul Ferlin

Abstract


Given the complexity of the Banda Sea, which has rapidly changing environmental factors, studies are needed to determine the optimal location for important fishing, such as bigeye tuna (BET). To map optimal fishing locations, statistical and machine-learning methods are used through an understanding of the dynamics of chlorophyll-a (Chlo) concentrations and sea surface temperature (SST) and fish catch data as key factors. Using the fishing logbook and oceanographic data from 2014 to 2022, this study applies the methods of generalized Additive Model (GAM) and Empirical Cumulative Distribution Function (ECDF) to analyze the effect of Chlo and SSTs on Catch per Unit Efforts (CPUEs). Due to GAM's suboptimal performance, ECDF is chosen as the primary method. Determining the optimal value range using k-means and the elbow method showed an optimal Chlo range of 0.087–0.30 mg/m³ and SST of 29.46–30.45°C. The analysis showed the best catching conditions from January to March. These findings support sustainable fishing by providing a monthly map of optimal fishing zones, helping fishers adapt to dynamic conditions. Further research is suggested to integrate real-time data and advanced techniques to improve location accuracy.


Keywords


Banda Sea, Bigeye tuna, Fishing optimality, Machine Learning, Statistics

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DOI: http://dx.doi.org/10.15578/marlin.V6.I2.2025.131-150

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