Skipjack tuna was one of the most important and significant catches of fish in the world. That makes maintaining their existences critically important to human communities that rely on them for food and economic well-being, particularly at a time of global ocean change. The tuna distribution in broad geographic areas and high market value have gradually supported the political and economic importance of the various commercial activities related to this species group. Predicting a specific fish area for this species can help to optimize the catch and help make the monitoring more accessible. Studies about predicting fish area were commonly done by investigating the relation between skipjack tuna life preference with the marine environmental variables, such as Chlorophyll-a, Sea Surface Temperature, and so on. Satellite imagery often employed to obtain the value of these environmental variables due to its ability to monitor a large area and provide much more information than it would be possible to get solely from the surface.
This study used skipjack tuna catch data in the year 2017 and utilized Chlorophyll-a, Sea Surface Temperature (SST), and Sea Surface Height (SSH) as the environmental variables. Chlorophyll-a and SST was obtained from MODIS-Aqua Level 3 SMI while SSH was obtained from Global Ocean Analysis. Following the catch data, all the environmental data were from the year 2017. This study considered three multi-scale cases distinguished by temporal processing. The Case I processed with the yearly data in 2017, Case II processed seasonal data, and Case III processed weekly data. Two methods (Decision Tree, DT and Generalized Linear Model, GLM) were employed for predicting skipjack area. DT achieved mostly above 80% accuracy rate. Overall, based on the accuracy assessment, this study concluded that DT appears to perform better than GLM in predicting skipjack tuna fish area. Furthermore, this study also predicted the fish catch per unit effort (CPUE) using regression tree and GLM, both models were appropriate for prediction. Meanwhile, GLM (average RMSE is 11.452) performed slightly better than the regression tree (average RMSE is 10.878) in the monthly. Moreover, the most influenced environmental variable in both model construction was SST, which means the existence of skipjack tuna in one region is affected mostly by the regional temperature.

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