Fishing Area Prediction Using Scene-Based Ensemble Models

Abstract

This study utilized Chlorophyll-a, sea surface temperature (SST), and sea surface height (SSH) as the environmental variables to identify skipjack tuna catch hotspots. This study conducted statistical methods (decision tree, DT, and generalized linear model, GLM) as ensemble models that were employed for predicting skipjack area for each time slice. Using spatial historical data, each model was trained for one of the ensemble model sets. For prediction, the correlations of historical and new inputs were applied to select the predictive model. Using the scene-based model with the highest input correlation, this study further identified the fishing area of skipjack tuna in every case whether the alterations in their environment affected their abundance or not. Overall, the performance achieved over 83% for correlation coefficients (CC) based on the accuracy assessment. This study concluded that DT appears to perform better than GLM in predicting skipjack tuna fishing areas. Moreover, the most influential environmental variable in model construction was sea surface temperature (SST), indicating that the presence of skipjack tuna was primarily influenced by regional temperature.

An Ensemble Learning Technique for Predicting Mortality Rate in Red Tilapia (Oreochromis niloticus Linn.) Fingerlings

“…For this study, we opted for ensemble methods utilizing DT, DL, and NB models to enhance learning from imbalanced data. This study's conclusions were supported by various research works, such as Jardim et al (2021) advising on fisheries management; Alfatinah et al (2023) predicting skipjack area for each time slice; Chen et al (2023) projecting fish distribution in response to climate changes; and Khiem et al (2023) predicting the growth of abalone.…”