Estimation of Potential Fishing Grounds Using the Random Forest Machine Learning Algorithm with MODIS Satellite Data
DOI:
https://doi.org/10.63639/bedws371Keywords:
Random Forest, Potential Fishing Zone , MODIS-Aqua, Oceanography, Carbon EmissionsAbstract
North Sumatra Province, located within Indonesia’s Fisheries Management Area (WPP) 571, has substantial marine capture fisheries potential along its eastern coastline. However, increasing fishing intensity has led to higher fuel consumption and carbon emissions from fishing vessels, emphasizing the need for spatially efficient fishing strategies. This study aims to estimate and validate potential fishing zones (PFZ) using a Random Forest machine learning algorithm integrated with MODIS-Aqua satellite data to enhance operational efficiency and support emission mitigation. Four oceanographic parameters were employed as predictor variables: Sea Surface Temperature (SST), chlorophyll-a concentration (Chl-a), normalized fluorescence line height (nFLH), and particulate organic carbon (POC), covering the period 2013–2020. Ecological threshold values were applied to generate PFZ and Non-PFZ labels, and the dataset was divided into 80% training and 20% testing subsets using stratified random sampling. Model performance was evaluated using confusion matrix analysis, precision, recall, F1-score, and Cohen’s Kappa coefficient. Results indicate that all variables were normally distributed and significantly correlated (R > 0.5; R² > 0.7). The Random Forest model achieved consistently high predictive accuracy, exceeding 90% across all evaluated years. Compared to a conventional threshold-based method, the machine learning approach produced more stable and spatially consistent PFZ delineations by capturing nonlinear environmental interactions. This study demonstrates that integrating satellite oceanography and machine learning provides a robust, data-driven framework for improving fishing efficiency while contributing to carbon emission reduction in capture fisheries management.
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