Aplicación de métodos de aprendizaje profundo para la imputación de niveles de concentración de clorofila-a en la Costa Pacífica colombiana - Martínez-Vargas, Luis-Miguel; Muñoz-Ordóñez, Julián-Fernando; Solano-Correa, Yady-Tatiana (Revista Científica, 2024)
The fishing sector in Colombia, which contributes 0.3% of the Gross Domestic Product (GDP) and generates USD 45.1 million in exports (equivalent to 3.3% of the agricultural GDP), faces significant challenges due to the lack of precision in measuring chlorophyll-a, a crucial indicator of marine ecosystem health. The use of satellite images, particularly those obtained by the MODIS sensor, is essential for obtaining accurate data. However, the high cloud cover, which is common in Colombian geography, affects the quality and availability of these images for much of the year, creating gaps in critical data for assessing the state of marine ecosystems. This work proposes a deep learning algorithm based on time series for predicting missing chlorophyll-a values. The presented methodology overcomes the limitations imposed by cloud cover, achieving an R2 accuracy above 0.8 in one of the models. In this specific context, the implementation and evaluation of various deep learning models have proven to be effective alternatives in providing a more accurate and continuous assessment of fishing areas. This offers valuable information to improve the management and sustainability of the fishing sector in Colombia by adding a temporal component to the prediction of chlorophyll-a values, using data from up to three months prior to the target feature.