DOI:
https://doi.org/10.14483/23448350.22614Publicado:
08/30/2024Número:
Vol. 50 Núm. 2 (2024): Mayo-Agosto 2024Sección:
ArtículosAplicación de métodos de aprendizaje profundo para la imputación de niveles de concentración de clorofila-a en la Costa Pacífica colombiana
Applying Deep Learning Methods for the Imputation of Chlorophyll-a Concentration Levels in the Colombian Pacific Coast
Palabras clave:
Chlorophyll-a, aprendizaje profundo, cobertura nubosa, MODIS, predicción de valores perdidos, sector pesquero, series temporales (es).Palabras clave:
chlorophyll-a, cloud cover, deep learning, fishing sector, missing value prediction, MODIS, time series (en).Descargas
Resumen (es)
El sector pesquero en Colombia, que aporta el 0.3 % del Producto Interno Bruto (PIB) y genera exportaciones
por USD 45.1 millones (equivalente al 3.3 % del PIB agropecuario), enfrenta desafíos significativos debido a la
falta de precisión en la medición de la clorofila-a, un indicador crucial de la salud de los ecosistemas marinos.
El uso de imágenes satelitales, particularmente aquellas obtenidas por el sensor MODIS, es esencial para obtener
datos precisos. Sin embargo, la alta cobertura nubosa, común en la geografía colombiana, afecta la calidad y
la disponibilidad de estas imágenes durante gran parte del año, creando lagunas en los datos críticos para la
evaluación del estado de los ecosistemas marinos. Este trabajo propone un algoritmo de aprendizaje profundo
basado en series temporales para la predicción de valores perdidos de clorofila-a. La metodología presentada
supera las limitaciones impuestas por la cobertura nubosa, alcanzando una precisión R2 superior a 0.8 en uno de
los modelos. En este contexto específico, la implementación y la evaluación de diversos modelos de aprendizaje
profundo han demostrado ser alternativas efectivas para proporcionar una evaluación más precisa y continua de
las áreas pesqueras. Esto ofrece información valiosa para mejorar la gestión y sostenibilidad del sector pesquero
en Colombia al añadir un componente temporal a la predicción de valores de clorofila-a. Esto, mediante datos de
hasta tres meses previos a la característica objetivo.
Resumen (en)
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.
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Derechos de autor 2024 Luis-Miguel Martínez-Vargas, Julián-Fernando Muñoz-Ordóñez, Yady-Tatiana Solano-Correa
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