Data Reconstruction of Sea Surface Temperature in Indonesia’s Fish Management Area 713 (IFMA-713) Using Machine Learning

Abstract

The IFMA-713 in Indonesia is water that has dynamic of temperature changes due to interactions with the Pacific Ocean and the surrounding. Sea surface temperature data can be obtained by measuring with satellite imagery. However, satellite imagery measurements of sea surface temperature can be incomplete due to cloud cover. In this study, a machine learning method was used to reconstruct sea surface temperature data using a backpropagation neural network algorithm. The data used in this research is data captured with MODIS Satellite. Then, the reconstruction of sea surface temperature data is carried with four scenarios with missing data percentages: empty data, zero values, average values at the point of data collection, and Indonesia’s average sea surface temperature. Accurate results were obtained in reconstructing sea surface temperature where the scenarios had a positive correlation. The most accurate scenarios for reconstructing sea surface temperature data with missing data were those in which the empty data was filled with average values at the point of data collection or Indonesia’s average sea surface temperature.