Time series model to forecast the surface temperature of the sea in the coastal area of Paita (Perú)
DOI:
https://doi.org/10.37467/revtechno.v11.4458Keywords:
El Niño Phenomenon, Sea Surface Temperature, Time series, Artificial Neural Network, PopulationAbstract
Artificial intelligence techniques have evolved and strengthened, allowing the development of transversal proposals that watch over and safeguard the integrity of the human being. The objective of this study is to develop a time series that forecasts the Sea Surface Temperature (SST) on an average daily scale in the coastal area of Paita, Perú. The methodology used focused on five phases, from data collection to model validation. The results obtained reveal that there is a margin of error of 3.96% on the SST on a weekly average scale and a difference of 0.05 to 1.42, on a daily basis.
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