Climate Zonation in Puerto Rico Based on Principal Components Analysis and an Artificial Neural Network

Abstract The authors analyzed climate data, seasonal averages of precipitation, and maximum, mean, and minimum temperatures over the years 1960–90, from 18 stations spread around the island of Puerto Rico in the Caribbean, to determine whether these distinguish the existence of climate zones in Puerto Rico. An R-mode principal components analysis (PCA), with varimax rotation to the seasonal data in order to reduce their dimensionality, was applied. The first five principal components, found by cross validation to be statistically significant, account for 99% of the variability in the 16 variables included in the analysis. These five components are related to annual variation in mean and minimum temperature (first PC), annual maximum temperature (second PC), and spring, summer, and fall precipitation (third through fifth PCs). A self-organizing map, an artificial neural network algorithm, was then employed to classify the first five PC scores in an optimal fashion. The scores were classified by the neural ...

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