Performance Analysis of Temperature Models for Environmental Monitoring in Southwest Nigeria
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Date
2019
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Publisher
LivingScience Foundation
Abstract
Temperature is a major meteorological parameter driving most of the atmospheric processes vis-a`-vis climate change. Therefore, a consistent model is necessary to achieve sustainable development goal 13 (SDG 13) known as climate action. Long-term monthly averages of surface temperature obtained from six southwest states in Nigeria were subjected to five mathematical models, namely the sum of two-Gaussians, the sum of two-Lorentzians, Fourier on four harmonics, Sine wave and Fourth-order polynomial functions. Statistical tools were used to examine the accuracy and fitness of the models. The evaluation showed that the Gaussian and Lorentzian models are good fits for the observed data. Furthermore, the performance indicators such as mean bias error (MBE), root mean square error (RMSE) and mean percentage error (MPE) recorded the lowest values for Fourier on the fourth harmonic model. Similarly, its correlation coefficient, R, was the highest ranging from 0.95 to 1. Consequently, the Fourier model presented the best correlation with the observed data and hence was recommended for predicting the temperature at the selected locations.
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Keywords
Temperature models, Predictive models, Surface temperature, Statistical test, Annual variation
Citation
Willoughby, A.A. Ndubuisi, A.O., Dairo, O.F., Aizebheokhai, A.P. and Oludotun, J.O. (2019) Performance Analysis of Temperature Models for Environmental Monitoring in Southwest Nigeria. Nigerian Journal of Environment and Health, Vol. 2(1), 65 – 74.