Faculty of Engineering

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Browsing Faculty of Engineering by Subject "Annual variation"

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    Assessment of Five Predictive Models for Solar Radiation in Southwest Nigeria
    (LivingScience Foundation, 2020-10) Dairo, Oluropo
    This study compares the accuracies of five predictive models for estimating solar radiation amongst other meteorological parameters in Southwest Nigeria. Twenty-one years of monthly averages of six measured meteorological parameters obtained from six stations in southwest Nigeria have been subjected to five mathematical models for prediction purposes. Solar radiation and sunshine hours have been modelled using the sum of two-Gaussians, the sum of two-Lorentzians, Fourier on four harmonics, sine wave and fourth-order polynomial functions. The fitting accuracies of these models were tested using performance indicators; mean bias error (MBE), root mean square error (RMSE), mean percentage error (MPE), standard error (SE) and the correlation coefficient (R). An evaluation of the models showed that the Gaussian and Lorentzian models are in good agreement with the observed data. However, the Fourier on the fourth harmonics model had the lowest MBE, RMSE and MPE, consequently highest correlation coefficient values, indicating high model accuracy. Thus, the Fourier model has the best correlation with the observed data and is recommended for estimating these variables in the selected locations.
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    Performance Analysis of Temperature Models for Environmental Monitoring in Southwest Nigeria
    (LivingScience Foundation, 2019) Dairo, Oluropo
    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.