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Estimation of Stratospheric Ozone Concentration over Southern Nigeria using Neural Network: Case Study of Lagos and Port Harcourt

C.C. Ekechukwu, J.A. Obu, E. Okoro, D.I. Okoh


The neural network model was developed to estimate the stratospheric ozone concentrations along with some meteorological parameters. The neural network was trained using monthly maximum data provided by Nigerian Meteorological Agency (NIMET) and National Aeronautics and Space Administration (NASA) over a period of 43year (1970-2012) in the towns of Lagos  and Port Harcourt  Ozone concentration from the two stations was estimated using a surface meteorological variable as predictors for the ANN. Based on the results, it was confirmed that Lagos station has the highest coefficient of correction of r = 0.85747 in comparison with Port Harcourt station. The sensitivity of the results indicated that Lagos station had the greatest maximum effect on the prediction of stratospheric ozone concentrations.

Keywords: Artificial neural network, mean square error, meteorological factors, regression, stratospheric ozone

Cite this Article

C.C. Ekechukwu, J.A. Obu, E. Okoro, D.I. Okoh. Estimation of Stratospheric Ozone Concentration over Southern Nigeria using Neural Network: Case Study of Lagos and Port Harcourt. Research & Reviews: Journal of Space Science & Technology. 2019; 8(1): 4–9p.


Stratospheric ozone, Artificial Neural Network, Meteorological factors, Regression, Mean square error.

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