International Journal of Education and Management Engineering(IJEME)

ISSN: 2305-3623 (Print), ISSN: 2305-8463 (Online)

Published By: MECS Press

IJEME Vol.8, No.5, Sep. 2018

A Model for Implementing Temperature Information Systems in South-east Nigeria

Full Text (PDF, 521KB), PP.51-64

Views:25   Downloads:0


Anthony T. Umerah, Eric C. Okafor

Index Terms

Temperature information systems;Forecasting models;F-value;Mean Square Error;Root Mean Square Error;Temperature forecasting


The aim of this study is to find an efficient and robust model for building temperature information systems in South-East Nigeria. The study obtained daily mean temperature data records for a period of 10years of the capture cities of Enugu, Abakaliki and Owerri, and applied the data to several forecasting models: 3 & 4 point moving averages (MA), the Single Exponential Smoothing (SES) and the time dependent regression model for intercept and non-intercept models as well as linear and non-linear models. The comparison of various forecasting models was made based on the following performance evaluation methods: F-values, Mean Square Error (MSE) and Root Mean Square Error (RMSE) where applicable. The findings show that the power model with statistical characteristics of F-values = 1513.71(Enugu), 1523.622(Abakaliki) and 1514.103(Owerri), MSE = 0.655(Enugu), 0.6495(Abakaliki), and 0.5925(Owerri), and RMSE = 0.80524(Enugu), 0.80292(Abakaliki) and 0.76703(Owerri), is the best model for temperature information systems because of its consistency in minimizing errors, and largeness of F-values. This is followed by the single exponential smoothing technique and logarithmic model. This study therefore presents and recommends the power regression model as the most robust model for temperature forecasting in South-East Nigeria.

Cite This Paper

Anthony T. Umerah, Eric C. Okafor,"A Model for Implementing Temperature Information Systems in South-east Nigeria", International Journal of Education and Management Engineering(IJEME), Vol.8, No.5, pp.51-64, 2018.DOI: 10.5815/ijeme.2018.05.06


[1]J. A. Nwanta, S. V. O. Shoyinka, K. F. Chah et al., "Production characteristics, disease prevalence, and herd-health management of pigs in Southeast Nigeria "J Swine Health Prod., vol. 19, no. 6, pp. 331–339, 2011.

[2]J. C. Okafor and E.C.M. Fernandes, "Compound farms of south-eastern Nigeria: A predominant agroforestry homegarden system with crops and small livestock" Agroforestry Systems vol. 5, issue 2, pp. 153-168, 1987.

[3]A. H. Igweze, M. N. Amagoh and A. N. Ashinze, "Analysis of rainfall variations in the Niger Delta region of Nigeria", Journal of Environmental Science, Toxicology and Food Technology, vol. 8, issue 1, ver. vi, pp. 25-30, 2014.

[4]E. C. Onyenechere, "Climate change and spatial planning concerns in Nigeria: Remedial measures for more effective response", Journal of Human Ecology, vol. 32, no. 3, pp. 137-148, 2010.

[5]A. Ogbo, E. L. Ndubuisi and W. Ukpere, "Risk management and challenges of climate change in Nigeria", Journal of Human Ecology, vol. 41, no. 3, pp. 221-235, 2013.

[6]L. Olatomiwa, S. Mekhilef, S. Shamshirband, et al., "Adaptive neuro-fuzzy approach for solar radiation prediction in Nigeria", Renewable and Sustainable Energy Reviews, vol. 51, pp.1784-1791, 2015.

[7]G. F. Ibeh, G. A. Agbo, P. E. Agbo et al., "Application of Artificial Neural Networks for Global Solar Radiation Forecasting With Temperature", Advances in Applied Science Research, vol. 3, no. 1, pp. 130-134, 2012.

[8]U. M. Bibi, J. Kaduk and H. Balzter, "Spatial-Temporal Variation and Prediction of Rainfall in Northeastern Nigeria", Climate, vol.2, no.3, pp.206-222, 2014.

[9]B. M. Al-Maqaleh, A. A. Al-mansoub and F. N. Al-Badani, "Forecasting using Artificial Neural Network and Statistics Models" International Journal of Education and Management Engineering, vol. 3, pp. 20-32, 2016.

[10]F. Olaiya and A. B. Adeyemo, "Application of Data Mining Techniques in Weather Prediction and Climate Change Studies" International Journal of Information Engineering and Electronic Business, vol. 1, pp.51-59, 2012.

[11]H. Bulut, O. Buyukalaca and T. Yilmaz, "New models for simulating daily minimum, daily maximum and hourly outdoor temperatures", In Proceedings of the first international exergy, energy and environment symposium (IEEES-1), Izmir, Turkey, pp. 499–504, 2003.

[12]C. C. Raible, G. Bischof, K. Fraedrich et al., "Statistical single-station short-term forecasting of temperature and probability of precipitation: Area interpolation and NWP combination", Weather and Forecasting, vol. 14, no. 2, pp. 203-214, 1999.<0203:SSSSTF>2.0.CO;2

[13]D. Zhou, "Time Series Forecasting Model Based on Discrete Grey LS-SVM" International Journal of Intelligent Systems and Applications, vol. 02, pp. 27-33, 2015. /10.5815/ijisa.2015.02.04