This research addressed the problem of forecasting an average monthly temperature, maximum and minimum with the application on the governorate of Cairo, in the period from January 1961 to December 2007. Two statistical methods are used; Multi-responses Regression Analysis (MRRA) and Artificial Neural Networks (ANN) method of Back Propagation. Where the dependent variables are the average monthly temperatures, maximum and minimum, while the independent variables are the air pressure, relative humidity, periods of sunshine, wind speed, the amount of evaporation (variable reflects the phenomenon of global warming), the amount of monthly rainfall. The research found that the neural networks method gives more accurate forecasting comparing with Multi-Responses Regression Analysis, and the variable amount of evaporation does not substantially affect the average temperature maximum or minimum
M. Abdel Aal, Medhat Dr; F. Aziz, Essam Dr; and H.I. Qansuh, Ihab
"Proposed a Statistical Model to Predict Average Monthly Temperatures and the Extent Affected by the Phenomenon of Global Warming in Cairo Governorate,"
Arab Journal of Administration المجلة العربية للإدارة: Vol. 35
, Article 21.
Available at: https://digitalcommons.aaru.edu.jo/aja/vol35/iss1/21