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Forecasting and Modelling the Impact of Air Temperature on Global Warming: Karachi as a Case Study

. Asma Zaffar, Muhammad Imran, Valiuddin and Muhammad Aamir


Abstract

The world has been experienced extreme global warming and climate changes and reason to increase temperature, rising sea level, droughts, and flooding. The transportation and construction activities in Karachi affected the Green House Gases (GHG) mostly and carbon dioxide (CO2) emission. The novelty of this paper to study the impact the temperature over time and forecast the maximum and minimum Karachi temperature, in this regard 60 years mean monthly maximum and minimum air temperature of Karachi ranging from 1961 to 2020 data under consideration. The minimum and maximum Karachi monthly average air temperatures are stationary. ARMA (p, q) model technique applied to evaluate forecasting and modelling the behaviour of Karachi maximum and minimum air temperature using Pakistan Metrological Department (PMD) data. The adequacy of model describes via least values Akaike information criterion (AIC), Bayesian Schwarz information criterion (SIC) and Hannan Quinn information criterion (HIC). Durbin- Watson (DW) test is also applied. DW values (< 2) shows that all month’s average maximum and minimum Karachi air temperature are strongly correlated. Diagnostic checking tests like Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Theil’s U-Statistics are used to predict minimum and maximum Karachi monthly average air temperatures. Theil’s U-Statistics values of each month lie near to zero in the results which are showed that the air temperature is strongly correlated to previously observed values. This study is very helpful to observe the impact of air temperature on the global warming.

 

 

Keyword: ARMA (p, q) model, air temperature, Root Mean Square Error, Skewness, Kurtosis.

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