Forecasting Karachi's Air Temperature Variation: Leveraging Mobile and Multimedia Dataset for Global Warming Insights

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外文摘要：In Karachi, the transport and building industries have a major impact on carbon dioxide (CO_2) emissions and greenhouse gas emissions. This study is innovative in that it examines how air temperature changes over time and forecasts the maximum and minimum temperatures in Karachi. Specifically, it looks at 60 years' worth of mean monthly maximum and minimum air temperatures in Karachi, spanning from (1961 to 2020) dataset classes. The monthly average air temperature in Karachi, both at its minimum and maximum, remains constant. Using dataset from the Pakistan Metrological Department (PMD) and, the ARMA (p, q) model technique was used to assess forecasting and modelling the behavior of the maximum and minimum air temperatures in Karachi. The least values Akaike information criterion (AIC), the Bayesian Schwarz information criterion (SIC), and the Hannan Quinn information criterion (HIC) are used to describe how adequate the model is. Additionally, the Durbin-Watson (DW) test is used. The average monthly maximum and minimum air temperature in Karachi are strongly correlated, as indicated by DW values (<2). The lowest and maximum monthly average air temperatures in Karachi are predicted using diagnostic checking techniques such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Theil's U-Statistics. The results indicate that there is a strong correlation between the air temperature and previously measured values, with Theil's U-Statistics values for each month lying close to zero. This study is a great resource for observing how air temperature affects global warming. This study also highlights the the innovative aspect of utilizing vast datasets, possibly including those from mobile and multimedia sources, to address a critical environmental issue. It also emphasizes the predictive modelling aspect of your study, which is central to understanding and mitigating global warming effects.

外文关键词：

ARMA (p,q) modelair temperatureroot mean square errorskewnesskurtosis

作者：

Asma Zaffar、Shakil Ahmed、Aysha Rafique、Muhammad Imran、Muhammad Amir、Vali Uddin

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作者单位：

Department of Mathematics and Sciences, Sir Syed University of Engineering & Technology, Karachi, Pakistan

Department of Computer Engineering, Sir Syed University of Engineering & Technology, Karachi, Pakistan

Department of Telecommunication Engineering, Sir Syed University of Engineering & Technology, Pakistan

Department of Civil Engineering, Sir Syed University of Engineering &Technology, Karachi, Pakistan

Computer and Electrical Engineering, Sir Syed University of Engineering & Technology, Karachi, Pakistan

Sir Syed University of Engineering & Technology,Karachi, Pakistan

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出版年：

2024

DOI：

10.13052/jmm1550-4646.2065

Journal of mobile multimedia

ISSN：1550-4646

年,卷(期)：2024.20(6)