A Hybrid Time-Series Prediction of the Greater Riyadh's Metropolitan Area Expansion


  • Faizah Alshammari College of Computer Science and Engineering, Department of Computer Science and Artificial Intelligence, University of Jeddah, Saudi Arabia
  • Nahla Aljojo College of Computer Science and Engineering, Department of Information System and Technology, University of Jeddah, Saudi Arabia
  • Araek Tashkandi College of Computer Science and Engineering, Department of Information System and Technology, University of Jeddah, Saudi Arabia
  • Abdullah Alghoson College of Computer Science and Engineering, Department of Information System and Technology, University of Jeddah, Saudi Arabia
  • Ameen Banjar College of Computer Science and Engineering, Department of Information System and Technology, University of Jeddah, Saudi Arabia
  • Nidhal K. El Abbadi Computer and Engineering Techniques Department, College of Engineering and Techniques, Al-Mustqbal University, Iraq
Volume: 13 | Issue: 5 | Pages: 11890-11897 | October 2023 | https://doi.org/10.48084/etasr.6350


Riyadh is the most populous city in Saudi Arabia, with a population of over five million people. The governmental and economic centers of Saudi Arabia are located in the city. Due to the fact that the metropolitan region that surrounds Riyadh is continuously growing and expanding, appropriate planning is essential. To be able to formulate efficient plans, one needs access to trustworthy facts and information. Failing to have a clear picture of the future renders planning inefficient. Along with a hybrid time-series prediction of the expansion of the wider Riyadh metropolitan area, an urban growth forecasting model was constructed for the Riyadh region as part of this study. This model was used to make projections about the city's future population. This prediction was conducted with the application of Linear Regression (LR), Seasonal Auto-Regressive Integrated Moving Average (SARIMAX), and Auto-Regressive Integrated Moving Average (ARIMA). The dataset for this study consisted of satellite images of the region surrounding Riyadh that were acquired between 1992 and 2022. Mean Absolute Percentage Error (MAPE) was applied to measure the performance of the proposed hybrid models. The calculated MAPE vales are 2.0% for SARIMAX, 12% for LR, and 22% for ARIMA. As a consequence, the hybrid model's forecast for the future of the region suggests that the projections made regarding the expansion are keeping pace.


ARIMA, SARIMAX, urban-growth, logistic regression


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How to Cite

F. Alshammari, N. Aljojo, A. Tashkandi, A. Alghoson, A. Banjar, and N. K. El Abbadi, “A Hybrid Time-Series Prediction of the Greater Riyadh’s Metropolitan Area Expansion”, Eng. Technol. Appl. Sci. Res., vol. 13, no. 5, pp. 11890–11897, Oct. 2023.


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