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


Download data is not yet available.


L. Bursztyn, A. L. González, and D. Yanagizawa-Drott, "Misperceived Social Norms: Women Working Outside the Home in Saudi Arabia," American Economic Review, vol. 110, no. 10, pp. 2997–3029, Oct. 2020.

O. H. Sayed and Y. S. Masrahi, "Climatology and phytogeography of Saudi Arabia. A review," Arid Land Research and Management, vol. 37, no. 3, pp. 311–368, Jul. 2023.

M. D. Alotaibi et al., "Assessing the response of five tree species to air pollution in Riyadh City, Saudi Arabia, for potential green belt application," Environmental Science and Pollution Research, vol. 27, no. 23, pp. 29156–29170, Aug. 2020.

United Nations, Department of Economic and Social Affairs, Population Division, World Urbanization Prospects - The 2018 Revision. New York, NY, USA: United Nations, 2019.

M. Arshad, K. M. Khedher, E. M. Eid, and Y. A. Aina, "Evaluation of the urban heat island over Abha-Khamis Mushait tourist resort due to rapid urbanisation in Asir, Saudi Arabia," Urban Climate, vol. 36, Mar. 2021, Art. no. 100772.

S. Awasthi, "‘Hyper’-Urbanisation and migration: A security threat," Cities, vol. 108, Jan. 2021, Art. no. 102965.

S. Dinda, K. Das, N. D. Chatterjee, and S. Ghosh, "Integration of GIS and statistical approach in mapping of urban sprawl and predicting future growth in Midnapore town, India," Modeling Earth Systems and Environment, vol. 5, no. 1, pp. 331–352, Mar. 2019.

S. Ghosh, "A city growth and land-use/land-cover change: a case study of Bhopal, India," Modeling Earth Systems and Environment, vol. 5, no. 4, pp. 1569–1578, Dec. 2019.

K. T. Deribew, "Spatiotemporal analysis of urban growth on forest and agricultural land using geospatial techniques and Shannon entropy method in the satellite town of Ethiopia, the western fringe of Addis Ababa city," Ecological Processes, vol. 9, no. 1, Sep. 2020, Art. no. 46.

K. Malarvizhi, S. V. Kumar, and P. Porchelvan, "Urban sprawl modelling and prediction using regression and Seasonal ARIMA: a case study for Vellore, India," Modeling Earth Systems and Environment, vol. 8, no. 2, pp. 1597–1615, Jun. 2022.

L. Xu, X. Liu, D. Tong, Z. Liu, L. Yin, and W. Zheng, "Forecasting Urban Land Use Change Based on Cellular Automata and the PLUS Model," Land, vol. 11, no. 5, 2022.

W. Boulila, H. Ghandorh, M. A. Khan, F. Ahmed, and J. Ahmad, "A novel CNN-LSTM-based approach to predict urban expansion," Ecological Informatics, vol. 64, Sep. 2021, Art. no. 101325.

X. Liu, X. Wang, K. Chen, and D. Li, "Simulation and prediction of multi-scenario evolution of ecological space based on FLUS model: A case study of the Yangtze River Economic Belt, China," Journal of Geographical Sciences, vol. 33, no. 2, pp. 373–391, Feb. 2023.

B. Mahdavi Estalkhsari, P. Mohammad, and A. Karimi, "Land Use and Land Cover Change Dynamics and Modeling Future Urban Growth Using Cellular Automata Model Over Isfahan Metropolitan Area of Iran," in Ecological Footprints of Climate Change : Adaptive Approaches and Sustainability, U. Chatterjee, A. O. Akanwa, S. Kumar, S. K. Singh, and A. Dutta Roy, Eds. Cham, Switzerland: Springer International Publishing, 2022, pp. 495–516.

M. Shrestha, C. Mitra, M. Rahman, and L. Marzen, "Mapping and Predicting Land Cover Changes of Small and Medium Size Cities in Alabama Using Machine Learning Techniques," Remote Sensing, vol. 15, no. 1, 2023.

A. E. AlDousari, A.-A. Kafy, M. Saha, Md. A. Fattah, A. Bakshi, and Z. A. Rahaman, "Summertime Microscale Assessment and Prediction of Urban Thermal Comfort Zone Using Remote-Sensing Techniques for Kuwait," Earth Systems and Environment, vol. 7, no. 2, pp. 435–456, Jun. 2023.

D. Strumsky, L. Bettencourt, and J. Lobo, "Agglomeration effects as spatially embedded social interactions: identifying urban scaling beyond metropolitan areas," Environment and Planning B: Urban Analytics and City Science, Jan. 2023, Art. no. 23998083221148200.

H. B. Akdeniz, N. S. Sag, and S. Inam, "Analysis of land use/land cover changes and prediction of future changes with land change modeler: Case of Belek, Turkey," Environmental Monitoring and Assessment, vol. 195, no. 1, Nov. 2022, Art. no. 135.

Y. Gao, Z. Shen, Y. Liu, C. Yu, L. Cui, and C. Song, "Optimization of differentiated regional land development patterns based on urban expansion simulation—A case in China," Growth and Change, vol. 54, no. 1, pp. 45–73, 2023.

T. He, Y. Lu, W. Yue, W. Xiao, X. Shen, and Z. Shan, "A new approach to peri-urban area land use efficiency identification using multi-source datasets: A case study in 36 Chinese metropolitan areas," Applied Geography, vol. 150, Jan. 2023, Art. no. 102826.

D. Q. Rutan, P. Hepburn, and M. Desmond, "The Suburbanization of Eviction: Increasing Displacement and Inequality Within American Suburbs," RSF: The Russell Sage Foundation Journal of the Social Sciences, vol. 9, no. 1, pp. 104–125, Feb. 2023.

A. A. Salam, "Ageing in Saudi Arabia: new dimensions and intervention strategies," Scientific Reports, vol. 13, no. 1, Mar. 2023, Art. no. 4035.

R. H. Shumway and D. S. Stoffer, Time Series Analysis and Its Applications: With R Examples. Cham, Switzerland: Springer International Publishing, 2017.

F. Marandi and S. M. T. Fatemi Ghomi, "Time series forecasting and analysis of municipal solid waste generation in Tehran city," in 2016 12th International Conference on Industrial Engineering (ICIE), Tehran, Iran, Jan. 2016, pp. 14–18.

H. Alabdulrazzaq, M. N. Alenezi, Y. Rawajfih, B. A. Alghannam, A. A. Al-Hassan, and F. S. Al-Anzi, "On the accuracy of ARIMA based prediction of COVID-19 spread," Results in Physics, vol. 27, Aug. 2021, Art. no. 104509.

P. Manigandan et al., "Forecasting Natural Gas Production and Consumption in United States-Evidence from SARIMA and SARIMAX Models," Energies, vol. 14, no. 19, 2021.

D. Maulud and A. M. Abdulazeez, "A Review on Linear Regression Comprehensive in Machine Learning," Journal of Applied Science and Technology Trends, vol. 1, no. 4, pp. 140–147, Dec. 2020.

A. R. Ramadhan, M. Choi, Y. Chung, and J. Choi, "An Empirical Study of Segmented Linear Regression Search in LevelDB," Electronics, vol. 12, no. 4, 2023.

E. Vivas, H. Allende-Cid, and R. Salas, "A Systematic Review of Statistical and Machine Learning Methods for Electrical Power Forecasting with Reported MAPE Score," Entropy, vol. 22, no. 12, 2020.

M. Ahmadi and M. Ghamary Asl, "Monitoring urban growth in Google Earth Engine from 1991 to 2021 and predicting in 2041 using CA-MARKOV and geometry: case study—Tehran," Arabian Journal of Geosciences, vol. 16, no. 2, Jan. 2023, Art. no. 107.

S. M. Abdullah et al., "Optimizing Traffic Flow in Smart Cities: Soft GRU-Based Recurrent Neural Networks for Enhanced Congestion Prediction Using Deep Learning," Sustainability, vol. 15, no. 7, 2023.

U. Iftikhar, K. Asrar, M. Waqas, and S. A. Ali, "Evaluating the Performance Parameters of Cryptographic Algorithms for IOT-based Devices," Engineering, Technology & Applied Science Research, vol. 11, no. 6, pp. 7867–7874, Dec. 2021.

N. B. Serradj, A. D. K. Ali, and M. E. A. Ghernaout, "A Contribution to the Thermal Field Evaluation at the Tool-Part Interface for the Optimization of Machining Conditions," Engineering, Technology & Applied Science Research, vol. 11, no. 6, pp. 7750–7756, Dec. 2021.

M. U. Farooq, A. Ahmed, S. M. Khan, and M. B. Nawaz, "Estimation of Traffic Occupancy using Image Segmentation," Engineering, Technology & Applied Science Research, vol. 11, no. 4, pp. 7291–7295, Aug. 2021.


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.


Abstract Views: 342
PDF Downloads: 250

Metrics Information

Most read articles by the same author(s)