Towards A Smart City Concept - Machine Learning Smarts for the Estimation of Future Temperature Rise in Tabuk City

Eman Khaled Albalawi (1)
(1) Assistant Professor, Spatial Science, Department of Geography, Umm Al-Qura University, Makkah, Saudi Arabia, Saudi Arabia

Abstract

In the Middle Eastern peninsula especially in Saudi Arabia, there is a varsity temperature variation among the individual regions. As far as the city of Tabuk is concerned, no study has been conducted, regarding climate change (the temperature rise) in the Tabuk region and its implications on society and for the flagship “Future Smart Cities” concept. In this paper, machine learning algorithms are used to predict the future temperature values in the Tabuk region. The machine learning algorithms were trained on the data collected from the real-time weather radar stations of the region. Different features from the dataset are used for machine learning models to predict the future temperature. These unique features, for example, humidity and pressure, impact the accurate predictability of the temperature. Temperature prediction is modelled as a regression problem due to the nature of the data, therefore, different machine learning regression models were developed, i.e. Artificial Neural Network ANN-based techniques (Multi-layer Perceptron (MLP)), Decision Trees (DT), K-Nearest Neighbours (KNN), and Support Vector Regression (SVR). Encouragingly, the preliminary model evaluations utilizing Mean Absolute Error (MAE) yielded a high accuracy of 90% on the testing dataset. The findings are envisaged to inform decision-makers in the Climate and Weather Ministry of Tabuk City, potentially contributing to the advancement of the city's "Future Smart Cities" concept.

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References

Albalawi, E., Dewan, A., & Corner, R. (2018). Spatio-temporal analysis of land use and land cover changes in arid region of Saudi Arabia. GEOMATE Journal, 14(44), 73-81.‏

Albalawi, E., Dewan, A., & Corner, R. (2018). Predicting Spatio-Temporal Urban Growth in Tabuk, Saudi Arabia using a Cellular Automata-Markov Model. Proceedings of 156th IASTEM International Conference, Auckland, New Zealand, 5th-6th December.

Albalawi, E. (2020). Assessing and predicting the impact of land use and land cover change on groundwater using geospatial techniques: a case study of Tabuk, Saudi Arabia (Doctoral dissertation, Curtin University).‏

Alyami, S. H. (2019). Opportunities and challenges of embracing green city principles in Saudi Arabia’s future cities. Ieee Access, 7, 178584-178595

Anushka, P., MD, A. H., & UPAKA, R. (2020). Comparison of different artificial neural network (ANN) training algorithms to predict the atmospheric temperature in Tabuk, Saudi Arabia. Mausam, 71(2), 233-244.

Azari, B., Hassan, K., Pierce, J., & Ebrahimi, S. (2022). Evaluation of machine learning methods application in temperature prediction. Environ Eng, 8, 1-12.

Culpepper, J. B., & Gulrez, T. (2023, October). Machine learning approach for extracting radiometric data from RGB images: a preliminary study. In Target and Background Signatures IX (Vol. 12736, pp. 138-148). SPIE.

Cifuentes, J., Marulanda, G., Bello, A., & Reneses, J. (2020). Air temperature forecasting using machine learning techniques: a review. Energies, 13(16), 4215.‏

Di Nunno, F., Zhu, S., Ptak, M., Sojka, M., & Granata, F. (2023). A stacked machine learning model for multi-step ahead prediction of lake surface water temperature. Science of The Total Environment, 890, 164323

Fahimi Nezhad, E., Fallah Ghalhari, G., & Bayatani, F. (2019). Forecasting maximum seasonal temperature using artificial neural networks “Tehran case study”. Asia-Pacific Journal of Atmospheric Sciences, 55, 145-153.

Feigl, M., Lebiedzinski, K., Herrnegger, M., & Schulz, K. (2021). Machine-learning methods for stream water temperature prediction. Hydrology and Earth System Sciences, 25(5), 2951-2977.

Gulrez, T. (2021). Robots Used Today that we Did Not Expect 20 Years Ago (from the Editorial Board Members). Journal of Intelligent & Robotic Systems, 102(3).

Gulrez, T., & Kavakli, M. (2007, June). Precision position tracking in virtual reality environments using sensor networks. In 2007 IEEE International Symposium on Industrial Electronics (pp. 1997-2003). IEEE.

Gulrez, T., Kekoc, V., Verhagen, W., Ong, J., Williams, D., Marzocca, P., & Mills, T. (2021, January). A physical load metric development for assessment of mixed reality in aircraft inspection tasks. In Proceedings of the 19th Australian International Aerospace Congress (AIAC 2021) (pp. 167-172). Engineers Australia.

Gulrez, T., Kekoc, V., Gaurvit, E., Schuhmacher, M., & Mills, T. (2023, March). Machine Learning Enabled Mixed Reality Systems-For Evaluation and Validation of Augmented Experience in Aircraft Maintenance. In Proceedings of the 2023 7th International Conference on Virtual and Augmented Reality Simulations (pp. 77-83).

Geng, D., Zhang, H., & Wu, H. (2020). Short-term wind speed prediction based on principal component analysis and LSTM.

Hong, S., Park, C., & Cho, S. (2021). A rail-temperature-prediction model based on machine learning: warning of train-speed restrictions using weather forecasting. Sensors, 21(13), 4606.

Hou, J., Wang, Y., Zhou, J., & Tian, Q. (2022). Prediction of hourly air temperature based on CNN–LSTM. Geomatics, Natural Hazards and Risk, 13(1), 1962-1986.

Kramer, O. (2013). Dimensionality reduction with unsupervised nearest neighbors (Vol. 51, pp. 13-23). Berlin: Springer.

Krishna, L. V. (2014). Long term temperature trends in four different climatic zones of Saudi Arabia. Int. J. Appl, 4(5).‏

Mahmoud, S. H., Gan, T. Y., & Zhu, D. Z. (2023). Impacts of climate change and climate variability on water resources and drought in an arid region and possible resiliency and adaptation measures against climate warming. Climate Dynamics, 1-27.‏

Malakouti, S. M. (2023). Utilizing time series data from 1961 to 2019 recorded around the world and machine learning to create a Global Temperature Change Prediction Model. Case Studies in Chemical and Environmental Engineering, 7, 100312.

Moosavia, S. R., Woodb, D. A., & Samadanic, S. A. (2020). Modeling Performance of Foam-CO2 Reservoir Flooding with Hybrid Machine-learning Models Combining a Radial Basis Function and Evolutionary Algorithms. methods, 4, 5.

Papacharalampous, G.; Tyralis, H.; Koutsoyiannis, D (2018). Univariate Time Series Forecasting of Temperature and Precipitation with a Focus on Machine Learning Algorithms: A Multiple-Case Study from Greece. Water Resour. Manag, 32, 5207–5239.

Park, I., Kim, H. S., Lee, J., Kim, J. H., Song, C. H., & Kim, H. K. (2019). Temperature prediction using the missing data refinement model based on a long short-term memory neural network. Atmosphere, 10(11), 718.‏

Poh, C., Gulrez, T., & Konak, M. (2021, March). Minimal neural networks for real-time online nonlinear system identification. In 2021 IEEE Aerospace Conference (50100) (pp. 1-9). IEEE.

Qu, N., Liu, Y., Liao, M., Lai, Z., Zhou, F., Cui, P., Han, T., Yang, D., & Zhu, J. (2019). Ultra-high temperature ceramics melting temperature prediction via machine learning. Ceramics International, 45(15), 18551–18555. https://doi.org/10.1016/j.ceramint.2019.06.076

Quan, Q., Hao, Z., Xifeng, H. et al. Research on water temperature prediction based on improved support vector regression. Neural Comput & Applic 34, 8501–8510 (2022). https://doi.org/10.1007/s00521-020-04836-4

Rahayu, I. S., Djamal, E. C., Ilyas, R., & Bon, A. T. (2020). Daily temperature prediction using recurrent neural networks and long-short term memory. In Proceedings of the International Conference on Industrial Engineering and Operations Management (pp. 2700-2709).‏

Rezvani, S., de Almeida, N., & Falcão, M. (2023). Climate adaptation measures for enhancing urban resilience. Buildings, 13(9), 2163. https://doi.org/10.3390/buildings13092163

Sarkar, P. P., Janardhan, P., & Roy, P. (2020). Prediction of sea surface temperatures using deep learning neural networks. SN Applied Sciences, 2(8), 1458

Sharif, M. (2015). Analysis of projected temperature changes over Saudi Arabia in the twenty-first century. Arabian Journal of Geosciences, 8, 8795-8809.‏

Wolff, S., O’Donncha, F., & Chen, B. (2020). Statistical and Machine Learning Ensemble modelling to forecast sea surface temperature. Journal of Marine Systems, 208, 103347. https://doi.org/10.1016/j.jmarsys.2020.103347

Zhu, J. J., Yang, M., & Ren, Z. J. (2023). Machine learning in environmental research: common pitfalls and best practices. Environmental Science & Technology, 57(46), 17671-17689.

Authors

Eman Khaled Albalawi
[email protected] (Primary Contact)
Albalawi, E. (2024). Towards A Smart City Concept - Machine Learning Smarts for the Estimation of Future Temperature Rise in Tabuk City. Resourceedings, 4(1), 07–13. https://doi.org/10.21625/resourceedings.v4i1.1069

Article Details

Received 2024-02-27
Accepted 2024-03-24
Published 2024-03-31