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


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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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.

Article Details

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