Eman AlBalawi

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., Multi-layer Perceptron (MLP) or Artificial Neural Network (ANN), Decision Trees (DT), K-Nearest Neighbours (KNN), and Support Vector Regression (SVR). The preliminary results shown in this paper are encouraging and produced 90% accuracy on the testing dataset. We envisage that the findings will inform decision-makers of the Climate and Weather Ministry of Tabuk City, eventually providing a step towards the Smart City’s Concept in Future.