An Effective Forecasting Approach of Temperature Enabling Climate Change Analysis in Saudi Arabia

Climate change is a global issue with far-reaching consequences, and understanding regional temperature patterns is critical for effective climate change analysis. In this context, accurate forecasting of temperature is critical for mitigating and understanding its impact. This study proposes an effective temperature forecasting approach in Saudi Arabia, a region highly vulnerable to climate change's effects, particularly rising temperatures. The approach uses advanced neural networks models such as the Long Short-Term Memory (LSTM), Gate Recurrent Unit (GRU), and Bidirectional LSTM (BiLSTM) model. A comparative analysis of these models is also introduced to determine the most effective model for forecasting the mean values of temperatures in the following years, understanding climate variability, and informing sustainable adaptation strategies. Several experiments are conducted to train and evaluate the models on a time series data of temperatures in Saudi Arabia, taken from a public dataset of countries' historical global average land temperatures. Performance metrics such as Mean Absolute Error (MAE), Mean Relative Error (MRE), Root Mean Squared Error (RMSE), and coefficient of determination (R-squared) are employed to measure the accuracy and reliability of each model. Experimental results show the models' ability to capture short-term fluctuations and long-term trends in temperature patterns. The findings contribute to the advancement of climate modeling methodologies and offer a basis for selecting a suitable model in similar environmental contexts.