Prediction of Daily Temperature Patterns in Iraq Using Deep Learning Models

The current study highlights the importance of accurate temperature prediction in Iraq, a country facing economic challenges due to its hot, arid climate and increasing climate change effects. Conventional forecasting methods, such as statistical and shallow machine learning models, struggle to address the complex time-dependent characteristics of meteorological data. The present study proposes to improve the temperature forecasting of the three large cities in Iraq, i.e., Dohuk, Erbil, and Mosul, using the deep learning models that can learn both short- and seasonal weather trends. A meteorological dataset of 24 years (2000-2024) was created with five major characteristics, namely, temperature, wind speed, relative humidity, total precipitation, and surface pressure. The models to be used in the deep learning model were three, namely (Long Short-term memory (LSTM), Gated Recurrent Unit (GRU), and Artificial Neural Network (ANN). The metrics of performance were Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), and R². The LSTM model performed the best in all the cities with RMSE values of 2.544, 2.366, and 2.323 and R² scores of 0.941, 0.948, and 0.952 in Dohuk, Erbil, and Mosul, respectively. The study confirms that LSTM is the most effective in modeling complex temporal dependencies in climatic time series, making it a significant contribution to understanding deep learning's application in weather forecasting in the Middle East. It suggests integrating AI-driven technology into the national meteorological system for climate-resistant decision-making in agricultural, water resource management, and urban development sectors.