Climate change prediction in Saudi Arabia using a CNN GRU LSTM hybrid deep learning model in al Qassim region.

Climate change, which causes long-term temperature and weather changes, threatens natural ecosystems and cities. It has worldwide economic consequences. Climate change trends up to 2050 are predicted using the hybrid model that consists of Convolutional Neural Network-Gated Recurrent Unit-Long Short-Term Memory (CNN-GRU-LSTM), a unique deep learning architecture. With a focus on Al-Qassim Region, Saudi Arabia, the model assesses temperature, air temperature dew point, visibility distance, and atmospheric sea-level pressure. We used Synthetic Minority Over-sampling Technique for Regression with Gaussian Noise (SMOGN) to reduce dataset imbalance. The CNN-GRU-LSTM model was compared to 5 classic regression models: DTR, RFR, ETR, BRR, and K-Nearest Neighbors. Five main measures were used to evaluate model performance: MSE, MAE, MedAE, RMSE, and R². After Min-Max normalization, the dataset was split into training (70%), validation (15%), and testing (15%) sets. The paper shows that the CNN-GRU-LSTM model beats standard regression methods in all four climatic scenarios, with R² values of 99.62%, 99.15%, 99.71%, and 99.60%. Deep learning predicts climate change well and can guide environmental policy and urban development decisions.