A Deep Learning CNN-GRU-RNN Model for Sustainable Development Prediction in Al-Kharj City

This study introduces an advanced Deep Learning (DL) framework, the Convolutional Neural Network-Gated Recurrent Unit-Recurrent Neural Network (CNN-GRU-RNN). This model is engineered to forecast climate dynamics extending to the year 2050, with a particular focus on four pivotal scenarios: temperature, air temperature dew point, visibility distance, and atmospheric sea level pressure, specifically in Al-Kharj City, Saudi Arabia. To address the data imbalance problem, the Synthetic Minority Over-Sampling Technique was employed for Regression along with the Gaussian Noise (SMOGN). The efficacy of the CNN-GRU-RNN model was benchmarked against five regression models: the Decision Tree Regressor (DTR), the Random Forest Regressor (RFR), the Extra Trees Regressor (ETR), the Bayesian Ridge Regressor (BRR), and the K-Nearest Neighbors Regressor (KNNR). The models were evaluated using five distinct metrics: Mean Squared Error (MSE), Mean Absolute Error (MAE), Median Absolute Error (MedAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). The experimental outcomes demonstrated the superiority of the CNN-GRU-RNN model, which surpassed the traditional regression models across all four scenarios.