A Deep Learning CNN-GRU-RNN Model for Sustainable Development Prediction in Al-Kharj City
Received: 12 October 2024 | Revised: 29 October 2024 | Accepted: 3 December 2024 | Online: 2 February 2025
Corresponding author: Ahmed M. Elshewey
Abstract
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.
Keywords:
climate change, deep learning, temperature, air temperature dew point, visibility distance, atmospheric sea level pressureDownloads
References
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