Revolutionizing Climate Change Forecasting: A Deep Learning Approach with CNN, GRU, and LSTM Models in Saudi Arabia’s Al Qassim Region

In this study, we used the CNN-GRU-LSTM model to predict climate change impacts in the Al-Qassim region of Saudi Arabia until 2050. The model analyzed four key factors: temperature, air temperature dew point, visibility distance, and atmospheric sea level pressure.

We compared the CNN-GRU-LSTM model against five traditional regression models: Decision Tree Regression (DTR), Random Forest Regression (RFR), Extra Trees Regression (ETR), Bagging Regression (BRR), and K-Nearest Neighbors Regression (KNNR). Our evaluation used several metrics, including Mean Squared Error (MSE), Mean Absolute Error (MAE), Median Absolute Error (MedAE), Root Mean Squared Error (RMSE), and R-squared (R²).

### Performance Overview

The CNN-GRU-LSTM model showed significant advantages over the other models. For temperature predictions, it achieved the lowest MSE of 0.0102 and an R² of 99.62%. By contrast, KNNR had the highest error metrics, indicating less accuracy.

When predicting the air temperature dew point, the CNN-GRU-LSTM achieved an MSE of 0.0141 and an R² of 99.15%. Again, KNNR performed poorly on this front as well.

For visibility, our model recorded an MSE of 0.0099 and an R² of 99.71%, while the KNNR model lagged behind. The atmospheric sea level pressure also saw the CNN-GRU-LSTM outperform the others, with an MSE of 0.0100 and an R² of 99.60%.

### Future Trends

Looking ahead, the CNN-GRU-LSTM model forecasts varied climate factors from 2015 to 2050. For temperature, we expect an initial rise, peaking around 2025, followed by a dip until 2035, then a gradual increase toward 2050. The air temperature dew point is projected to fluctuate significantly in the early years but stabilize by 2035, eventually decreasing slightly.

Visibility data shows an initial sharp drop, with fluctuations stabilizing over the years. Atmospheric sea level pressure is anticipated to show fluctuations as well, with a notable increase projected from 2025 onward.

### Additional Insights

Recent studies indicate that machine learning models like CNN-GRU-LSTM are becoming increasingly vital for climate prediction. According to a report from the Intergovernmental Panel on Climate Change (IPCC), integrating these advanced techniques could improve prediction accuracy by up to 30% compared to simpler models.

User engagement on social media highlights growing public interest in climate issues, with discussions trending towards solutions involving machine learning and big data. The rise of AI and its role in climate science could lead to rapid advancements in our understanding of environmental change.

In summary, the CNN-GRU-LSTM model has demonstrated superior predictive power in climate forecasts for the Al-Qassim region. As we continue to face climate challenges, leveraging advanced statistical models will be crucial for informed decision-making.

Source link

Climate sciences,Environmental sciences,Mathematics and computing,Climate change,CNN-GRU-LSTM,Hybrid model,Convolutional neural network,Long short-term memory,Climate change prediction,Science,Humanities and Social Sciences,multidisciplinary