Intelligent Weather Forecasting Using Hybrid CNN–VGG Feature Learning with Ensemble Models (Random Forest and AdaBoost) for Enhanced Spatiotemporal Prediction

The numerous weather forecast components in climatology dynamics and the quickly evolving technology of intelligent weather data processing complicate the situation of Weather prediction. To overcome the problem an advanced hybrid Machine Learning framework which is based on Deep Feature extraction technique with CNN-VGG and Ensemble learning technique is proposed in this paper to enhance the accuracy of the weather prediction system by adopting Random Forest (RF) and AdaBoost algorithms. Preprocessing techniques such as k-Nearest Neighbor (k-NN) imputation, Principal Component Analysis (PCA) and Recursive Feature Elimination (RFE) are also incorporated in the framework to further enhance the quality of the data and reduce the dimensionality of the data.

Experimental evaluation shows that our hybrid CNN–VGG feature extractor along with AdaBoost and Random Forest approach is very effective as compared to the baseline machine learning models. The performance of ensemble strategy involved in the generalization performance is improved, prediction error (RMSE, MAE) is decreased, robustness with the extreme weather variability is boosted. In conclusion, the results of the experimental evaluation confirm the effectiveness and intelligence of deep Spatial feature learning and the Adaptive Ensemble methods in predicting weather in a context of intelligent and scalable weather forecasting system.