Harnessing the Power of Stacked GRU for Accurate Weather Predictions

This research proposed a novel approach using Stacked GRU (Gated Recurrent Unit) models to address the problem of weather prediction and aimed to improve forecasting accuracy in sectors like agriculture, transportation, and disaster management. The key idea involved leveraging the temporal dependencies and memory management capabilities of Stacked GRU to model complex weather patterns effectively. Comprehensive data preprocessing ensured data quality and fine-tuning of the model architecture and hyperparameters optimized performance. The research demonstrated the Stacked GRU model's effectiveness in accurately forecasting temperature, pressure, humidity, and wind speed, validated by low RMSE and MAE scores and high R2 coefficients. However, challenges in forecasting humidity and a percentage discrepancy in wind speed predictions were observed. Overfitting and computational complexity were identified as potential limitations. Despite these constraints, the study concluded that the Stacked GRU model showed promise in weather forecasting and warranted further refinement for broader applications in time-series prediction tasks.