Weather Prediction with Feature Selection and Random Forest

Abstract

Accurate weather prediction is crucial for various sectors, including agriculture, disaster management, and aviation. Traditional weather prediction relies heavily on numerical weather prediction (NWP) models that simulate atmospheric processes using mathematical equations. While these methods have been the backbone of forecasting for decades, they often require significant computational resources and may need help capturing localized weather events. In contrast, machine learning models can quickly analyze large datasets to identify patterns and relationships that traditional methods might miss. This paper employs a Random Forest model to investigate the impact of feature selection on weather prediction accuracy. This paper identifies those critical to model performance by systematically excluding individual features, offering valuable insights into how ML can enhance traditional forecasting techniques. The results underscore the importance of features such as “Present_Tmin” and “LDAPS_LH”, which significantly influence model performance as measured by Root Mean Square Error (RMSE). This study contributes to a better understanding of feature selection in developing accurate weather prediction models. It lays the groundwork for future research to improve prediction accuracy through advanced techniques and expanded datasets.