Weather Prediction Using Machine Learning Classifiers

Abstract

This study evaluates machine learning algorithms to increase weather predicting accuracy. However, traditional NWP models are computationally time-consuming and may fail to capture the nonlinear complexities inherent in meteorological observations. This research studies three machine learning algorithms—XGBoost, Gradient Boosting, and Random Forest. For the prediction of future conditions based on detailed patterns of past weather, these methods are adopted. The XGBoost model recorded an accuracy rate of 82.25% which is higher than the Random Forest and Gradient Boosting models. All models performed well in normal weather conditions such as "sun" and "rain," but were least competent at predicting less common phenomena like "drizzle," "fog," and "snow." This demonstrates the challenge of making predictions when unbalanced data sets are used in weather forecasting exercises. The incorporation of such machine learning techniques with conventional NWP models has the potential to result in improved prediction performance in various applications.