Enhancing The Effectiveness Of Weather Forecasting Using Ensemble Machine Learning Techniques

Abstract

Accurate weather forecasting is essential for managing the consequences of climate change and erratic weather conditions affecting various sectors such as agriculture, transportation, and public safety. This study presents a novel approach to improving weather prediction by integrating advanced machine learning techniques. The proposed model consists of Base Model 2 which combines Long Short-Term Memory (LSTM), eXtreme Gradient Boosting (XGBoost) using Ensemble approach. The output is then compared with Base Model 1 which combines K-Nearest Neighbor (KNN), Decision Tree (DT), Multi-Layer Perceptron (MLP) using Stacking Approach. The results from both Base Models are compared on the basis of accuracy and prediction time. The proposed methodology involves comprehensive data collection, rigorous preprocessing, feature selection, and development of an ensemble model using various machine learning models. The ability to capture intricate, nonlinear relationships in weather data is enhanced by this strategy, resulting in more precise and trustworthy forecasts. The model was evaluated using several metrics and achieved a high accuracy of 0.9608, MCC of 0.8850, and F1-score of 0.9602. The findings suggest that the integration of intelligent techniques significantly improves the accuracy and reliability of weather forecasting. Future research could extend this model to different regions and incorporate additional meteorological factors. This study contributes to the ongoing efforts to develop more sophisticated weather forecasting models, offering valuable insights for decision-makers in weather-sensitive industries.