PCA-SWF: A Principal Component Analysis-Based Stacked Model Approach for Weather Forecasting

Abstract

Weather forecasting plays a crucial role across various sectors of society, enabling timely predictions of severe weather events such as hurricanes, tornadoes, storms, floods, and heatwaves. Accurate forecasts provide essential data for issuing public warnings, allowing individuals and authorities to take necessary precautions to safeguard lives and property. In agriculture, farmers rely on weather forecasts to optimize irrigation, planting, and harvesting schedules, ensuring efficient water resource management, maximizing crop yields, and minimizing damage caused by extreme weather. Additionally, weather predictions significantly impact transportation systems by providing insights into potential hazards such as ice, snow, poor visibility, wind speeds, and road conditions. This information helps railways, airlines, and shipping industries adjust schedules and ensure safe and efficient transport operations. In this study, we propose a weather prediction approach using a stacked model, which outperforms single classifiers such as Decision Trees, Support Vector Machines, K-Nearest Neighbors, and Multilayer Perceptrons. Principal Component Analysis (PCA) is employed for feature extraction to reduce dimensionality and improve prediction accuracy. The performance of the proposed model is evaluated using metrics such as accuracy, Matthews Correlation Coefficient (MCC), F1-score, and the Receiver Operating Characteristic (ROC) curve, along with the Area Under the Curve (AUC). The results demonstrate the effectiveness of the stacked model in achieving robust and reliable weather forecasting.