Support vector machine learning algorithm for the prediction of atmospheric air temperature using historical weather data

Abstract

The Support Vector Machine (SVM) learning algorithm has emerged as a robust tool for predicting atmospheric air temperature from historical weather data, showcasing its potential in tackling complex meteorological challenges. Recent trends in environmental predictions emphasize the importance of advanced machine learning techniques over traditional methods, highlighting SVM's ability to handle dynamic atmospheric data and provide more accurate forecasts compared to conventional statistical approaches. By incorporating diverse weather features and real-time data, SVM significantly improves the reliability of temperature predictions, crucial for effective environmental management amidst urbanization-induced climate impacts. The efficacy of SVM extends beyond temperature forecasting to applications like predicting hazardous flight conditions and black ice events, demonstrating high accuracy rates in analyzing meteorological parameters. As SVM evolves and adapts, its role in temperature prediction is expected to expand, enhancing safety and operational efficiency in aviation and other sectors reliant on precise weather forecasting. The advancements in predictive accuracy through SVM represent a pivotal step towards understanding and mitigating the effects of climate change, underscoring its growing significance in environmental modeling and policy-making.