Short Term Weather Forecasting Using Machine Learning Approaches

Abstract

This paper explores the application of machine learning techniques for short-term weather prediction, aiming to enhance the accuracy and reliability of weather forecasts. With the increasing availability of meteorological data and advancements in computational power, machine learning offers promising methods for analyzing complex weather patterns. We investigate various machine learning algorithms, including decision trees, support vector machines, and neural networks, to predict key weather parameters such as temperature, humidity, and precipitation. The study leverages historical weather data, satellite imagery, and real-time sensor inputs to train and validate the models. Comparative analysis highlights the strengths and limitations of each algorithm in different weather scenarios. Our results demonstrate that machine learning models can significantly improve short-term weather prediction, providing valuable insights for meteorologists, policymakers, and the public. This research contributes to the growing field of data-driven weather forecasting, proposing robust methodologies for enhancing predictive accuracy and supporting effective decision-making.