MACHINE LEARNING-BASED WEATHER FORECASTING USING TIME SERIES ANALYSIS

Abstract

The purpose of this research is to investigate the use of machine learning techniques to improve the accuracy of weather forecasts by making use of previous historical meteorological data. Many industries rely on accurate weather predictions, including farming, transportation, emergency preparedness, and ecological design. The research looks for trends in weather by analyzing important meteorological variables including air pressure, temperature, humidity, precipitation, and wind speed and direction. Prediction models are built using a variety of machine learning algorithms, such as neural networks, decision trees, random forests, and support vector machines. With the help of these algorithms, large datasets may be able to uncover complex correlations and produce accurate predictions. Improving predicted accuracy is the primary focus of the research, which places an emphasis on feature selection, data preparation, and model training. Accuracy and error measures are among the suitable metrics used to assess the models' performance. According to the results of the tests, machine learning algorithms can improve the precision of weather predictions by spotting trends more quickly. The accuracy of weather predictions is improved by combining current and historical meteorological data. Businesses that are dependent on the weather can benefit from the suggested method since it helps people make better judgments.