Machine Learning Methods to Weather Forecasting to Predict Apparent Temperature

Abstract

Abstract: Due to the impact that it has on human life across the globe, weather forecasting has attracted the attention of researchers from a wide variety of research communities. Many researchers have been motivated to investigate hidden hierarchical patterns in large volumes of weather datasets by the recent emergence of deep learning techniques over the past decade. This motivation has been influenced by these methods, as well as by the ubiquitous accessibility of enormous amounts of weather observing data and the development of information and technology. In this study, we explore use of the machine learning techniques to apparent temperature weather forecasting. We use the dark sky apis data set for forecasting and assessment, and this study uses a random forests, decision tree, linear regression, or polynomial regression. Finally, performance is evaluated using metrics like mean square error & r-square.