Comparative Analysis of Weather Forecasting Techniques Using Machine Learning

This study presents a comprehensive framework that incorporates machine learning with weather forecasting to predict the future temperature by using past data and current observations as well as allow temperature forecasting updates. With regards to model performance assessment, mean squared error (MSE) and root mean squared error (RMSE) are some of the indicators used. The way error contours are plotted also helps in understanding both realised and forecasted temperature extremes. This technique also shows how predictive models are beginning to guide decisions when such climate based services as improved agricultural and transportation systems are available to offer flexible and scalable solutions for weather forecasting.