MLRM: A Multiple Linear Regression based Model for Average Temperature Prediction of A Day
This is an incremental improvement for weather forecasting applications, using a basic machine learning approach on standard data.
The paper tackles the problem of predicting average daily temperature using past meteorological data, achieving a prediction error of 2.8 degrees Celsius with a multiple linear regression model.
Weather is a phenomenon that affects everything and everyone around us on a daily basis. Weather prediction has been an important point of study for decades as researchers have tried to predict the weather and climatic changes using traditional meteorological techniques. With the advent of modern technologies and computing power, we can do so with the help of machine learning techniques. We aim to predict the weather of an area using past meteorological data and features using the Multiple Linear Regression Model. The performance of the model is evaluated and a conclusion is drawn. The model is successfully able to predict the average temperature of a day with an error of 2.8 degrees Celsius.