Real-Time Weather Prediction Using Multivariable Regression Model

Weather prediction is a vital part of our life. The collective information about the change in temporal dynamics of weather is very significant. This present work summarizes to analyze the current research works on weather forecasting and compare different machine learning techniques that has been used by several researchers for weather prediction. Various input parameters to these machine learning models are also tested to determine the usefulness of each of them. The fundamental aim of this present study is to develop a real time integrated system for weather monitoring system which enables prediction of temperature, humidity, and pressure information. Multivariable linear regression-based prediction model is used here to obtain the appropriate prediction model. Dataset is considered here of a city of tropical country India for the prediction model. It is an existing dataset which has been downloaded from online data repository. This work elaborates the proposed prediction model and the corresponding analytics supported by comparative study with the variation of different parameter. Temperature and humidity is predicted here among different weather parameters. Along with this, a hardware prototype model for real time for weather data acquisition using Raspberry Pi 3B model is reported in this present work. Raspberry Pi will fetch the data from the sensor, and all those data will be stored into a database and display of this data using a GUI is also shown for monitoring purpose.