IoT Framework for Real Time Weather Monitoring using Machine Learning Techniques

Abstract

Weather forecasting and weather warnings are used to protect human lives and property. Temperature, Outlook, Humidity, and Wind forecasts are critical for farmers, as well as traders in product markets. Since weather data analytics necessitates extreme precision, high-performance computing is required to handle the massive amount of data. The significant variability of climatic observations obtained in a day makes weather forecasting difficult. The objective of this project is to forecast the weather parameters for the next 24 hours using Auto ARIMA model and to use machine learning techniques to reliably predict the weather. Machine learning predicts the weather conditions for the day using strong and highly significant results based on current data. A cost effective IoT frame work is designed to read the real time input using sensors integrated with Arduino platform. By inputting average temperature, humidity, pressure, and other variables, decision trees and the Random Forest Algorithm will be utilized to predict events such as fog, rain, dry, windy, clear, breezy, and thunder. The algorithm is evaluated based on various performance metrics that include precision, recall, F score and accuracy.