Integrated IoT Based Weather Monitoring and Machine Learning Weather Prediction System

This project presents an Integrated IoT based Weather Monitoring System and Machine Learning based Weather Prediction System that integrates real-time sensor data collection through sensors and predictive analysis to implement weather prediction. The system includes IoT sensors like BME680, Wind Speed and Direction and Rainfall sensors to collect real-time parameters like temperature, humidity, wind speed which is sent over to the cloud NodeMCU-ESP32 over Wi-fi and is also stored in a cloud storage platform (OneDrive). The data is then visualized and monitored on open-source platform called ThingsBoard. To enhance the project, we implemented Machine Learning Algorithms, Random Forest Regressor for temperature prediction and Random Forest Classifier for rain prediction. The models are trained on the historical data and also real-time data to increase accuracy of prediction. This predicted data is displayed the ThingsBoard dashboard for user accessibility. This costeffective, scalable and efficient system focuses on weather monitoring and prediction to increase the accuracy and making it more valuable for applications like Air Quality Monitoring, Disaster Management and many more.