The project combines the capabilities of IoT(Internet of Things) and TinyML to forecast the likelihood of rainfall based on real-time temperature and humidity data. With a DHT sensor interfaced with an Arduino Uno microcontroller, this system gathers environmental information crucial for accurate predictions.
Trained on a Logistic Regression model based on historical weather data, the model has an accuracy of 74%. The model analyzes incoming sensor data to estimate the probability of rainfall occurrence.
This project demonstrates a practical application of IoT and TinyML technologies in weather forecasting, providing users with valuable insights into the likelihood of rainfall in their vicinity. The project has applications in various use cases such as agricultural planning, outdoor activities, and general weather monitoring.
- Raghav Mangla
- Akshit Singhal
- Aditya Kumar