Envirocast is a real-time air monitoring and forecasting system that provides monitoring and forecasting of indoor and outdoor environmental parameters. The system includes an IoT component for data collection, a machine learning component for forecasting, and a mobile app developed with Flutter for user interface and alerts.
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Real-time Monitoring:
- Indoor Parameters: Temperature, Humidity, Dust, LPG, Natural Gas, Carbon Monoxide.
- Outdoor Parameters: Temperature, Carbon Monoxide, Sulfur Dioxide, Nitrogen Dioxide, Ozone, PM2.5, PM10, AQI.
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Hourly Forecasting:
- Outdoor parameters forecasted for the next 7 days.
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Alerts:
- Notifications when monitored parameters reach harmful levels.
- Hardware Used: Arduino Uno, MQ5, MQ7, MQ135, DHT11, GP2Y101AU0F sensors, ESP32 Wi-Fi Module.
- Functionality: Collects real-time data from sensors and sends it to Firebase.
- Dataset: Gujrat, Pakistan dataset (Feb 2022 - June 2024).
- Models Trained: Moving Averages, ARIMA, SARIMA, FB Prophet, LSTM, and 1D-CNNs.
- Best Models: 1D-CNN models (8 models trained for 8 parameters).
- Deployment: Models deployed on AWS.
- Framework: Developed using Flutter.
- Features: Provides a user-friendly interface for monitoring, forecasting, and receiving alerts.
- envirocast_IOT: Includes Arduino code for sensor interfacing.
- envirocast_ML: Contains notebooks/scripts for data preprocessing, model training, and evaluation.
- envirocast_App: Flutter code for the mobile application.
