Environmental Monitoring System

The Environmental Monitoring System is a project designed to track environmental parameters such as air quality, temperature, and humidity, predict pollution trends, and alert users in real-time. By leveraging IoT devices (simulated via frontend inputs), machine learning, and web technologies, this project aims to empower communities to respond proactively to environmental changes.

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Features

1. Real-Time Monitoring:

   • Simulates IoT devices by accepting environmental data input from the frontend.
   • Monitors air quality, temperature, and humidity.

2. Machine Learning:

   • Random Forest Classifier for AQI bucket prediction based on input parameters.
   • Future-ready for time-series forecasting, anomaly detection, and clustering for advanced use cases.

3. Web-Based Dashboard:

   • A React-based frontend for users to input environmental parameters and view predictions and alerts.

4. Scalability:

   • Designed to integrate IoT devices (e.g., Arduino, ESP32) for live data collection.
   • Flask API for seamless integration between ML models and the web interface.

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Architecture

1. Frontend (React):

   • Accepts environmental parameter inputs (e.g., air quality, temperature, humidity).
   • Displays predictions and real-time alerts.

2. Backend (Flask):

   • Exposes an API to handle requests from the frontend.
   • Hosts the trained Random Forest Classifier ML model for AQI predictions.

3. Machine Learning:

   • Processes the input data to predict the Air Quality Index (AQI) bucket.
   • Future-ready for additional ML models like time-series forecasting.

4. IoT Devices (Simulated):

   • Environmental parameter inputs are manually provided via the frontend to simulate IoT-enabled devices.

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Project Setup

Prerequisites

• Python 3.8 or above
• Node.js and npm
• Flask
• React
• flask-cors for handling cross-origin requests
• Required Python libraries: pandas, scikit-learn, flask, flask-cors

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Backend Setup (Flask + ML Model)

1. Clone the repository:

   git clone https://github.com/YuvaSriSai18/Environment_Monitoring_System.git

2. Frontend Setup :

   cd client
   npm install
   npm run dev
   

   Simulating Environmental Data

   1. Open the React application.
   2. Input environmental parameters (air quality, temperature, humidity).
   3. Submit the data to receive the predicted AQI bucket and alerts.

Example Data Flow

• Frontend:

  • Input data: { "PM2.5": 60, "PM10": 100, "Temperature": 35, "Humidity": 50 }
  • Sends the data via a POST request to the Flask API.

• Backend:

  - Flask receives the input data.
  

  Random Forest Classifier predicts the AQI bucket (e.g., "Moderate"). - The prediction is sent back to the frontend.

• Frontend:

  • Displays the predicted AQI bucket and any associated alerts.

Technologies Used

• Frontend : React
• Backend : Flask with Flask-CORS , Express JS
• Machine Learning : Scikit-learn (Random Forest Classifier)
• IoT Hardware : ESP32 , MQ2 , MQ6 MQ7 , MQ8

Impact

The Environmental Monitoring System provides real-time environmental insights, empowering communities to:

1. Take proactive measures against pollution.
2. Monitor air quality and weather conditions.
3. Stay informed about their environment for better decision-making