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Weather Monitoring System

Overview

This Weather Monitoring System is a Python-based application that fetches real-time weather data for multiple Indian metro cities, processes and stores this data, generates alerts for extreme weather conditions, and creates visualizations of weather trends.

Core Features

  • Fetches weather data from OpenWeatherMap API for Delhi, Mumbai, Chennai, Bangalore, Kolkata, and Hyderabad
  • Processes and stores weather data in MongoDB
  • Generates daily weather summaries including:
    • Average temperature
    • Maximum temperature
    • Minimum temperature
    • Dominant weather condition
  • User-configurable alerts for temperature thresholds
  • Visualizes daily weather summaries, historical trends, and triggered alerts

Additional Features

  • Extended weather parameters: Includes humidity and wind speed in addition to temperature

  • 5-day weather forecast retrieval and processing

  • Web dashboard for real-time weather visualization

  • Machine learning-based weather prediction for the next 24 hours

    Web Dashboard

    The web dashboard provides a user-friendly interface to view current weather data, forecasts, and predictions for all monitored cities.

    Machine Learning Weather Prediction

    Trained machine learning models for 24-hour weather prediction are stored in the models/ directory.

    Hosted Dashboard

    You can view a static version of the weather dashboard online:

    https://aryanbv.github.io/weather_monitoring_system/

    Note: This static version is updated periodically and may not reflect real-time data. For the most up-to-date information, please run the application locally as described in the Usage section.

Weather Monitoring System

To view the real-time weather dashboard:

  • Ensure Flask is installed: pip install flask
  • Ensure you're in the project root directory.
  • Run the Flask app:
python app.py

The dashboard displays the latest weather data for all monitored cities in an easy-to-read format.

Prerequisites

  • Python 3.8+
  • MongoDB
  • OpenWeatherMap API key

Installation

  1. Clone the repository:

    git clone https://github.com/AryanBV/weather_monitoring_system.git
cd weather_monitoring_system

  2. Install the required packages:

    pip install -r requirements.txt

  3. Set up MongoDB:

    • Install MongoDB if you haven't already
    • Start the MongoDB service

  4. Set up your OpenWeatherMap API key:

    • Sign up for a free API key at OpenWeatherMap
    • Set your API key as an environment variable:
      • On Windows: 
        set OPENWEATHERMAP_API_KEY=your_api_key_here
      • On macOS/Linux: 
        export OPENWEATHERMAP_API_KEY=your_api_key_here

  5. Configure the application:

    • Open config/config.yaml
    • Adjust the MongoDB configuration if necessary
    • Modify alert thresholds and update intervals as desired

Usage

To run the core Weather Monitoring System:

python main.py

To view the real-time weather dashboard (additional feature):

python app.py

Then open a web browser and navigate to http://localhost:5000

For a short demonstration run:

python run_demo.py

Project Structure

weather_monitoring_system/
├── config/
│   └── config.yaml
├── logs/
├── models/
├── src/
│   ├── alerts/
│   │   ├── __init__.py
│   │   └── alert_manager.py
│   ├── api/
│   │   ├── __init__.py
│   │   └── weather_api.py
│   ├── data_processing/
│   │   ├── __init__.py
│   │   ├── aggregator.py
│   │   └── data_processor.py
│   ├── database/
│   │   ├── __init__.py
│   │   └── db_handler.py
│   ├── ml/
│   │   └── weather_predictor.py
│   ├── utils/
│   │   ├── __init__.py
│   │   ├── config_loader.py
│   │   └── logger.py
│   └── visualization/
│       ├── __init__.py
│       └── visualizer.py
├── templates/
│   └── dashboard.html
├── tests/
│   ├── test_aggregator.py
│   ├── test_alert_manager.py
│   ├── test_data_processor.py
│   ├── test_db_handler.py
│   └── test_weather_api.py
├── visualizations/
├── app.py
├── main.py
├── README.md
├── requirements.txt
├── run_demo.py
└── test_system.py

Configuration

You can modify the following in config/config.yaml:

  • Database settings
  • Alert thresholds (user-configurable)
  • Data processing and visualization update intervals

The OpenWeatherMap API key should be set as an environment variable for security reasons.

Logs

Logs are stored in the logs/ directory. Check these for application activity and any errors.

Visualizations

Weather trend visualizations are saved in the visualizations/ directory.

Testing

To run the system tests:

python test_system.py

Individual test files for different components can be found in the tests/ directory.

Test Cases

  1. System Setup:
    • Verifies system starts successfully and connects to the OpenWeatherMap API using a valid API key.
  2. Data Retrieval:
    • Simulates API calls at configurable intervals.
    • Ensures the system retrieves weather data for the specified locations and parses the response correctly.
  3. Temperature Conversion:
    • Tests conversion of temperature values from Kelvin to Celsius.
  4. Daily Weather Summary:
    • Simulates a sequence of weather updates for several days.
    • Verifies that daily summaries are calculated correctly, including average, maximum, minimum temperatures, and dominant weather condition.
  5. Alerting Thresholds:
    • Defines and configures user thresholds for temperature conditions.
    • Simulates weather data exceeding or breaching the thresholds.
    • Verifies that alerts are triggered only when a threshold is violated.

Troubleshooting

  • If you encounter any issues with API key authentication, ensure that you've correctly set the OPENWEATHERMAP_API_KEY environment variable.
  • For database connection issues, check your MongoDB service is running and the connection details in config.yaml are correct.
  • If visualizations aren't generating, ensure you have write permissions in the visualizations/ directory.

Note

This project is an assignment submission. The implementation focuses on demonstrating understanding of real-time data processing, API integration, data aggregation, alerting systems, and visualization techniques in a weather monitoring context. Additional features showcase further capabilities beyond the core requirements.