BreatheWise Project

Overview

WeatherWise is a Python-based project designed to provide real-time warnings about wildfires and air pollution in Canada. The project utilizes external data sources and incorporates machine learning techniques to predict wildfires and issue warnings on a map in the client app.

Data Sources

• Weatherbit API: Provides current, forecast, and historical weather data globally, including air quality information.
• Bureau of Meteorology (BOM) Research Report: A research report on relevant meteorological information.

Obtaining Real-Time Data

To obtain real-time data for wildfires and air pollution in Canada, sign up for a free account on the Weatherbit API and access the relevant data through their API.

Machine Learning for Wildfire Prediction

To predict wildfires in Canada using machine learning:

1. Utilize Python libraries such as scikit-learn and TensorFlow.
2. Train a machine learning model using historical wildfire and air quality data.
3. Use real-time data from the Weatherbit API to make predictions and issue warnings.
4. Integrate this into the Python backend code to provide real-time wildfire warnings in the client app.

Refer to research articles and resources for detailed information on implementing machine learning for wildfire prediction.

Microservice Architecture with Python and Kafka

To implement a microservice architecture using Python and Kafka:

1. Develop Python microservices as producers and consumers.
2. Utilize Kafka for event-driven communication.
3. Ensure flexibility, scalability, and maintainability in the architecture.
4. Consider implementing event sourcing and CQRS for handling wildfire and air pollution data.

For technical details and implementation guidance, refer to provided GitHub repositories, articles, and videos.

Boilerplate Code and File Structure

Explore the provided GitHub repositories for boilerplate code and file structures for microservice projects in TypeScript, Go, and gRPC.

Wildfire Prediction Machine Learning Code

For wildfire prediction machine learning code in Python:

1. Gather historical wildfire data.
2. Preprocess data, handle missing values, and normalize features.
3. Train a model using machine learning libraries.
4. Evaluate and fine-tune the model.
5. Integrate the model into a real-time system for predictions.

Refer to GitHub repositories, Kaggle notebooks, and articles for detailed code examples and implementation details.

Trained Models for Wildfire Predictions

Yes, there are trained machine learning models available:

• AltaML: A wildfire occurrence prediction system trained on historical fire data.
• Open-source projects like "Forest Fire Prediction System" on GitHub.

These trained models can serve as valuable resources for your wildfire prediction project.

For further details, explore the provided sources and repositories mentioned above.