Fire Weather Index (FWI) Prediction

A web-based application built using Flask that predicts the Fire Weather Index (FWI) using a machine learning model. The app accepts weather and environmental parameters as inputs and returns a prediction of the FWI, which helps in assessing wildfire risks.

Table of Contents

• Overview
• Features
• Technologies Used
• Installation
• Usage
• Input Parameters
• Output
• File Structure
• Screenshots
• Future Enhancements
• Contributing
• License

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Overview

Wildfires are a significant hazard affecting ecosystems and human communities. The Fire Weather Index (FWI) is a key component used to assess the potential risk of wildfires based on various environmental and weather conditions. This project leverages machine learning to predict the FWI given a set of weather-related inputs.

The application is simple and intuitive, allowing users to input parameters like temperature, humidity, wind speed, and other related factors to generate predictions.

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Features

• User-friendly interface for inputting weather data.
• Real-time predictions powered by a Ridge Regression machine learning model.
• Data pre-processing using a StandardScaler to normalize inputs.
• Flask-based backend with dynamic HTML templates.
• Easily deployable on any server or cloud platform.

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Technologies Used

• Programming Language: Python
• Web Framework: Flask
• Machine Learning Model: Ridge Regression (using scikit-learn)
• Frontend: HTML
• Libraries:
  • numpy
  • pandas
  • scikit-learn
  • pickle
• Deployment Ready: Compatible with services like Heroku or AWS.

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Installation

Follow these steps to set up and run the application locally:

1. Clone the repository:

   git clone https://github.com/your-username/fwi-prediction.git
   cd fwi-prediction

2. Create a virtual environment and activate it:

   python -m venv venv
   source venv/bin/activate  # On Windows, use venv\Scripts\activate

3. Install the required dependencies:

   pip install -r requirements.txt

4. Ensure that the models directory contains:

   • ridge.pkl (trained Ridge Regression model)
   • scaler.pkl (fitted StandardScaler object)

5. Run the Flask application:

   python application.py

6. Open the application in your browser:

   http://127.0.0.1:5000
   

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Usage

1. Navigate to the application in your browser.
2. Enter the required weather parameters in the form fields:
   • Temperature
   • Relative Humidity (RH)
   • Wind Speed (Ws)
   • Rain
   • Fine Fuel Moisture Code (FFMC)
   • Duff Moisture Code (DMC)
   • Initial Spread Index (ISI)
   • Classes
   • Region
3. Click the Predict button.
4. View the Fire Weather Index (FWI) prediction displayed below the form.

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Input Parameters

Parameter	Description	Example Value
Temperature	Air temperature in degrees Celsius	22.5
RH	Relative humidity in percentage	45
Ws	Wind speed in km/h	12.3
Rain	Rainfall in mm	0.0
FFMC	Fine Fuel Moisture Code	85.2
DMC	Duff Moisture Code	45.7
ISI	Initial Spread Index	4.2
Classes	Fire classes	1
Region	Region code	2

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Output

The application predicts the Fire Weather Index (FWI) as a single numerical value. This index indicates the potential for wildfire occurrence and severity.

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File Structure

fwi-prediction/
│
├── models/
│   ├── ridge.pkl              # Trained Ridge Regression model
│   ├── scaler.pkl             # Fitted StandardScaler object
│
├── templates/
│   ├── index.html             # Welcome Page
|   ├── home.html              # Main HTML template for the form
│
├── application.py              # Flask application
├── README.md                  # Project documentation

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Screenshots

1. Input Form

2. Prediction Result

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Future Enhancements

• Add data visualization to display trends in historical FWI data.
• Improve the UI with better styling and animations.
• Support multiple machine learning models for comparison.
• Deploy the application to a public cloud platform (e.g., Heroku, AWS).
• Add error handling and validation for user inputs.

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Contributing

Contributions are welcome! If you have ideas or suggestions for improving the application:

1. Fork this repository.
2. Create a new branch for your feature:

   git checkout -b feature-name

3. Commit your changes:

   git commit -m "Add new feature"

4. Push your changes to your forked repository:

   git push origin feature-name

5. Submit a pull request!