Logo Lanfrica

PW-5214/Algerian-Forest-Fire-Prediction-

Domain:

environment and energy

Record type:

software
Creator:
PW-
Host:
This is a Flask-based web app that predicts the Fire Weather Index (FWI) using environmental features from the Algerian Forest Fire dataset. It uses a trained Ridge Regression model and provides a simple HTML interface for input and result display. # Algerian Forest Fire Prediction πŸ”₯🌲 This is a machine learning web application built with **Flask** to predict the **Fire Weather Index** (FWI) using environmental features from the **Algerian Forest Fire dataset**. --- ## πŸš€ Project Overview The goal of this project is to build a regression model that predicts the severity of forest fires based on features like temperature, humidity, wind, rain, and other fire indices (FFMC, DMC, DC, ISI). The model is deployed via a web interface using Flask. --- ## πŸ“ Project Structure ``` β”œβ”€β”€ model/ β”‚ β”œβ”€β”€ ridge.pkl # Trained Ridge Regression model β”‚ └── scalar.pkl # StandardScaler used during training β”œβ”€β”€ templates/ β”‚ β”œβ”€β”€ index.html # Input form page β”‚ └── home.html # Result display page β”œβ”€β”€ application.py # Flask backend application β”œβ”€β”€ README.md # Project documentation └── requirements.txt # Python dependencies ``` --- ## 🧠 Machine Learning - **Algorithm Used**: Ridge Regression - **Preprocessing**: StandardScaler - **Target Variable**: Fire Weather Index (FWI) --- ## 🌐 Web Interface - Users enter environmental parameters via a simple HTML form. - The input is passed to a Flask backend. - The model predicts and returns the FWI value. --- ## πŸ› οΈ How to Run the Project 1. **Clone the repository** ```bash git clone github.com cd Algerian-Forest-Fire-Prediction- ``` 2. **Install dependencies** ```bash pip install -r requirements.txt ``` 3. **Run the Flask app** ```bash python application.py ``` 4. Open your browser and go to: `127.0.0.1` --- ## πŸ§ͺ Sample Input Parameters - Temperature - Relative Humidity - Wind Speed - Rain - FFMC Index - DMC Index - DC Index - ISI Index - Classes (1 = fire, 0 = no fire) --- ## πŸ“Š Dataset Source - The cleaned dataset used in this project is based on the Algerian Forest Fire dataset. --- ## πŸ™Œ Acknowledgements - Scikit-learn - Flask - …