Prakhar-3009/Algerian-Forest-Fire

This project predicts the Fire Weather Index (FWI) for two regions in Algeria — Sidi-Bel and Brjaia — using a machine learning model. The model takes input parameters like temperature, humidity, wind speed, rainfall, and other weather indicators to provide an accurate FWI prediction. # Algerian Forest Fire Prediction This project predicts the **Fire Weather Index (FWI)** for two regions in Algeria: **Sidi-Bel** and **Brjaia**. The FWI is a crucial indicator used to estimate the likelihood of forest fires based on environmental parameters. The model has been developed using **Machine Learning (ML)** algorithms to assist in early fire detection and risk management. ## Table of Contents - Overview - Features - Dataset - Model - Tech Stack - Usage - Screenshots - Future Enhancements - Contributing - License ## Overview The project provides a web-based interface where users can input various environmental factors such as temperature, humidity, wind speed, and rainfall to predict the Fire Weather Index. Based on the FWI value, the risk level of a forest fire is categorized from **Low** to **Extreme**. ## Features - Predicts Fire Weather Index (FWI) using ML. - Supports prediction for two regions: **Sidi-Bel** and **Brjaia**. - Provides visual indicators for fire risk levels. - User-friendly web interface for input and result display. ## Dataset The dataset used in this project contains historical environmental data including: - Temperature (°C) - Relative Humidity (%) - Wind Speed (km/h) - Rainfall (mm) - Fine Fuel Moisture Code (FFMC) - Duff Moisture Code (DMC) - Initial Spread Index (ISI) - Region Classification (0: Brjaia, 1: Sidi-Bel) ## Model The machine learning model has been trained using algorithms like: - **Linear Regression** - **Ridge Regression** - **Lasso Regression** - **Elastic-Net** The final model was selected based on evaluation metrics like **Mean Squared Error (MSE)** and **R-Squared Score**. ## Tech Stack - **Frontend:** HTML, CSS, JavaScript - **Backend:** Flask - **Machine Learning:** Python, NumPy, Pandas, Scikit-Learn - **Deployment:** Flask Web App ## Usage - Enter the environmental parameters in the input form. - Select the region (**Sidi-Bel** or **Brjaia**). - Click on **Calculate FWI**. - The model will display …