pinkidagar18/Algerian-Forest-Fire-Prediction-System

A machine learning-powered web application that predicts the Fire Weather Index (FWI) for Algerian forests using real-time meteorological data. The system helps assess fire risk levels across different regions to enable proactive fire prevention and resource allocation. --- title: Algerian Forest Fire Prediction System emoji: 🔥 colorFrom: green colorTo: yellow sdk: docker pinned: false license: apache-2.0 --- # 🔥 Algerian Forest Fire Prediction System A machine learning-powered web application that predicts the Fire Weather Index (FWI) for forest fire risk assessment in Algeria using meteorological and environmental data. ## 📋 Table of Contents - Overview - Features - Dataset - Model - Installation - Usage - Project Structure - API Endpoints - Input Parameters - Technologies Used - Screenshots - Troubleshooting - Common Issues & Solutions - Future Improvements - Contributing - License ## 🌟 Overview Forest fires pose a significant threat to ecosystems, wildlife, and human settlements. This project leverages machine learning to predict the Fire Weather Index (FWI), a critical metric used by fire management agencies to assess fire danger levels. By analyzing meteorological and environmental factors, the system provides real-time predictions to aid in fire prevention and resource allocation. ### What is FWI? The Fire Weather Index (FWI) is a numeric rating of fire intensity. It combines various factors including temperature, humidity, wind speed, and rainfall to produce a comprehensive fire danger rating. Higher FWI values indicate greater fire danger. ## ✨ Features - **Real-time FWI Prediction**: Instant fire weather index predictions based on current conditions - **User-Friendly Interface**: Clean, responsive web interface with modern design - **Input Validation**: Comprehensive validation of all input parameters - **Model Performance**: Ridge Regression model with optimized hyperparameters - **RESTful API**: Health check endpoint for monitoring system status - **Error Handling**: Robust error handling and logging system - **Responsive Design**: Works seamlessly on desktop and mobile devices ## 📊 Dataset The project uses the **Algerian Forest Fires Dataset**, …