Algerian Forest Fire Prediction

Predict Fire Weather Index (FWI) using Machine Learning and meteorological data. This project analyzes environmental conditions to estimate forest fire risk and intensity, helping support early detection and disaster prevention.

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📖 About the Project

Forest fires are among the most devastating natural disasters. This project uses the Algerian Forest Fire Dataset and a Ridge Regression Machine Learning model to predict Fire Weather Index values. The application demonstrates the complete machine learning lifecycle from data preprocessing and feature engineering to model deployment using Flask.

🚀 Key Features

Machine Learning

Ridge Regression model trained and optimized for accurate FWI prediction.

Instant Prediction

Enter weather parameters and receive real-time prediction results.

Fire Risk Analysis

Estimate wildfire danger levels before severe fire incidents occur.

📊 Input Features

🌡 Temperature
Air temperature in °C
Higher temperature increases fire risk by drying vegetation.
💧 Relative Humidity
Moisture level in air (%)
Low humidity makes the environment more prone to fire spread.
🌬 Wind Speed
Speed of wind affecting fire spread
Strong winds can accelerate and spread forest fires rapidly.
🌧 Rainfall
Amount of rainfall (mm)
Rainfall reduces dryness and lowers the chances of fire occurrence.
🔥 FFMC
Fine Fuel Moisture Code
Indicates moisture in surface fuels like leaves and grass.
📈 DMC
Duff Moisture Code
Represents moisture in decomposed organic material below surface.
⚡ ISI
Initial Spread Index
Measures how fast a fire can initially spread in current conditions.
🏷 Classes
Fire / No-Fire classification label
Indicates whether fire is likely to occur or not.
🌍 Region
Geographical area of dataset
Different regions have different climate and fire risk patterns.

⚙ Machine Learning Workflow

1. Data Collection

Algerian Forest Fire Dataset

2. Data Cleaning

Handling missing values and preprocessing

3. Feature Scaling

StandardScaler transformation

4. Model Training

Ridge Regression Algorithm

5. Prediction

FWI Forecast Generation

🛠 Technology Stack

Python
Flask
Scikit-Learn
Pandas
NumPy
HTML5
CSS3

🎯 Project Objective

The objective of this project is to develop a reliable Machine Learning system capable of predicting Fire Weather Index values from meteorological data. It showcases data preprocessing, model training, evaluation, and deployment as a complete end-to-end Machine Learning application.

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