Forest-Fire-Prediction

Overview

The primary objective of this project is to develop a machine learning model that accurately predicts the burned areas caused by forest fires. Forest fires have become increasingly frequent and devastating disasters, causing significant environmental damage and posing real emergencies. Traditional methods of detection and response often have limitations in terms of speed and accuracy, resulting in delayed mitigation efforts. To address this challenge, this project proposes the use of machine learning regression algorithms and ensemble learning techniques.

Various regression algorithms will be implemented, including:

1. Linear Regression: A basic regression model that establishes a linear relationship between the input features and the target variable
2. Lasso and Ridge Regression: These are regularization techniques that help prevent overfitting by adding penalty terms to the regression equation.
3. Support Vector Regression with Radial Basis Function (SVR-rbf): SVR is a powerful regression algorithm that can handle non-linear relationships between variables.
4. Support Vector Regression with Polynomial Kernel (SVR-poly): Similar to SVR-rbf, this variant uses a polynomial kernel for handling non-linear data.

Additionally, ensemble learning will be employed using the Gradient Boosting Regressor. Ensemble methods combine the predictions of multiple models to improve accuracy and robustness.

The project will be implemented in Python, utilizing popular machine learning libraries such as Scikit-Learn and Pandas. Data preprocessing, feature engineering, and model evaluation will be key steps in the workflow. The dataset used for training and testing the models will consist of historical fire data with associated satellite image features.

Usage

This project serves as a valuable resource for predicting forest fire damage using machine learning techniques. You can use the provided code and models as a starting point for your own forest fire prediction projects.

Data

Source: https://archive.ics.uci.edu/ml/datasets/forest+fires

Acknowledgments

Thanks to the machine learning and data science communities for their contributions to regression techniques and ensemble learning. Special thanks to the data sources providing historical fire data and satellite imagery for this project.