Forest Fire Risk Prediction model using a machine learning approach (Random Forest Classifier) trained on environmental data (temperature, humidity, wind, rainfall, etc.). This example includes data preprocessing, model training, evaluation, and prediction.

ForestSathi (Forest Companion) is an intelligent wildfire risk prediction system designed to help communities, forest officials, and researchers understand and mitigate fire risks across Nepal's diverse terrain. The platform combines satellite data analysis with machine learning to provide real-time, location-specific fire risk assessments.

Forest Fire Prediction using machine learning and geospatial analysis. Features include data preprocessing, geospatial transformations, visualization with Kepler.gl, and evaluation of models like Logistic Regression, SVM, and Random Forest. Achieved 89% accuracy with interactive fire risk predictions.

IoT system for forest fire risk analysis, early detection, and spread prediction

This project performs end-to-end wildfire risk prediction using multiple regression models based on the Algerian Forest Fires dataset. It covers complete data preprocessing, feature engineering, model training, evaluation, and deployment.

Forest Fire Risk Prediction App

No description available

No description available

Forest Fire Weather Index (FWI) Prediction App: A Flask web app that predicts FWI for Algerian forests using weather data. Built with a Ridge Regression model trained on the Algerian Forest Fires Dataset (2012). Enter weather details to estimate fire risk instantly.

Fire Weather Index (FWI) - Web App Predictor: Algerian Forest Fires dataset. Using Ridge, Lasso, Linear Regression, and ElasticNet models. Deployed as a Flask app on AWS Elastic Beanstalk. Explore the prediction insights for fire risk assessment.

Forest Fire Prediction is a machine learning project designed to predict the likelihood and severity of forest fires based on environmental and meteorological data. The project is developed primarily using Jupyter Notebook and leverages the power of data analysis and supervised learning techniques to provide accurate fire risk assessments.

Forest-fire risk prediction system for Indian terrain using MODIS satellite data. Includes automated Earth Engine extraction, NDVI/LST/NDMI processing, burned-area labeling, ML training with Random Forest, and geospatial heatmap visualization for research and disaster management.

Forest fire risk prediction

No description available

Forest Fire Risk Prediction Dashboard

A machine learning solution to predict the risk of forest fires using geospatial data.

No description available

THIS IS MY THEASIS WHERE I USED ML & RF , XGBOOST

No description available

No description available

No description available

Forest fire risk prediction — PySpark + Power BI

No description available

Forest fire risk prediction using linear and regularized regression models with cross-validation and model comparison.

No description available

No description available

No description available

streamlit app for forest fire risk prediction

AI-based forest fire risk prediction system

Machine learning based forest fire risk prediction using SVM and meteorological data