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

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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.

AI-based forest fire risk prediction system

AI-Powered Disaster Management System: Forest Fire Risk Prediction

Machine Learning based Forest Fire Risk Prediction System using Flask

AI-based forest fire risk prediction system using machine learning

Forest Fire Prediction System AI-Powered Wildfire Risk Assessment & Early Warning

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Forest Fire Prediction System is a machine learning–based web application built with Scikit-Learn and Streamlit that predicts forest fire risk using environmental and meteorological data. The system leverages a Random Forest classifier, visual feature importance, and an interactive green-black themed UI to provide real-time fire risk analysis.

Forest Fire Prediction System 🔥 Live fire data map using NASA FIRMS 📊 Risk level prediction based on weather & historical data 🌐 Interactive map (Leaflet.js) to show: Current fire locations Risk zones (colored overlays) 📈 Graphs for historical trends (temp, humidity, past fire frequency)

The Forest Fire Prediction System is a machine learning-based application that predicts the risk of forest fires based on environmental factors such as temperature, wind speed, humidity, and rainfall. It utilizes a Gradient Boosting Regressor model to analyze fire risk and classify the forest status as "Safe" or "In Danger!".

Predicts Forest Fire Weather Index (FWI) using a machine learning model trained on the Algerian Forest Fires Dataset (244 samples from Bejaia & Sidi Bel-abbes). Helps assess wildfire risk based on environmental data. Ideal for wildfire prediction and prevention systems.

Forest Fire Prediction System uses machine learning to predict fire risk based on temperature, humidity, wind, and rainfall. Designed to support early warning and prevention, it provides accurate forecasts and visualizations to help environmental agencies and disaster teams respond effectively.

A Hybird Approach Using Context based fire risk, LSTM, and XGBoost. A hybrid wildfire prediction system using CBFR, LSTM, and XGBoost models with a Linear Regression meta-learner. It analyzes historical and real-time data to predict forest fire risks accurately, supporting early warnings and proactive disaster management.

The Forest Land Alert and Monitoring System (FLAMS) is a web app using Laravel (front-end) and Flask (back-end) to predict forest fires in Indonesia. It employs the Extreme Learning Machine (ELM) algorithm and the Fire Weather Index (FWI) for predictions, displayed on an interactive map for risk visualization and timely prevention.

A hybrid wildfire prediction system using CBFR, LSTM, and XGBoost models with a Linear Regression meta-learner. It analyzes historical and real-time data to predict forest fire risks accurately, supporting early warnings and proactive disaster management

🔥 Algeria-Forestfire-Prediction – Smart Fire Forecasting : A smart and simple machine learning project to predict forest fires in Algeria using weather and environmental data. From data cleaning to model building, this project helps in creating early warning systems that can reduce wildfire risks and protect nature.

🔥 AI Forest Fire Prediction System Predicts wildfire risks using real-time weather data & ML. Features comprehensive risk assessments, professional reports, and an AI chat assistant for safety guidance. Built with Python/Flask, integrates Open-Meteo API. Uses RandomForestClassifier (78.3% accuracy, 96.6% ROC AUC).