A real-time deep learning system powered by YOLOv8 for accurate fire and smoke detection across images, videos, and live webcam feeds. The system issues instant Telegram alerts when fire confidence surpasses a safe threshold. With a Flask-based interface and detailed model evaluation metrics, this solution enhances wildfire monitoring, early detect

The experiment project for predicting the burned area of the forest fires specifically in the northeast region of Portugal, based on the spatial, temporal and weather variables where the fire is spotted using deep learning.

Machine learning model to predict the Fire Weather Index (FWI) from meteorological data for forest fire risk assessment.

Forest fires are a significant threat to the environment, wildlife, and human lives. Project Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials

Final Year Project Topics For Computer Engineering Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials

This project focuses on predicting the burned area of forest fires using Long Short-Term Memory (LSTM) neural networks. The LSTM model is trained on historical data to forecast the extent of forest fire damage based on various environmental and meteorological factors.

This project predicts the likelihood of forest fires in Algeria using machine learning regression techniques based on weather and environmental data. It includes EDA, feature engineering, and model deployment.

Forest Fire Prediction using multiple Regression Models and Flask

End-to-end machine learning pipeline for predicting forest fire risk using the Algerian Forest Fires dataset, including EDA, feature engineering, model training, hyperparameter tuning, and deployment using Flask and AWS/render.

🔥 AI-based forest fire detection system using CNN and real-time webcam monitoring with email alerts

No description provided.

A Flask-based web app that predicts Fire Weather Index (FWI) powered by machine learning and real-time environmental inputs.

This project predicts forest fires in Algeria using machine learning models . The dataset includes various meteorological and environmental features such as temperature, humidity, and wind speed. The app cleans the data and builds models to predict the likelihood of forest fires based on historical data and environmental conditions.

Code for data processing and ML training

Machine learning project predicting forest fire occurrence in Algeria using meteorological data and Fire Weather Index (FWI) components with Flask web deployment

Flask web app for predicting Algerian forest fire weather index using machine learning