A multi-purpose dataset for data-driven wildfire modeling in the Mediterranean. Deep Learning models for wildfire modeling, e.g. danger forecasting, burned area prediction, etc

Forecasting wildfire danger using deep learning.

Teleconnection-driven vision transformers for improved long-term forecasting

Data science for wildfire risk forecasting and monitoring

Wildfire risk assessment using remote sensing data - Prediction of Wildfires

A probabilistic approach to wildfire spread prediction using a denoising diffusion model

Physics-informed fire occurrence prediction using structured fire indices (ISI, FFMC, DMC, DC, BUI, FWI), and latent clustering. Implements an interpretable neural model fulfilling ISI’s predictive role. Stage 1 of a modular fire propagation modeling framework grounded in physical science. Resulted in a perfect 100% accuracy

This repository contains the code for a two-stage learning framework for wildfire forecasting under partial observability.

General Assembly Data Science Immersive (GA-DSI) Group Project - A machine learning model to predict the likelihood of a California wildfire based on historical weather and wildfire data.

Wildfire Management Tool (WMT) - desktop version, with the Campbell Prediction System (CPS). (This git repo was migrated from the original BitBucket/Mercurial repo.)

This repository includes some applications of extreme value analysis techniques for modeling wildfire data. It's a work in progress :) — feel free to reach out if you'd like more details!

This repository contains code and resources for the CS274E (Deep Generative Models) course project.

Florida-specifc wildfire prediction model

In this repository you will find the complete implementation of the model proposed in the paper entitled “Wildfire prediction using zero-inflated negative binomial mixed models: Application to Spain”

This web-app displays areas in British Columbia with the conditions likely for a Wildfire in the next 72-Hours using live weather data pulled from open-meteo and evaluated through a custom-trained Deep Learning Neural Network designed for Probabilistic Binary Classification.

This repository has the codes to predict wildfire susceptibility with various geospatial data.

As part of MSBA, developed a wildfire prediction model using Machine Learning and XGBoost to predict the likelihood of a wildfire occurring given historical weather data.

Modeling Wildfire Ignition in California Utilizing Daily Weather Readings and Machine Learning.