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

Survival modeling based wildfire time to threat prediction using CV bagged Gradient Boosting Survival Analysis and IPCW weighted LightGBM. Hybrid metric optimization combining C index and weighted Brier scores with monotonic multi horizon probability enforcement. Public LB score 0.96841.

AI-powered wildfire detection and risk mapping using U-Net on satellite imagery with an interactive Streamlit dashboard.

Spatiotemporal wildfire risk prediction using NDVI + terrain features and a Genetic Algorithm–optimized ML pipeline.

Near-term wildfire risk forecasting platform for California using H3 geospatial indexing, NASA FIRMS, AlphaEarth embeddings, weather features, baseline ML models, and a map-ready dashboard.

Socioeconomic drivers of wildfire probability and urban smoke exposure in East & Southern Africa (2018-2024)

AI forest fire prediction using ResUNet-A deep learning and cellular automata. Real-time 30m resolution mapping for Uttarakhand. ISRO BAH Hackathon 2025.

Project contribution to Omdena's initiative to develop an AI-based system for wildfire spread prediction and early warning in Uttarakhand. This contribution focuses on using Land Surface Temperature (LST) data, geospatial analysis with Google Earth Engine, and Python to predict wildfire behaviors and identify potential hotspots.

Sample Machine Learning project that predicts wildfires in real-time and alerts emergency services including police, ambulance, and fire response teams.

Wildfire prediction using dual ML approaches: classical models (Logistic Regression, Random Forest, K-NN) on the WildfireDB tabular dataset, and transfer learning CNNs (VGG16, ResNet-50, EfficientNet-B3) on satellite imagery, with EDA, Grad-CAM visualisations, and full data pipelines.

Open-source wildfire prediction & early detection — free for firefighters and everyone at risk. By Infinite Space Technologies.

🔥 Model wildfire risk across the U.S. using a physics-informed Genetic Algorithm for efficient, autonomous scouting in constrained environments.