This repository contains the code for a two-stage learning framework for wildfire forecasting under partial observability.
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Updated
Mar 13, 2026 - Python
This repository contains the code for a two-stage learning framework for wildfire forecasting under partial observability.
Spatiotemporal wildfire risk prediction using NDVI + terrain features and a Genetic Algorithm–optimized ML pipeline.
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.
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.
AI forest fire prediction using ResUNet-A deep learning and cellular automata. Real-time 30m resolution mapping for Uttarakhand. ISRO BAH Hackathon 2025.
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)
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.
Result of a Research Initiation grant given by the São Paulo State Resarch Foundation (FAPESP) from 2025-2026. Uses Random Forest to train a wildfire prediction model by feeding environmental data (vegetation indices, temperature, land use) to a binary predictor with probabilities of occurences.
🔥 Model wildfire risk across the U.S. using a physics-informed Genetic Algorithm for efficient, autonomous scouting in constrained environments.
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