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chaseungjoon/
wildfire-prediction-A3C-LSTM

Name: wildfire-prediction-A3C-LSTM
Availability: InStock

reinforcement-learning

chaseungjoon/wildfire-korea-episodes-300m

chaseungjoon/wildfire-korea-embedded-300m

mit

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Wildfire Spread Prediction Model (A3C-LSTM, Korea 300m Grid)

This repository provides a reinforcement-learning–based wildfire spread prediction model trained on a 300 m spatial grid covering the Korean Peninsula.

The model uses an A3C agent with an LSTM policy, optimized to learn spatiotemporal wildfire propagation patterns using real wildfire episodes.

Model Overview

Architecture: CNN Encoder → LSTM Policy → Spatial Upsampling Policy Head
RL Algorithm: A3C
Input Resolution: 300 m × 300 m grid tiles
Input Channels:
- DEM (Elevation)
- RSP (Slope)
- LCM (Land Cover Map)
- FSM (Forest Structure Model)
- NDVI (Vegetation Index)
- Fire mask history (previous timesteps)
Output: Probability distribution over 8 directional spread actions + stay
Training Episodes: Derived from 10 years of NASA FIRMS wildfire detections (VIIRS/MODIS), clustered temporally into fire episodes.

Included in This Model Repo

v3.pt — trained model weights
config.json — architecture & input configuration
README.md — model card (this file)
model.py - A3C model architecture
inference_engine.py - Inference logic (must use wildfire-korea-embedded-300m for inference)
wildfire_env_inference.py - Environment interface
requirements.txt - All dependencies
visualizer.py - Visualization utilities

Github Repo

Inference/Training code, dataset preparation scripts, and environment setup are hosted on GitHub.

DISCLAIMER

The model is not intended as an operational early-warning system without additional validation.

chaseungjoon/wildfire-prediction-A3C-LSTM

Wildfire Spread Prediction Model (A3C-LSTM, Korea 300m Grid)

Model Overview

Included in This Model Repo

DISCLAIMER

chaseungjoon/
wildfire-prediction-A3C-LSTM