FirePredict
Know the Risk. Before the Fire.
FirePredict is an AI-powered forest fire risk prediction system that gives rangers and forest managers advance warning — so they can prepare, not just respond.
The Problem We Are Solving
Every year, forest fires destroy millions of hectares of forest across Africa — taking with them biodiversity, clean water, livelihoods, and carbon stored for decades. Most fire management systems respond to fires that are already burning. By then, the damage has begun.
FirePredict asks a different question: what if we could see the risk coming days in advance? What if rangers could be in position before the first spark — not racing to catch up after?
What FirePredict Does
FirePredict analyses satellite imagery, weather patterns, and local environmental conditions using a deep learning AI model trained on a decade of fire history data. It generates forest fire risk predictions for specific zones and delivers them to rangers through a mobile application — even in areas with no internet connection.
See FirePredict in Action
How It Works
- 1
Observe
Satellite imagery and weather data are collected continuously for each monitored forest zone.
- 2
Analyse
Our ConvLSTM deep learning model analyses spatial and temporal patterns to assess fire risk.
- 3
Predict
The model generates a risk prediction for the coming days — categorised as Very Low, Low, Moderate, or High.
- 4
Act
Rangers receive risk alerts on the FirePredict mobile app. A human reviews every alert before action is taken.
Key Capabilities
- Multi-day fire risk prediction — giving rangers advance time to prepare
- Zone-level risk maps showing where risk is highest
- Weather and vegetation monitoring across forest areas
- Mobile app delivery — works on Android smartphones
- Offline functionality — alerts available even without internet
- Fire reporting — rangers can report incidents directly from the app
- Scenario testing — simulate how risk changes under different conditions
- Human-in-the-loop design — every alert is reviewed by a human before action
Model Performance
Accuracy
Precision
F1 Score
AUROC
Validation: Independently validated against NASA VIIRS satellite fire records
Who FirePredict Is Built For
- National park authorities and forest rangers
- Government forestry and environmental agencies
- Meteorological services
- Conservation organisations
- Local community leaders and early warning committees
- Climate risk researchers and institutions
- Tourism operators and mountain guides
Current Status
FirePredict is currently in the validation phase — tested with rangers, local leaders, and environmental experts. We are working towards full operational deployment in partnership with Tanzania National Parks (TANAPA). We are actively seeking pilot partners in East Africa and beyond.
Adapting FirePredict for Your Region
FirePredict is designed to be adapted. The underlying AI architecture can be retrained using local satellite, weather, and fire history data for any forest region in the world. We provide full documentation and technical support for partner organisations seeking to deploy FirePredict in new regions.
SDG Alignment
- 13Climate Action: Protects forest carbon sinks and supports national REDD+ commitments
- 15Life on Land: Prevents fire damage to forest biodiversity and ecosystems
- 3Good Health and Well-Being: Protects water catchment forests that millions of people depend on
- 17Partnerships for the Goals: Built through collaboration with national and international partners
Alignment With Tanzania National Goals
- Tanzania Vision 2050 — homegrown AI innovation for green economy and environmental sustainability
- Tanzania NDC (Paris Agreement) — protecting forests and reducing fire-related emissions
- Tanzania National Climate Change Response Strategy — early warning systems and disaster risk reduction
- Tanzania Forest Policy — preventing the biggest cause of forest degradation
UNFCCC Recognition
FirePredict has been recognised as a Top 5 Global Finalist in the UNFCCC AI for Climate Action Award 2026 — selected from a competitive global field as one of the most promising AI solutions for climate action.