Development of an AI system for predicting emergency situations
Emergency forecasting involves merging diverse data: meteorology, geology, hydrology, and socioeconomic factors. The AI system doesn't replace the Ministry of Emergency Situations' experts, but it does provide them with a quantitative tool for prioritizing resources and providing early warning to the population.
Typology of predicted emergencies
Natural:
- Floods: 48 hours to several weeks forecast
- Forest fires: 24-72 hours (Fire Weather Index)
- Mudflows and landslides: after intense rainfall, hours-days
- Earthquakes: short-term forecast (days) is limited, but after-shock assessments are possible
- Hurricanes/typhoons: 5-7 days in NWP modeling
Man-made:
- Accidents at industrial facilities: analysis of historical incidents + ongoing monitoring
- Transport accidents: predictive patterns by season and weather
- Utility accidents: pipeline rupture forecast
Social:
- Epidemics: SIR/SEIR models + ML spread forecast
- Mass riots: sentiment analysis + historical patterns (sensitive area)
Forest Fire Forecasting Model
Canadian Forest Fire Weather Index (FWI): Standard agrometeorological fire danger index:
def calculate_fwi(temp, humidity, wind, precipitation):
"""
FFMC (Fine Fuel Moisture Code): сухость мелкого горючего
DMC (Duff Moisture Code): влажность слоя опада
DC (Drought Code): глубокий засушливый слой
ISI = FFMC × Wind function
BUI = DMC + DC
FWI = f(ISI, BUI)
"""
# Реализация на Python: pyrogue или cffdrs пакеты
...
ML based on FWI + additional factors:
- Satellite data: NDVI (dryness of vegetation), NBR (Normalized Burn Ratio)
- Topography: slope, aspect, height
- History of fires in the area
- Lightning density (thunderstorms without rain = ignition risk)
Model: Random Forest for the probability of a fire in a specific grid cell in the next 24-72 hours. Accuracy: AUC 0.85-0.92 over a 24-hour horizon.
Flood forecasting model
Hydrological model + ML:
Distributed Hydrological Model (HEC-HMS, SWAT): Physical model of the basin: precipitation → surface runoff → river level.
ML correction: The physical model has systematic errors (incorrect soil parameterization, unknown groundwater flows). The LSTM adds errors based on residuals.
Flash flood prediction: Flash floods (< 6 hours) are the most dangerous. Flash Flood Guidance (FFG): how much rainfall in 1/3/6 hours is required to overflow a channel:
def flash_flood_risk(observed_precipitation, ffg_threshold, soil_moisture, antecedent_rain):
"""
Если accumulated_rain / FFG > 1 → flash flood imminent
ML добавляет soil_moisture как корректор FFG threshold
"""
Early warning system
LEWS (Local Early Warning System): Levels:
- Watch: probability of emergency > 30% in the next 72 hours
- Warning: probability > 60% in the next 24 hours
- Emergency: An emergency occurs or is unavoidable in < 6 hours
Automatic actions by levels:
| Level | System Actions |
|---|---|
| Watch | Notification of the RSChS, preparation of resources |
| Warning | SMS distribution to the population of the risk zone |
| Emergency | Activation of warning systems (sirens), evacuation |
Risk zones: GIS analysis: which settlements are in the flood zone at the predicted water level. QGIS + FloodMapping: DEM + flood level → inundation map.
Data and infrastructure
Sources:
- Roshydromet: hydroposts, weather stations, NWP forecasts (Central Federal District)
- ECMWF/GFS: global NWP models
- NASA FIRMS: satellite fire hotspots (MODIS, VIIRS) - real time
- Sentinel-1 SAR: flood monitoring via radar (clouds pass through)
- Ministry of Emergency Situations RSChS: history of emergencies by region
Architecture:
- Apache Kafka: Streaming data from sensors
- Apache Flink: real-time processing, index calculation
- ClickHouse: analytics based on historical data
- GeoServer: publishing geolayers for GIS interfaces
- Grafana GeoMap: an operational dashboard for the Ministry of Emergency Situations duty shift
Integration with RSChS: The Ministry of Emergency Situations' Crisis Management Center (CMC) — integration via API or a secure communication channel.
Deadlines: FWI model + fire risk + FIRMS integration — 6-8 weeks. A full-fledged system with flood monitoring, multi-risk monitoring, and RSChS integration — 5-7 months.