Why in the news?

  • Japan recently released an AI-generated video of Mount Fuji erupting, to give people an idea of what to expect if the disaster strikes and how to prepare themselves.

AI in Disaster Prediction

  • Applications of AI in Disaster Prediction
    • Earthquake Prediction
      • AI analyses seismic data to identify microtremors and fault-line stresses.
      • Machine learning models improve short-term forecasts.
      • Example: Google’s AI-powered earthquake alert system in California.
    • Flood Prediction
      • AI integrates satellite imagery, rainfall data, and river flow sensors to forecast floods.
      • Real-time flood inundation maps help evacuation planning.
      • Example: IIT Gandhinagar’s flood forecasting system for India using machine learning.
    • Cyclones & Hurricanes
      • AI predicts storm intensities, wind speeds, and landfall locations more accurately than traditional models.
      • Helps in disaster readiness and evacuation.
      • Example: IBM’s Deep Thunder project.
    • Wildfire Prediction
      • AI uses remote sensing data, vegetation maps, and weather patterns to detect fire-prone areas.
      • Real-time fire spread models assist firefighting strategies.
      • Example: NASA’s AI-based wildfire prediction model.
    • Landslides & Avalanches
      • AI integrates geological data, soil moisture, rainfall intensity, and terrain mapping.
      • Predicts slope failures and risk-prone zones.
      • Example: Indian Space Research Organisation (ISRO) landslide hazard mapping in Himalayan states.
    • Pandemics & Biological Disasters
      • AI analyses disease outbreak patterns using social media, health data, and mobility reports.
      • Example: AI platforms like BlueDot predicted COVID-19 spread before WHO alerts.
  • Advantages
    • Accuracy: Reduces false alarms by learning from past data.
    • Speed: Provides real-time analysis for early warnings.
    • Scalability: Works across diverse geographies and hazards.
    • Integration: Combines multiple datasets (satellite, IoT, drones, climate models).
  • Challenges
    • Data Gaps: Developing countries often lack reliable datasets.
    • Bias in Algorithms: AI predictions may be skewed if trained on limited or biased data.
    • Infrastructure Dependence: Requires robust digital connectivity.
    • Ethical Concerns: Risk of surveillance misuse and unequal access to AI benefits.
  • Initiatives
    • India
      • C-DAC developing AI-enabled disaster warning systems.
      • NIDM (National Institute of Disaster Management) incorporating AI in disaster training.
      • ISRO’s Bhuvan platform uses AI/ML for hazard mapping.
    • Global
      • UNDRR (United Nations Office for Disaster Risk Reduction) encourages AI integration.
      • Global Flood Awareness System (GloFAS) by EU uses AI-based modelling.