New Year Offer End Date: 30th April 2024

Skills you will gain:

About Program:

This workshop explores how artificial intelligence can be used to integrate and analyze multiple disaster risks for better decision-making. Participants will gain insights into risk layering, predictive analytics, and geospatial data applications to improve disaster preparedness, resilience planning, and response strategies across complex hazard scenarios.

Aim:

To equip participants with the knowledge and practical skills to apply AI-driven techniques for integrating and analyzing multi-hazard risks, enabling informed decision-making and enhancing disaster preparedness, resilience, and response strategies.

Program Objectives:

  • To introduce the concept of risk layering and its importance in disaster analytics

  • To understand the role of AI and machine learning in multi-hazard risk assessment

  • To explore geospatial data integration and predictive modeling techniques

  • To develop skills for analyzing and interpreting layered risk data

  • To apply AI-driven approaches for improving disaster preparedness and response strategies

  • To examine real-world case studies for practical implementation of disaster risk analytics

What you will learn?

📅 Day 1: Multi-Hazard Vulnerability & Exposure Mapping

  • Risk Layering: High-frequency/low-impact vs. low-frequency/catastrophic events
  • GeoAI: Satellite & SAR data for infrastructure & social vulnerability mapping
  • IoT & Crowdsourced Data: Real-time urban vulnerability
  • Hands-on (Colab): Overlay flood-plain maps with socio-economic data to identify high-risk clusters

📅 Day 2: Predictive Modeling for Cascading Disasters

  • Disaster Chains: GNNs to model primary-to-secondary event triggers
  • Probabilistic Risk: Bayesian networks for uncertainty in forecasts
  • Generative AI: Simulate rare “Black Swan” disasters
  • Hands-on (Colab): Predict secondary hazard probabilities with Random Forest/XGBoost

📅 Day 3: Parametric Analytics & Decision Support Systems

  • Parametric Triggers: AI-driven insurance and instant relief activation
  • Multi-Agent Crisis Management: Autonomous resource allocation
  • Explainable AI: SHAP for policy & insurer transparency
  • Hands-on (Colab): Simulate automated alerts & fund releases based on real-time telemetry

Mentor Profile

Fee Plan

INR 1999 /- OR USD 50

Get an e-Certificate of Participation!

2024Certfiacte

Intended For :

  • Researchers and Ph.D. scholars

  • Data scientists and AI/ML professionals

  • Urban planners and environmental engineers

  • Government officials and policymakers

  • Insurance and risk assessment professionals

  • Academicians and students in related fields

Career Supporting Skills

Program Outcomes

  • Understand and apply risk layering in disaster analytics

  • Use AI/ML techniques for multi-hazard risk assessment

  • Analyze and interpret integrated risk and geospatial data

  • Develop data-driven disaster preparedness strategies

  • Apply AI solutions to real-world disaster scenarios