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
Get an e-Certificate of Participation!

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

