Pideya Learning Academy

AI-Powered Recovery Planning for Disasters

Upcoming Schedules

  • Live Online Training
  • Classroom Training

Date Venue Duration Fee (USD)
27 Jan - 31 Jan 2025 Live Online 5 Day 3250
10 Mar - 14 Mar 2025 Live Online 5 Day 3250
14 Apr - 18 Apr 2025 Live Online 5 Day 3250
30 Jun - 04 Jul 2025 Live Online 5 Day 3250
28 Jul - 01 Aug 2025 Live Online 5 Day 3250
04 Aug - 08 Aug 2025 Live Online 5 Day 3250
06 Oct - 10 Oct 2025 Live Online 5 Day 3250
15 Dec - 19 Dec 2025 Live Online 5 Day 3250

Course Overview

In today’s fast-evolving global risk environment, the increasing intensity and frequency of disasters—ranging from climate-driven catastrophes and pandemics to cyber disruptions and supply chain breakdowns—are putting immense pressure on governments, institutions, and humanitarian actors to plan not just for response, but for resilient recovery. Traditional recovery models are often too slow, fragmented, or reactive to meet the demands of modern crises. That’s where artificial intelligence (AI) is redefining the future of disaster recovery planning. In response to this need, Pideya Learning Academy introduces AI-Powered Recovery Planning for Disasters, a specialized training program crafted to equip professionals with the foresight and tools to navigate post-disaster complexities using AI-driven intelligence.
The scope of disaster-related losses is both human and economic. According to the United Nations Office for Disaster Risk Reduction (UNDRR), global economic losses from disasters surpassed $3 trillion between 2000 and 2019, with climate-related events accounting for a sharp increase. Additionally, the World Bank reports that approximately 26 million people fall into poverty every year due to natural disasters. In such a context, AI-powered recovery solutions offer unprecedented capabilities—enabling faster situational awareness, optimized decision-making, and inclusive recovery designs grounded in real-time data and predictive modeling.
This training by Pideya Learning Academy is designed to bridge the gap between conventional disaster recovery approaches and the transformative possibilities AI brings. Participants will gain exposure to AI systems that analyze geospatial patterns, simulate recovery scenarios, and forecast long-term risks to better prepare organizations for future shocks.
Key highlights of the training include:
Understanding the role of AI across all phases of the disaster recovery cycle—from immediate response to long-term redevelopment
Leveraging machine learning models for rapid damage assessment, priority setting, and infrastructure needs mapping
Integrating geospatial data, satellite imagery, and remote sensing for dynamic recovery planning and impact visualization
Utilizing AI-powered simulation and forecasting tools to test recovery frameworks under various scenarios
Building ethical, inclusive, and transparent AI governance frameworks that center community well-being and data protection
Exploring AI-driven recovery strategies in housing, healthcare, economy, education, and critical infrastructure restoration
Designing institutional readiness programs and multi-stakeholder governance systems that align with AI-augmented recovery goals
By the end of this program, participants will not only be fluent in AI applications but also confident in architecting resilient, equitable recovery plans rooted in global best practices such as the Sendai Framework for Disaster Risk Reduction and the UN Sustainable Development Goals (SDGs). The course blends theoretical depth with structured learning exercises, ensuring learners build the capacity to lead AI-powered recovery projects across sectors.
This course by Pideya Learning Academy is ideal for professionals who want to be at the forefront of disaster risk management transformation. Whether you are involved in government policy, humanitarian logistics, urban resilience, infrastructure renewal, or continuity planning, this program empowers you with AI insights that drive smarter, faster, and more sustainable recovery outcomes.

Key Takeaways:

  • Understanding the role of AI across all phases of the disaster recovery cycle—from immediate response to long-term redevelopment
  • Leveraging machine learning models for rapid damage assessment, priority setting, and infrastructure needs mapping
  • Integrating geospatial data, satellite imagery, and remote sensing for dynamic recovery planning and impact visualization
  • Utilizing AI-powered simulation and forecasting tools to test recovery frameworks under various scenarios
  • Building ethical, inclusive, and transparent AI governance frameworks that center community well-being and data protection
  • Exploring AI-driven recovery strategies in housing, healthcare, economy, education, and critical infrastructure restoration
  • Designing institutional readiness programs and multi-stakeholder governance systems that align with AI-augmented recovery goals
  • Understanding the role of AI across all phases of the disaster recovery cycle—from immediate response to long-term redevelopment
  • Leveraging machine learning models for rapid damage assessment, priority setting, and infrastructure needs mapping
  • Integrating geospatial data, satellite imagery, and remote sensing for dynamic recovery planning and impact visualization
  • Utilizing AI-powered simulation and forecasting tools to test recovery frameworks under various scenarios
  • Building ethical, inclusive, and transparent AI governance frameworks that center community well-being and data protection
  • Exploring AI-driven recovery strategies in housing, healthcare, economy, education, and critical infrastructure restoration
  • Designing institutional readiness programs and multi-stakeholder governance systems that align with AI-augmented recovery goals

Course Objectives

After completing this Pideya Learning Academy training, the participants will learn to:
Interpret the critical phases of disaster recovery and where AI integration offers the highest value
Design data-informed post-disaster recovery plans using AI-enabled systems
Apply machine learning models to support real-time decision-making during recovery
Incorporate geospatial intelligence for assessing damage, displacement, and infrastructure status
Evaluate ethical and privacy risks in AI-driven disaster recovery systems
Formulate cross-sectoral recovery strategies incorporating health, economy, housing, and infrastructure
Establish AI-governed coordination protocols among stakeholders for more agile responses
Develop simulation-based recovery frameworks that adapt to evolving conditions
Align AI tools with global recovery frameworks like the Sendai Framework and SDGs
Build organizational capability to integrate AI into future disaster management systems

Personal Benefits

Participants of this program will benefit from:
Deep understanding of AI’s evolving role in disaster risk management
Expertise in interpreting recovery data and designing scalable solutions
Increased decision-making confidence in high-stress, post-disaster environments
Recognition as a strategic recovery leader in the AI age
Access to Pideya Learning Academy’s alumni network and resources
Certification validating expertise in AI-enhanced recovery planning
Capacity to contribute to organizational resilience and innovation agendas

Organisational Benefits

Organizations participating in this training will gain:
Enhanced capacity to lead AI-enabled disaster recovery operations
Reduced recovery cycle times through data-informed planning
Increased resilience of critical infrastructure and service continuity
Strategic foresight in future-proofing recovery frameworks
Alignment with international best practices and recovery standards
A competitive edge in donor engagement and funding proposals
Stronger inter-agency collaboration guided by intelligent coordination tools

Who Should Attend

This course is ideal for:
Disaster recovery planners and emergency response coordinators
Government officials in civil protection and resilience planning
Urban planners and infrastructure managers
Data scientists and AI professionals in the public sector
Risk and resilience professionals in international NGOs
Business continuity and crisis management leaders
Health system recovery specialists and logistics managers
Professionals working in sustainable reconstruction and redevelopment programs
Detailed Training

Course Outline

Module 1: Foundations of Disaster Recovery Planning
Principles and frameworks (Sendai, SDGs, FEMA guidelines) The recovery cycle: short-, medium-, and long-term recovery Sectoral recovery priorities: health, housing, economy, infrastructure Key stakeholders and inter-agency roles Recovery governance and funding structures Metrics and KPIs for effective recovery Policy alignment with AI innovation
Module 2: Introduction to AI in Disaster Management
What is AI? Scope in disaster risk management AI vs. traditional disaster tools Ethical foundations of AI use in crises Historical use cases: lessons and trends Technology readiness and digital maturity in public systems Open-source AI tools relevant to recovery Challenges and barriers to adoption
Module 3: Data Infrastructure and Sources for Recovery Planning
Critical datasets: demographics, infrastructure, health systems Use of remote sensing and drone data Satellite imagery for mapping disaster zones Data standards and interoperability issues Crisis data repositories and open datasets Data privacy and anonymization in recovery efforts Structuring dynamic, real-time dashboards
Module 4: Predictive Modeling for Post-Disaster Needs Assessment
Machine learning for damage prediction Demand forecasting for shelter, food, and healthcare AI in modeling displaced populations Infrastructure failure prediction using sensor data Resource optimization models Real-time gap analysis and prioritization Case studies in AI-based needs assessments
Module 5: Geospatial Intelligence and Risk Mapping
Introduction to GIS and AI integration Satellite, aerial, and terrain mapping systems Flood and hazard impact models Urban heat mapping and climate risk overlays Automated damage detection models Tools: Google Earth Engine, Copernicus, Sentinel Hub Visual storytelling for stakeholder engagement
Module 6: AI in Recovery Logistics and Supply Chain Planning
AI for optimizing aid distribution networks Forecasting supply-demand imbalances Route mapping and last-mile delivery models Resource allocation engines Logistics collaboration platforms with AI Case example: COVID-19 supply chain AI systems Infrastructure recovery logistics
Module 7: Multi-sector Recovery Strategy Design
Housing reconstruction: predictive timelines and needs Healthcare system resilience models Economic redevelopment using AI indicators Environmental recovery planning School and education continuity models Transport and urban mobility recovery Cross-sector integration of recovery strategies
Module 8: AI Governance and Ethical Use in Disaster Recovery
Bias, transparency, and accountability in AI models Data ownership and community rights Legal frameworks governing AI in emergencies Inclusion of vulnerable populations Ethics review boards and oversight Risk assessments and algorithm audits Trust-building with affected populations
Module 9: Simulation-Based Recovery Planning
Digital twins for urban resilience testing Scenario planning with AI-enhanced variables Stress testing infrastructure recovery plans Modeling cascading failures Real-time feedback and adjustment tools Predictive vs. reactive planning strategies Deployment of simulation platforms
Module 10: Institutional Integration and Capacity Building
Building in-house AI recovery teams Public-private partnerships for AI deployment National vs. local implementation strategies Developing internal AI-readiness assessments Training and reskilling plans Long-term AI maturity roadmap Monitoring and evaluation of AI-driven recovery programs

Have Any Question?

We’re here to help! Reach out to us for any inquiries about our courses, training programs, or enrollment details. Our team is ready to assist you every step of the way.