In a world increasingly exposed to natural disasters, health crises, and geopolitical emergencies, the ability to predict, respond to, and recover from crises is more critical than ever. Artificial Intelligence (AI) is emerging as a transformative force in disaster management—enabling faster decision-making, accurate risk assessments, and smarter resource coordination.

This course introduces professionals to the practical use of AI across the entire disaster management cycle. Participants will explore real-time data analysis, geospatial intelligence, early warning systems, and autonomous response tools. Through hands-on sessions and real-world examples, learners will build the skills to apply AI responsibly and effectively in disaster-prone and high-risk environments.

By the end of this course, participants will be able to:

Understand the role of AI across disaster risk reduction, emergency response, and recovery.
Apply AI technologies for forecasting, detection, and situational awareness.
Use AI to support resource allocation and crisis decision-making in real time.
Analyze damage, assess needs, and support recovery using AI tools.
Address ethical and operational challenges of using AI in humanitarian contexts.
Integrate AI into national, local, or organizational disaster strategies.

This course is ideal for:

Disaster risk reduction specialists and emergency managers.
Government and civil defense personnel.
NGO workers and humanitarian aid planners.
AI professionals building solutions for resilience and public safety.
Public health and crisis response teams.
Data analysts working in early warning or crisis modeling.
Urban planners, transport, and infrastructure managers.

The course uses simulations, AI tool demonstrations, team-based problem-solving, and case study analysis. Participants will explore disaster scenarios, interact with live data, and design AI-informed strategies for different crisis contexts. Learning is grounded in real-world situations with high relevance to multi-sectoral challenges.

Day 5 of each course is reserved for a Q&A session, which may occur off-site. For 10-day courses, this also applies to day 10

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ID	التواريخ المتاحة	المدينة	الرسوم	الإجراءات

Course Content

Section 1: The AI–Disaster Management Interface

Disaster types: natural, biological, technological, and complex crises
The four phases: mitigation, preparedness, response, recovery
Key AI technologies: machine learning, geospatial AI, NLP, computer vision
Success stories: wildfire tracking, flood alerts, earthquake response
Comparing traditional vs. AI-augmented disaster workflows

Section 2: Forecasting and Early Warning with AI

Predictive analytics using climate, weather, and epidemiological data
AI in flood prediction, landslide detection, and disease outbreak modeling
AI-enhanced satellite imagery interpretation
Social media and mobile data for real-time alerts
Building and managing AI-based early warning dashboards
Case study: AI use in anticipatory action in cyclone zones

Section 3: Response Optimization and Real-Time Decision Support

AI in resource deployment and routing for emergency services
Crisis mapping using drones, computer vision, and sensor data
NLP-driven systems for analyzing field communications
Chatbots for citizen communication and assistance during disasters
AI-powered prioritization of aid and services
Simulation: Managing a multi-hazard response using AI tools

Section 4: Recovery, Reconstruction, and Impact Assessment

Post-disaster needs analysis using AI-generated insights
Damage detection in buildings and infrastructure with computer vision
AI for tracking displaced populations and identifying vulnerable groups
Long-term planning using AI-supported environmental and economic models
Using AI to monitor health recovery, education continuity, and shelter access
Tool review: UN, Red Cross, and NGO AI platforms for recovery

Section 5: Ethical Use, Risk Mitigation, and Implementation Strategy

Ethical dilemmas in predictive decision-making: who gets help first?
Privacy and data protection in crisis contexts
Bias in crisis AI models: risk of exclusion or misallocation
Legal compliance, trust, and transparency in humanitarian tech
Building human-in-the-loop crisis response systems
Final task: Design a customized AI crisis strategy for your sector

Certificate Details

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