Data-driven modeling using system dynamics simulation to provide relief in earthquake based on different scenarios.

Area of Science:

• Disaster Management
• Operations Research
• Civil Engineering

Background:

• Effective disaster relief is crucial for mitigating damage and protecting populations during events like earthquakes.
• Existing models often lack a holistic approach, failing to integrate pre- and post-crisis considerations simultaneously.
• Sustainability in disaster response is increasingly vital for long-term resilience.

Purpose of the Study:

• To propose and validate a data-driven, sustainable model for earthquake relief logistics.
• To determine optimal quantities and allocation strategies for relief goods during seismic events.
• To minimize relief goods shortages, untreated casualties, and supply chain costs.

Main Methods:

• A two-phase approach involving simulation and multi-objective modeling.
• Dynamic simulation system to model earthquake impacts on urban infrastructure and vital arteries.
• Development of a data-based multi-objective model incorporating sustainability principles.

Main Results:

• The model effectively estimated relief goods demand across various earthquake scenarios.
• Identified the
• Mosha-day fault
• scenario as having the highest demand and the
• North Tehran-night fault
• scenario as having the lowest.
• The multi-objective model successfully balanced minimizing shortages, untreated wounded, and supply chain expenses.

Conclusions:

• The proposed data-driven model provides a robust framework for sustainable earthquake relief planning.
• Integrating pre- and post-crisis data enhances the efficiency and effectiveness of disaster response.
• The model's findings offer valuable insights for optimizing resource allocation in seismic emergencies.