| Using Monte Carlo simulation to refine emergency logistics response models: a case study. | Summary
The probability distribution for the Monte Carlo simulation was based on a triangular distribution. “Fuzzy” information had to be transformed into a triangular distribution. Conventional quantitative transformation techniques are not well suited for dealing with decision problems involving fuzziness |
| Assessing the reliability and the expected performance of a network under disaster risk. | Summary
Monte Carlo sampling algorithm to estimate the measures under interest for the computationally difficult case of independent link failures for purposes of comparison. |
| Parameter-Based Data Aggregation for Statistical Information Extraction in Wireless Sensor Networks. | Summary
Theoretical approximation, simulation development, experimental evaluation |
| A decision support system for debris-flow hazard mitigation in towns based on numerical simulation: a case study at Dongchuan, Yunnan Province. | Summary
This method involves complicated arithmetic, and needs to obtain all kinds ofphysical parameters of debris flow, such as bulk density, coefficient of viscosity, yield stress etc |
| An emergency logistics response system for natural disasters. | Summary
The simulation model was run for 300 replications (years) with a length of one year each, equivalent to 1064 hurricanes. Since thiswas a terminating simulation, nowarm-up period and steady-state analyses were required. |
| Developing shared situational awareness for emergency management. | Summary
To determine what is critical information for the CCRRS we divided the approach into critical information, i.e. the minimum SSA needs of the CCRRS, and action-triggering information, i.e. the minimum SA needs of an organization or actor. |
| Emergency crowd evacuation modeling and simulation framework with cellular discrete event systems. | Summary
The experiments are divided into four sections; the first demonstrating a proof of concept for the basic singular models, the second demonstrating a proof of concept for integrated models, the third demonstrating a proof of concept for the introduction of complications and the ability of the models to handle these complications, and the fourth measuring the performance of these models in larger scale environments, such as schools, malls, or even airports |
| Supporting collaborative sense-making in emergency management through geo-visualization. | Summary
Design implementation phases:Java system prototypeWeb-based prototype |
| An optimization approach for ambulance location and the districting of the response segments on highways | Summary
For simulation on case study 1: The procedures to calculate the transient period (warm up) and the simulation run length are described in detail in Iannoni and Morabito |
| State Mandate Influences on FEMA-Approved Hazard-Mitigation Plans Under the Disaster Management Act of 2000. | Summary
Comparison of Mitigation Action Plan (MAP) and FEMAs State Planning Mandates on Hazard Mitigation DMA2K |
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Serwis internetowy Portfolio of Solutions został początkowo opracowany w ramach projektu DRIVER+. Obecnie serwis jest zarządzany przez AIT Austrian Institute of Technology GmbH, na rzecz Europejskiego Zarządzania Kryzysowego. PoS jest popierany i wspierany przez Disaster Competence Network Austria (DCNA), jak również przez projekty STAMINA i TeamAware H2020. |