Dynamic agent based simulation of welfare effects of urban disasters

Grinberger, A. Yair; Felsenstein, Daniel

doi:10.1016/j.compenvurbsys.2016.06.005

Cited by 27 publications

(12 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…These studies focus on how to solve problems in relation to post-earthquake emergency response (Ghosh and Gosavi, 2017), early warning system for biodiversity (Rovero and Ahumada, 2017), welfare impacts of urban disasters (Grinberger and Felsenstein, 2016), etc. This includes the use of semi-Markov model, cloud-based architecture, agent-based models, and P2P cloud network services (Chung and Park, 2016).…”

Section: Review Results and Classificationsmentioning

confidence: 99%

Big data and disaster management: a systematic review and agenda for future research

2017

View full text Add to dashboard Cite

The era of big data and analytics is opening up new possibilities for disaster management (DM). Due to its ability to visualize, analyze and predict disasters, big data is changing the humanitarian operations and crisis management dramatically. Yet, the relevant literature is diverse and fragmented, which calls for its review in order to ascertain its development. A number of publications have dealt with the subject of big data and its applications for minimizing disasters. Based on a systematic literature review, this study examines big data in DM to present main contributions, gaps, challenges and future research agenda. The study presents the findings in terms of yearly distribution, main journals, and most cited papers. The findings also show a classification of publications, an analysis of the trends and the impact of published research in the DM context. Overall the study contributes to a better understanding of the importance of big data in disaster management.

show abstract

Section: Review Results and Classificationsmentioning

confidence: 99%

Big data and disaster management: a systematic review and agenda for future research

2017

View full text Add to dashboard Cite

show abstract

“…In the studies presented in Table 1, few researchers have addressed how agents interact in disaster environments. For many reasons, the utilization of MASs is appropriate in crisis management [4]. They are suitable for finding optimal strategies for widespread incidents and crisis management.…”

Section: Optimal Resource Allocation In Emergency Responsementioning

confidence: 99%

“…Multi agent systems (MASs) making it possible to simulate building demolition, damage to urban infrastructure, injuries, search and rescue teams [4]. MASs deal with complex systems by emphasizing the interaction between agents and dividing the system into sub-sectors of the environment and other actors [5].…”

Section: Introductionmentioning

confidence: 99%

Developing an Agent-Based Simulation System for Post-Earthquake Operations in Uncertainty Conditions: A Proposed Method for Collaboration among Agents

Hooshangi

Alesheikh

2018

IJGI

View full text Add to dashboard Cite

Agent-based modeling is a promising approach for developing simulation tools for natural hazards in different areas, such as during urban search and rescue (USAR) operations. The present study aimed to develop a dynamic agent-based simulation model in post-earthquake USAR operations using geospatial information system and multi agent systems (GIS and MASs, respectively). We also propose an approach for dynamic task allocation and establishing collaboration among agents based on contract net protocol (CNP) and interval-based Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods, which consider uncertainty in natural hazards information during agents' decision-making. The decision-making weights were calculated by analytic hierarchy process (AHP). In order to implement the system, earthquake environment was simulated and the damage of the buildings and a number of injuries were calculated in Tehran's District 3: 23%, 37%, 24% and 16% of buildings were in slight, moderate, extensive and completely vulnerable classes, respectively. The number of injured persons was calculated to be 17,238. Numerical results in 27 scenarios showed that the proposed method is more accurate than the CNP method in the terms of USAR operational time (at least 13% decrease) and the number of human fatalities (at least 9% decrease). In interval uncertainty analysis of our proposed simulated system, the lower and upper bounds of uncertain responses are evaluated. The overall results showed that considering uncertainty in task allocation can be a highly advantageous in the disaster environment. Such systems can be used to manage and prepare for natural hazards.

show abstract

“…Wise's [122] agent-based model of wildfire evacuation demonstrates how, when given a set of parameters for specific scenarios, social simulations in computational models can be used to create data for analysis, predict human behavior, and provide data for policymakers. To assess the longer-term welfare impacts of urban disasters, Grinberger et al [244,245] made several simulations spanning the three years after an earthquake. They simulated the urban dynamics (residential and non-residential capital stock and population dynamics) using both bottom-up (locational choice for workplace, residence, and daily activities) and top-down (land use and housing price) protocols and analyzed the resulting data to find that low-income groups lose both housing and embedded social support systems.…”

Section: Interactions Among the Components Of Cssdmentioning

confidence: 99%

Computational Social Science of Disasters: Opportunities and Challenges

Burger

Kennedy

et al. 2019

Future Internet

View full text Add to dashboard Cite

Disaster events and their economic impacts are trending, and climate projection studies suggest that the risks of disaster will continue to increase in the near future. Despite the broad and increasing social effects of these events, the empirical basis of disaster research is often weak, partially due to the natural paucity of observed data. At the same time, some of the early research regarding social responses to disasters have become outdated as social, cultural, and political norms have changed. The digital revolution, the open data trend, and the advancements in data science provide new opportunities for social science disaster research. We introduce the term computational social science of disasters (CSSD), which can be formally defined as the systematic study of the social behavioral dynamics of disasters utilizing computational methods. In this paper, we discuss and showcase the opportunities and the challenges in this new approach to disaster research. Following a brief review of the fields that relate to CSSD, namely traditional social sciences of disasters, computational social science, and crisis informatics, we examine how advances in Internet technologies offer a new lens through which to study disasters. By identifying gaps in the literature, we show how this new field could address ways to advance our understanding of the social and behavioral aspects of disasters in a digitally connected world. In doing so, our goal is to bridge the gap between data science and the social sciences of disasters in rapidly changing environments.

show abstract

Dynamic agent based simulation of welfare effects of urban disasters

Cited by 27 publications

References 31 publications

Big data and disaster management: a systematic review and agenda for future research

Big data and disaster management: a systematic review and agenda for future research

Developing an Agent-Based Simulation System for Post-Earthquake Operations in Uncertainty Conditions: A Proposed Method for Collaboration among Agents

Computational Social Science of Disasters: Opportunities and Challenges

Contact Info

Product

Resources

About