A Scenario-Based and Game-Based Geographical Information System (GIS) Approach for Earthquake Disaster Simulation and Crisis Mitigation

The current research study aims to introduce the experience of implementing a serious game using the concept of game-based GIS approach for crisis management during earthquake disasters. In this study, we aimed to develop a game-based GIS approach and examine its efficiency for simulating earthquake rescue management in Tabriz city. In designing this game, typical scenario-based, game-based GIS methods and techniques were employed, and the proposed approach was applied to crisis management. To achieve this goal, we addressed the technical details regarding the development and implementation of the scenario-based and game-based GIS approach. Based on the results, game-based simulations can be considered an efficient approach for disaster simulation and can improve the skills of rescue teams. The outcome of this application is an intellectual game that almost all users at any age can play, and the game can challenge their ability to solve critical issues. The results are critical for explaining the effectiveness of rescue teams and crisis management facilities. As we intended to develop an approach for the simulation of earthquake disasters and emergency responses, we therefore conclude that the results of this study can also be employed to improve the skills of rescue teams and citizens for dealing with crises resulting from earthquake disasters. As a result of this research, the developed tool is published, together with this paper, as an open source and can be employed for any scenario-based analysis in other case studies. By presenting a-state-of-the-art approach, the results of this research study can provide significant contribution to further the development of GIScience and its applications for disaster and risk mitigation and management.Deutsche Forschungsgemein-schaft (DFG, German Research Foundation)Open Access Publication Fund of Humboldt-Universität zu BerlinPeer Reviewe

A Methodology for Assessing Dynamic Resilience of Coastal Cities to Climate Change Influenced Hydrometeorological Disasters

Confronted with rapid urbanization, intensified tourism, population densification, increased migration, and climate change impacts, coastal cities are facing more challenges now than ever before. Traditional disaster management approaches are no longer sufficient to address the increased pressures facing urban areas. A paradigm shift from disaster risk reduction to disaster resilience building strategies is required to provide holistic, integrated, and sustainable disaster management looking forward. To address some of the shortcomings in current disaster resilience assessment research, a mathematical and computational framework was developed to help quantify, compare, and visualize dynamic disaster resilience. The proposed methodological framework for disaster resilience combines physical, economic, engineering, health, and social spatio-temporal impacts and capacities of urban systems in order to provide a more holistic representation of disaster resilience. To capture the dynamic spatio-temporal characteristics of resilience and gauge the effectiveness of potential climate change adaptation options, a disaster resilience simulator tool (DRST) was developed to employ the mathematical framework. The DRST is applied to a case study in Metro Vancouver, British Columbia, Canada. The simulation model focuses on the impacts of climate change-influenced riverine flooding and sea level rise for three future climates based on the results of the CGCM3 global climate model and two (2) future emissions scenarios. The output of the analyses includes a dynamic set of resilience maps and graphs to demonstrate changes in disaster resilience in both space and time. The DRST demonstrates the value of a quantitative resilience assessment approach to disaster management. Simulation results suggest that various adaptation options such as access to emergency funding, provision of mobile hospital services, and managed retreat can all help to increase disaster resilience. Results also suggest that, at a regional scale, Metro Vancouver is relatively resilient to climate change influenced-hydrometeorological hazards, however it is not distributed proportionately across the region. Although a pioneering effort by nature, the methodological and computational framework behind the DRST could ultimately provide decision support to disaster management professionals, policy makers, and urban planners

A Disaster Relief Inventory Model Based on Transshipment

This research study is an effort to shed light on how transshipment may help improve the management of inventory in a disaster relief system. System dynamics simulation was used to compare inventory control and costs in a humanitarian supply chain without transshipment vs. one with transshipment. A framework for this approach is given along with the results of simulations on a system consisting of two warehouses where transshipment is allowed compared to the alternative where transshipment is not allowed. The preliminary results of this study indicate that transshipment can reduce costs and improve service to disaster victims based on inventory levels maintained in the warehouses. In some cases, transshipment may be more expensive, but this assumes the cost of replenishing inventory as a result of emergency purchase costs

STRATEGI PENANGANAN BANJIR BERBASIS MITIGASI BENCANA PADA KAWASAN RAWAN BENCANA BANJIR DI DAERAH ALIRAN SUNGAI SEULALAH KOTA LANGSA

Langsa City is a city located in Aceh Province, Indonesia. Recently there was heavy rain in the upstream area of the Krueng Langsa River Basin, which resulted in flooding in the area and overflowing of the river in Langsa Kota Subdistrict, Langsa City, Aceh. According to the head of the Aceh Disaster Management Agency (BPBA), Sunawardi, the overflowing of the Krueng Langsa hit the Java Rear Village and Seulalah Village with the water level reaches 50 centimeters. The purpose of this research is to identify studies of flood disaster risk in flood prone areas in Langsa City, and formulate flood mitigation-based strategies for disaster mitigation in flood prone areas in Langsa City. This research uses quantitative analysis method with overlay/superimpose analysis approach to analyze the level of vulnerability, hazard level and risk level of flood disaster. Strategy for flood mitigation based on disaster mitigation in flood prone areas in Langsa is divided into two, namely: (1). Structural mitigation in the form of explanations regarding the construction of flood control buildings such as making embankments, making drainage network structures, and making drop structures; (2) Non-structural mitigation in the form of training and simulation of disaster mitigation, as well as evaluating policies on reducing the risk of flood disasters in flood prone areas in Langsa City, Aceh, Indonesia

Developing an Adaptive Building Evacuation Simulation and Decision Support Framework using Cognitive Agent-Based Modelling

Preparing for an unprecedented event involving the movement of populations could take up large amounts of resources if done conventionally. The main motivation of this study is the behavioural modification approach which is an underexplored potential in evacuation dynamics, offering new possibilities in terms of practicality and ease of implementation. This paper tackles an adaptive building evacuation simulation and decision support framework that will serve as a guide to evaluate and propose evacuation strategies for disaster management researchers and decision-making authorities. The framework mainly involves the formulation of the cognitive agent model, the evacuation simulation, and the decision support. The timeliness in the Philippine context of the long-overdue “Big One” earthquake, the vulnerability of the case study, and the capability of the framework to be a standard guide where components can be customized by users based on the disaster type and site-specific requirements make this research a significant undertaking

The contribution of tsunami evacuation analysis to evacuation planning in Chile: Applying a multi-perspective research design

Research on evacuation behavior in natural disasters provides a valuable contribution in the development of effective short- and long-term strategies in disaster risk management (DRM). Many studies address evacuation simulation utilizing mathematical modeling approaches or GIS-based simulation. In this contribution, we perform a detailed analysis of an entire evacuation process from the decision to evacuate right up to the arrival at a safe zone. We apply a progressive research design in the community of Talcahuano, Chile by means of linking a social science approach, deploying standardized questionnaires for the tsunami affected population, and a GIS-based simulation. The questionnaire analyzes evacuation behavior in both an event-based historical scenario and a hypothetical future scenario. Results reveal three critical issues: evacuation time, distance to the evacuation zone, and method of transportation. In particular, the excessive use of cars has resulted in congestion of street sections in past evacuations, and will most probably also pose a problem in a future evacuation event. As evacuation by foot is generally recommended by DRM, the results are extended by a GIS-based modeling simulating evacuation by foot. Combining the findings of both approaches allows for added value, providing more comprehensive insights into evacuation planning. Future research may take advantage of this multi-perspective research design, and integrate social science findings in a more detailed manner. Making use of invaluable local knowledge and past experience of the affected population in evacuation planning is likely to help decrease the magnitude of a disaster, and, ultimately, save lives

Selecting a Temporary Debris Management Site for Effective Debris Removal

The overall debris removal after disasters is often prolonged due to the huge amount of debris and lack of capacities such as a Temporary Debris Management Site (TDMS) in the community. This results in a delay of overall recovery and increases the total recovery cost. Strategic planning and building a TDMS will help in providing extra time for proper disposal of debris and clearing a disaster-impacted site that will facilitate the reconstruction process. This paper presents a unique approach for identifying and selecting TDMS locations for expediting debris removal from the community. A hypothetical example of a community impacted by a natural hazard is presented to explain how the the proposed model works. The research integrates data from a loss assessment report obtained from HAZUS-MH, Post Disaster Needs Assessment (PDNA), and Geographical Information System (GIS) in a dynamic simulation model. Various TDMS locations could be evaluated based on the existing capacity and infrastructure services and considering factors such as overall debris removal time, associated cost, productivity, and availability of resources. Debris management teams would greatly benefit from the research for strategically siting TDMS for accelerating the debris removal process

AGENT-BASED DISCRETE EVENT SIMULATION MODELING AND EVOLUTIONARY REAL-TIME DECISION MAKING FOR LARGE-SCALE SYSTEMS

Computer simulations are routines programmed to imitate detailed system operations. They are utilized to evaluate system performance and/or predict future behaviors under certain settings. In complex cases where system operations cannot be formulated explicitly by analytical models, simulations become the dominant mode of analysis as they can model systems without relying on unrealistic or limiting assumptions and represent actual systems more faithfully. Two main streams exist in current simulation research and practice: discrete event simulation and agent-based simulation. This dissertation facilitates the marriage of the two. By integrating the agent-based modeling concepts into the discrete event simulation framework, we can take advantage of and eliminate the disadvantages of both methods.Although simulation can represent complex systems realistically, it is a descriptive tool without the capability of making decisions. However, it can be complemented by incorporating optimization routines. The most challenging problem is that large-scale simulation models normally take a considerable amount of computer time to execute so that the number of solution evaluations needed by most optimization algorithms is not feasible within a reasonable time frame. This research develops a highly efficient evolutionary simulation-based decision making procedure which can be applied in real-time management situations. It basically divides the entire process time horizon into a series of small time intervals and operates simulation optimization algorithms for those small intervals separately and iteratively. This method improves computational tractability by decomposing long simulation runs; it also enhances system dynamics by incorporating changing information/data as the event unfolds. With respect to simulation optimization, this procedure solves efficient analytical models which can approximate the simulation and guide the search procedure to approach near optimality quickly.The methods of agent-based discrete event simulation modeling and evolutionary simulation-based decision making developed in this dissertation are implemented to solve a set of disaster response planning problems. This research also investigates a unique approach to validating low-probability, high-impact simulation systems based on a concrete example problem. The experimental results demonstrate the feasibility and effectiveness of our model compared to other existing systems

Disaster management and emerging technologies: a performance-based perspective

Purpose – This paper aims to analyse how emerging technologies (ETs) impact on improving performance in disaster management (DM) processes and, concretely, their impact on the performance according to the different phases of theDM cycle (preparedness, response, recovery and mitigation). Design/methodology/approach – The methodology is based on a systematic review of the literature. Scopus, ProQuest, EBSCO and Web of Science were used as data sources, and an initial sample of 373 scientific articles was collected. After abstracts and full texts were read and refinements to the search were made, a final corpus of 69 publications was analysed using VOSviewer software for text mining and cluster visualisation. Findings – The results highlight how ETs foster the preparedness and resilience of specific systems when dealing with different phases of the DM cycle. Simulation and disaster risk reduction are the fields of major relevance in the application of ETs to DM. Originality/value – This paper contributes to the literature by adding the lenses of performance measurement, management and accountability in analysing the impact of ETs on DM. It thus represents a starting point for scholars to develop future research on a rapidly and continuously developing topic

Considering Disaster Volunteer Behavior and the Work Environment in Managerial Decision Making

Over the last two decades, large-scale disaster events have significantly increased in frequency and intensity, causing tremendous loss of lives and property. A large number of relief organizations rely on their volunteers to respond to many disasters around the globe, serving people and communities in need. While their contributions are priceless, turnover among disaster volunteers has become a significant problem for these relief organizations. Work environment factors, such as volunteers being mismatched with tasks, unsuitable workloads, and conflict within groups of volunteers may give rise to turnover intentions, which may in turn lead to actual turnover. The link between work environment factors and volunteer turnover intentions in these situations has not yet received considerable attention in terms of quantitative research. Therefore, the purpose of this dissertation is to develop quantitative models that consider the factors that may cause turnover or turnover intentions. The goal of these models is to help decision makers for non-governmental organizations (NGOs) better manage their disaster volunteers during relief efforts, with the aim of satisfying community needs and improving volunteer retention rates. The first study addresses a gap in volunteer staff planning and scheduling where volunteer training is first presented, with volunteer turnover represented as a percentage of volunteer–task mismatch. We have developed a mixed-integer programming model for assigning optimal volunteer assignments based on a range of possible short- and long-term community need scenarios. The objective is to minimize the costs of unmet community needs, volunteer attrition due to mismatch assignments, and volunteer expenses. Under different demand scenarios, the optimum solution of volunteer assignment is to allow unskilled volunteers to start training early so that they can help skilled volunteers when a peak of long-term skilled task demand is expected. The second study investigates the effects of work environment factors on the satisfaction level and turnover intentions of disaster volunteers. Using an online survey, data from 386 disaster volunteers are collected and analyzed. Confirmatory factor analysis (CFA) and structural equation modeling are used to test the measurement model and answer research questions focused on volunteer behavior. After assessing and confirming the measurement model, we use the structural model to test the hypotheses and provide prediction equations. Job-fit, training, workload, volunteer group, and supervisor are the key work environment factors considered in this study. The findings suggest that these work environment factors have a positive significant relationship with satisfaction and a negative significant relationship with turnover intentions. The last study focuses on developing a simulation modeling approach that considers a volunteer’s satisfaction and turnover intentions in relation to management decisions of an NGO during a relief event. We use a survey to gather information from disaster volunteer managers about how they manage their volunteer teams and use this information and the findings from the second study to model a realistic relief event. We develop a hybrid simulation model, agent based and discrete event (AB-DE), that handles volunteer task and location assignments, as well as workload. Using data analysis from the surveys, we also introduce a group conflict variable within the simulation model. We evaluate the impact of different management decisions on unmet community needs, as well as on volunteer satisfaction and turnover intentions from the organization. This study uses a numerical example based on the survey data. Considering the scenario in which disaster volunteer managers do not assign heavy workload to disaster volunteers, the results of this study suggest that as a surplus of available volunteers’ increases, the overall satisfaction increases while the turnover intention decreases due to dissatisfaction with a non-essential workload as well as from group conflict. In contrast, when the number of volunteers is less than what is needed, disaster volunteers’ satisfaction and turnover intentions were not affected even if there is high group conflict due to the positive effect of the workload that offsets the negative impact of the group conflict