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2021 | Buch

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Dynamics of Disasters

Impact, Risk, Resilience, and Solutions

herausgegeben von: Ilias S. Kotsireas, Anna Nagurney, Panos M. Pardalos, Arsenios Tsokas

Verlag: Springer International Publishing

Buchreihe : Springer Optimization and Its Applications

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

Based on the “Fourth International Conference on Dynamics of Disasters” (Kalamata, Greece, July 2019), this volume includes contributions from experts who share their latest discoveries on natural and unnatural disasters. Authors provide overviews of the tactical points involved in disaster relief, outlines of hurdles from mitigation and preparedness to response and recovery, and uses for mathematical models to describe natural and man-made disasters. Topics covered include economics, optimization, machine learning, government, management, business, humanities, engineering, medicine, mathematics, computer science, behavioral studies, emergency services, and environmental studies will engage readers from a wide variety of fields and backgrounds.

Inhaltsverzeichnis

Frontmatter

Drone Routing for Post-disaster Damage Assessment

Abstract

We consider drones to support post-disaster damage assessment operations when the disaster-affected area is divided into grids and grids are clustered based on their attributes. Specifically, given a set of drones and a limited time for assessments, we address the problem of determining the grids to scan by each drone and the sequence of visits to the selected grids. We aim to maximize the total priority score collected from the assessed grids while ensuring that the pre-specified coverage ratio targets for the clusters are met. We adapt formulations from the literature developed for electric vehicle routing problems with recharging stations and propose two alternative mixed-integer linear programming models for our problem. We use an optimization solver to evaluate the computational difficulty of solving different formulations and show that both formulations perform similarly. We also develop a practical constructive heuristic to solve the proposed drone routing problem, which can find high-quality solutions rapidly. We evaluate the performance of the heuristic with respect to both mathematical models in a variety of instances with the different numbers of drones and grids.

Birce Adsanver, Elvin Coban, Burcu Balcik

DEA for the Assessment of Regions’ Ability to Cope with Disasters

Abstract

Usually, DEA methods are used for the assessment of a region’s disaster vulnerability. However, most of these methods work with precise values of all the characteristics of the regions. At the same time, in real life, quite often most of the data consists of expert estimates or approximate values. In this regard, we propose to use modified DEA methods, which will take into account inaccuracy of the data. We apply these methods to the evaluation of wildfire preventive measures in the Russian Federation regions.

Fuad Aleskerov, Sergey Demin

The Crisis Classification Component to Strengthen the Early Warning, Risk Assessment and Decision Support in Extreme Climate Events

Abstract

Climate change is considered as being one of the most important challenges of modern times, having multiple and significant impacts on human societies and environment. The negative effects which are revealed through weather extreme events and causing distress and loss of property and human lives will become more intensive in the future, especially in poor countries. Hence, there is an urgent need to develop novelty tools to enhance awareness and preparedness, to assess risks and to support decision-making, aiming to increase the social resilience to climate changes.

The proposed open-source holistic beAWARE framework encompasses technological achievements that enables first responders and authorities to manage efficiently the pre-emergency and emergency phases of a hazardous natural event. Specifically, the Crisis Classification component of beAWARE platform consolidates functionalities to provide dual services: (a) firstly, as an Early-Warning system, aiming to estimate the crisis level of the upcoming extreme conditions such as the hazard of flood, fire or heatwave (pre-emergency phase), and (b) secondly, as a Real-Time Monitoring and Risk Assessment system aiming to assess the risk and support to make accurate and timely decisions, when a crisis has evolved.

Gerasimos Antzoulatos, Anastasios Karakostas, Stefanos Vrochidis, Ioannis Kompatsiaris

Toward Decentralized Decision-Making for Interdependent Infrastructure Network Resilience

Abstract

Interdependence among infrastructure and community networks is an important aspect to consider when planning for disruptive events. Further, decision-makers within different infrastructures often make decentralized decisions to protect and restore their own networks after a disruption. As such, a resilience-based optimization model is extended in various ways to depict different decentralized decision-making structures and hierarchies: divided budget, isolation assumption, and dominance assumption. Among others, social vulnerability scores are used to show the effect of community resilience, and different scenarios are analyzed to reveal the effect of decentralization. The model is illustrated with a system of interdependent electric power, water, and gas infrastructure networks in Shelby County, TN.

Buket Cilali, Nafiseh Ghorbani-Renani, Kash Barker, Andrés D. González

Wavelets in Multi-Scale Time Series Analysis: An Application to Seismic Data

Abstract

Forecasting earthquakes is one of the most important problems in Earth science because of their devastating consequences. Current scientific studies related to earthquake forecasting focus on three key points: when the event will occur, where it will occur, and how large it will be. In this work we investigate the possibility to determine when the earthquake will take place.

We formulate the problem as a multiple change-point detection in the time series. In particular, we refer to the multi-scale formulation described in Fryzlewicz (Ann Stat 46(6B): 3390–3421, 2018). In that paper a bottom-up hierarchical structure is defined. At each stage, multiple neighbor regions which are recognized to correspond to locally constant underlying signal are merged. Due to their multi-scale structure, wavelets are suitable as basis functions, since the coefficients of the representation contain local information. The preprocessing stage involves the discrete unbalanced Haar transform, which is a wavelet decomposition of one-dimensional data with respect to an orthonormal Haar-like basis, where jumps in the basis vectors do not necessarily occur in the middle of their support.

The algorithm is tested on data from a well-characterized laboratory system described in Rouet-Leduc et al. (Geophys Res Lett 44(18): 9276–9282, 2017).

Stefania Corsaro, Pasquale Luigi De Angelis, Ugo Fiore, Zelda Marino, Francesca Perla, Mariafortuna Pietroluongo

Effectiveness of Investments in Prevention of Geological Disasters

Abstract

Research on geological disasters has made several achievements in monitoring, early warning, and risk assessment. Substantial resources are being invested in prevention projects, but, due to geographical and demographical complexity, incompleteness of data, and small number of samples, a quantitative analysis on the number of geological disasters and the entity of investments in their prevention is a difficult problem. In this work, the relation is studied between the amount of resources invested in prevention and the number of geological disasters in subsequent years. The analysis is performed on historical data, using statistical methods and a LSTM recurrent neural network.

Ugo Fiore, Zelda Marino, Francesca Perla, Mariafortuna Pietroluongo, Salvatore Scognamiglio, and Paolo Zanetti

Cyber Crises and Disaster Preparation in Austria: A Survey of Research Projects

Abstract

In this paper, we survey some recent applied research and development projects dealing with threat analysis and disaster scenario generation, preparation, management, and training funded by the security-focused funding scheme KIRAS by the Austrian government, which include efforts for the development and execution of serious games in the respective domains. In our analysis, we found multiple lines of multiyear, multi-project activities, which consistently improve and advance the technologies and capabilities available to the affected stakeholders. Based on this review of the state of the art, we identify areas of high-potential interest to direct future applied research and development efforts.

Bernhard Garn, Klaus Kieseberg, Dominik Schreiber, Dimitris E. Simos

Disaster Preparedness at the Municipality Level: A Scenario-Based Multistage Measurement Methodology

Abstract

This study aims to develop a methodology in the disaster management field to assist policy-makers in the evaluation of the state of disaster preparedness of a municipality considering possible disaster types and corresponding scenarios. The paper first provides an overview of selected past disaster preparedness studies and identifies the main dimensions of preparedness. Then, it integrates them into a comprehensive framework for disaster preparedness measurement. The framework considers multistage aspects of disaster preparedness by integrating the pre- and post-disaster status. Preparedness at the municipality level is evaluated with respect to the following four areas: hazard assessment, mitigation capabilities, resource preparedness, and management performance. Implementing the methodology can provide insights to governments concerning their level of disaster preparedness. By highlighting areas of weakness, it can contribute to strengthening their readiness.

Mehdi Ghazanfari, Mohammadmehdi Hakimifar, Tina Wakolbinger, Fuminori Toyasaki

Development of Flood Disaster Prevention Simulation Smartphone Application Using Gamification

Abstract

In recent years, there have been many serious flood disasters. However, only a few citizens have sufficient knowledge about evacuation from the disaster. In order to reduce the damage, it is necessary for citizens to be aware of the flood risk of the residence, to prepare for disasters, and to take appropriate evacuation action at the time of disasters. For that purpose, citizens need to continuously learn disaster prevention information. Based on the above considerations, we develop a smartphone application for simulating flood disaster evacuation, based on the notion of gamification. In this paper, we report design and development of the disaster prevention smartphone application for citizens, focusing on easiness, continuity (wanting to use the application continuously), learning effects, and improvement of interest.

Yutaka Matsuno, Futaba Fukanuma, Shigenobu Tsuruoka

Natural Disasters and Their Impact on Business Units: The Greek Case

Abstract

This research addresses current situation in terms of Greek companies’ awareness, preparedness, and resilience against catastrophic events. Furthermore, this research aims at recording the implications of catastrophic events on business activities as well as depicting entrepreneurs’ perception of business continuity and post-disaster recovery.

John A. Mpekiaris, George D. Tsiotras

Perishable Food Supply Chain Networks with Labor in the Covid-19 Pandemic

Abstract

The Covid-19 pandemic is a major healthcare disaster that has fundamentally transformed our daily lives and the operations of governments, businesses, healthcare operations, and educational institutions. It has elevated and expanded the role of essential workers, not only in healthcare but also in the food industry. The food industry has undergone major disruptions in the pandemic for reasons including compromised labor resources. In this paper, we develop a supply chain-generalized network optimization framework focused on perishable food. The model explicitly includes labor availability associated with the network economic activities of production, transportation, storage, and distribution in order to quantify the impacts of associated disruptions due to illnesses, physical/social distancing requirements, and decreases in labor productivity. Theoretical results are presented along with a series of numerical examples on a fresh produce product with quantification of a spectrum of pandemic-induced disruptions on product flows, demands, prices, and the profits of the food firm. We also show that including more direct demand markets for fresh produce can yield gains for the firm.

Anna Nagurney

Capacitated Human Migration Networks and Subsidization

Abstract

Large-scale migration flows are posing immense challenges for governments around the globe, with drivers ranging from climate change and disasters to wars, violence, and poverty. In this paper, we introduce multiclass human migration models under user-optimizing and system-optimizing behavior in which the locations associated with migration are subject to capacities. We construct alternative variational inequality formulations of the governing equilibrium/optimality conditions that utilize Lagrange multipliers and then derive formulae for subsidies that, when applied, guarantee that migrants will locate themselves, acting independently and selfishly, in a manner that is also optimal from a societal perspective. An algorithm is proposed, implemented, and utilized to compute solutions to numerical examples. Our framework can be applied by governmental authorities to manage migration flows and population distributions for enhanced societal welfare.

Anna Nagurney, Patrizia Daniele, Giorgia Cappello

Land Property Data Logging on Blockchain Ledger

Abstract

Occurrences of disaster lead to problems in information retrieval about damaged land properties as it is a time-consuming task, and one that entails a lot of bureaucracy. In this paper we will discuss a specific method using blockchain technology and specialized equipment to collect and register land property information. That information is critical for individuals, governments as well as insurance companies for evaluating risk associated with disasters. For reasons of data integrity and transparency, all of this information is available in a form on a public blockchain ledger.

Stamatis Papangelou, Zinos Alexios Charalampidis

Universal Maximum Flow with Intermediate Storage for Evacuation Planning

Abstract

The evacuation planning problem models the process of shifting residents from emergency areas (sources) to safe places (sinks) as quickly and efficiently as possible. Most of the flow over time models used in the evacuation planning are based on the flow conservation constraints, i.e., the inflow should be equal to the outflow on each node except at the sources and sinks. We investigate the universal maximum flow problem with intermediate storage, i.e., the inflow may be greater than the outflow on intermediate nodes which maximizes the number of evacuees leaving the emergency areas at each point of time. We propose efficient algorithms to solve the problem on two-terminal series-parallel and general networks. We also discuss the solution technique for the problem with arc reversal capability and compare these solutions without and with intermediate storage.

Urmila Pyakurel, Stephan Dempe

A General Framework and Control Theoretic Approach for Adaptive Interactive Learning Environments

Abstract

From a system’s theoretical point of view, adaptive learning systems (ALS) for education and training contain in their core – in a simplified form – closed feedback control loops in which the control is determined by the measured users’ performance. Improving this performance can increase the learning outcome, especially for critical disciplines such as education or training for disaster risk management. However, for this special form of intelligent (e-learning) assistance systems, learning theories and behavioral models have to be considered, e.g., game flow theory, cognition models, or learning models. The research question is how adaptive interactive learning environments (ILE) such as serious games and computer simulations can be characterized and analyzed to determine optimal adaptation strategies. Adaptive learning environments should adapt to the context-related needs of the user in order to ensure and optimize learning success, especially for disaster management training. This contribution presents a concept for an interoperable, adaptive ILE framework which follows control theory and its models, contributing to the state of the art for adaptive games or simulations in disaster risk management.

Alexander Streicher, Rainer Schönbein, Stefan Wolfgang Pickl

A Simulation Model for the Analysis of the Consequences of Extreme Weather Conditions to the Traffic Status of the City of Thessaloniki, Greece

Abstract

Natural disasters such as flooding and snow blizzards have evolved from a relatively rare event to a recurring concern for stakeholders, policy makers, and citizens. A special place in this debate is held by the transportation infrastructure; it provides services crucial to a society, and it can yield positive effects to the overall economy due to its interrelation with the urban activities. Finally, due to the increasing trend of urbanization, people are having an increasing dependence on urban transportation.

Consequently, extreme weather conditions could severely impact not only the operation of the transportation infrastructure (network and means) but also the economic activity of a city. Hence, there is the need for a framework that will allow decision-makers, on the one hand, to monitor in real time the status of the transportation network and on the other hand offer them insights on how a critical event, such as a flooding, could affect it before it does.

The purpose of this paper is to present such a tool that allows for efficient and effective monitoring of the status of the transportation network and crisis management in the case of a flooding.

To achieve the objective, two methodological frameworks will be combined: data analytics and simulation. Floating car data (FCD) from a fleet of taxis in the city of the Thessaloniki offer a glimpse on the status of the transportation network. The KPIs that are produced from the data are used as an input to a simulation model. The model has been developed with the methodology of system dynamics, because it allows for the adequate representations of complex systems (such as the transportation infrastructure), it offers a top-down view on the behavior of the system over time, and it can be easily communicated to non-experts.

The model also simulates the physical process of rain and snow, and the user can define how much rain and snow and at which times of the day it will fall. The water accumulates in the road network affecting the speed of the vehicles, and the larger the amount of water the more difficult it is for the sewage system to remove it, thus resulting in flooding roads.

Several scenarios were simulated, mainly trying to capture the dynamics of sudden rainfall and flooding. The results illustrate that there is a disproportional delay between the time that the rain stops and the time it is required for the system to bounce to an equilibrium.

Georgios Tsaples, Josep Maria Salanova Grau, Georgia Aifadopoulou, Panagiotis Tzenos

Titel: Dynamics of Disasters
herausgegeben von: Ilias S. Kotsireas
Anna Nagurney
Panos M. Pardalos
Arsenios Tsokas
Verlag: Springer International Publishing
Electronic ISBN: 978-3-030-64973-9
Print ISBN: 978-3-030-64972-2
DOI: https://doi.org/10.1007/978-3-030-64973-9