LEADER 10912nam 22007335 450 001 9910561294303321 005 20230810175152.0 010 $a3-031-04170-4 024 7 $a10.1007/978-3-031-04170-9 035 $a(MiAaPQ)EBC6953204 035 $a(Au-PeEL)EBL6953204 035 $a(CKB)21511120300041 035 $a(DE-He213)978-3-031-04170-9 035 $a(PPN)262167859 035 $a(EXLCZ)9921511120300041 100 $a20220413d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aInformation Technology in Disaster Risk Reduction $e6th IFIP WG 5.15 International Conference, ITDRR 2021, Morioka, Japan, October 25?27, 2021, Revised Selected Papers /$fedited by Jun Sasaki, Yuko Murayama, Dimiter Velev, Plamena Zlateva 205 $a1st ed. 2022. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022. 215 $a1 online resource (182 pages) 225 1 $aIFIP Advances in Information and Communication Technology,$x1868-422X ;$v638 311 08$aPrint version: Sasaki, Jun Information Technology in Disaster Risk Reduction Cham : Springer International Publishing AG,c2022 9783031041693 320 $aIncludes bibliographical references and index. 327 $aIntro -- Preface -- Organization -- Contents -- Information Analysis for Situation Awareness -- Automatic Calculation of Damage Rate of Roofs Based on Image Segmentation -- 1 Introduction -- 1.1 Problem in Building Damage Investigation -- 1.2 Usage of Aerial Photos Images During Disaster -- 1.3 Study Purpose -- 2 Previous Study -- 3 Development of Automatic Method to Calculate the Rate of Damage on Roof -- 3.1 The Method to Calculate the Rate of Damage on the Roof in the Building Damage Investigation -- 3.2 The Method to Calculate Damage Rate of Roof in This Study -- 3.3 Trimming Algorithm -- 3.4 Shortcomings of this Study -- 3.5 Increase of Data by Division of Roof Surface -- 4 Division of Roof Surface -- 4.1 Previous Study About Roof Surface -- 4.2 Segmentation Model -- 4.3 Used Data -- 4.4 Training Method -- 4.5 Result of Division in the First Experiment -- 4.6 Roof Image with Some Features -- 4.7 Result of Division in Additional Experiment -- 4.8 Image Processing After Division -- 5 Classification of Damage Degree -- 5.1 Classification Model -- 5.2 Data Used -- 5.3 Training Method -- 5.4 Classification Result -- 6 Calculation of Damage Rate -- 6.1 Calculation Method of Estimated Damage Rate -- 6.2 Error of Correct Answer -- 6.3 Comparison of Correct Damage Rate and Estimated Damage Rate -- 6.4 Evaluation of Model Accuracy -- 7 Discussion and Future Tasks -- References -- Flood Disaster Mitigation System Adopting Meteorological Data and Geographic Information Systems -- 1 Introduction -- 2 Related Work -- 3 System Design Requirement Analysis of FDMS Using SD -- 3.1 Outlines of SD -- 3.2 Iceberg Model Analysis -- 3.3 Causal Loop Diagram Analysis -- 3.4 Leverage Points -- 3.5 Observed Data Examples at River -- 3.6 System Concept -- 4 Basic Design and Integration of System -- 4.1 Basic Design of System -- 4.2 Integration of System. 327 $a5 System Verification -- 6 Conclusion -- References -- Flood Disaster Management System for Situation Awareness and Response Using Twitter Data -- 1 Introduction -- 2 Related Work -- 2.1 Situation Awareness -- 2.2 Usage of Social Media for Situation Awareness During Disasters -- 2.3 Disaster Response and Relief -- 2.4 Originality of the Present Study -- 3 System Design -- 3.1 System Configuration -- 3.2 Data Collection -- 3.3 Extraction of Information Location -- 3.4 Web Application -- 3.5 Situation Awareness -- 4 System Development -- 4.1 System Frontend -- 4.2 System Backend -- 4.3 System Operation Environment -- 4.4 Operation Target Area -- 5 Conclusion -- References -- Evacuation and Rescue -- Proposed Evacuation Behavior Model Using Open-Source Data: Flood Disaster Case Study -- 1 Introduction -- 2 Related Studies -- 3 Evacuation Model Concept -- 3.1 Evacuation Decision Process Model -- 3.2 Calculation of Evacuation Shelter Choice Probability -- 3.3 Evacuee Rate Calculation -- 4 Simulation -- 4.1 Algorithm Overview -- 4.2 Target Area and Data -- 4.3 Data Used in the Simulation -- 4.4 Explanatory Variables -- 4.5 Scenarios -- 4.6 Optimization Method -- 4.7 Comparison Between Actual and Predicted Number of Evacuees -- 4.8 Field Survey -- 5 Conclusion -- References -- Agent-Based Tsunami Crowd Evacuation Simulation for Analysis of Evacuation Start Time and Disaster Rate in Zushi City -- 1 Introduction -- 2 Methods -- 2.1 Modelling of Target Areas -- 2.2 Tsunami Model and Evacuation Behaviour of Agents -- 3 Results and Discussion -- 4 Conclusion -- References -- Rescue Strategy in Case of Large-Scale Flood Damage in the Koto Delta Region -- 1 Introduction -- 2 Research Background -- 2.1 Characteristics of Flooding in the Koto Delta Region -- 2.2 Issues of Wide-Area Evacuation -- 3 Previous Studies and Purpose of this Study. 327 $a4 Estimation of Changes in the Number of Isolated People in the Koto Delta Region When Rescue Operations are Conducted -- 4.1 Data and Estimation Methods -- 4.2 Comparison of the Difference in the Size of Population Aggregation Unit Area -- 4.3 Comparison of the Difference in the Order of Rescuing Isolated People -- 4.4 Comparison of the Difference in the Evacuation Rate of the Residents on Upper Floors -- 4.5 Estimation of the Evacuation Rate Required to Complete Rescue in 7 days -- 5 Summary and Future Work -- References -- COVID-19 Issues -- Trial of Building a Resilient Face-To-Face Classroom Based on CO2-Based Risk Awareness -- 1 Introduction -- 2 Case Study and Issues -- 2.1 Case Study -- 2.2 Ventilation Issues -- 3 Measurement System -- 4 Experiments -- 4.1 Measurement at an Event -- 4.2 Measurement of the Whole Campus -- 5 Conclusion -- References -- Analysis of Quote Retweets for COVID-19 State of Emergency Related Tweets Posted from Prefectural Governors' Accounts in Japan -- 1 Introduction -- 2 Related Studies -- 3 Analysis of Tweets -- 3.1 Governors' Accounts to Be Analyzed -- 3.2 Tweets Collection -- 3.3 Analysis of Quote Retweets of Governors' Tweets -- 4 Conclusions -- References -- Insights from the COVID-19 Pandemic for Systemic Risk Assessment and Management -- 1 Introduction -- 1.1 Have We Learnt Enough from COVID-19 to Manage New Pandemic Waves Better? -- 1.2 The Aim of This Paper and How It Is Organized -- 2 Characteristics of a Major Pandemic in the Globalization Era -- 3 Risks in the Light of Systemic Interdependencies -- 3.1 Adequate Risk Definition in the Presence of Dynamic Complexity -- 3.2 Quantitative Analysis of Qualitative Models of Systemic Risk -- 4 Order of Magnitude of Pandemic Cascading Effects -- 4.1 The Risk Systemicity Approach -- 4.2 Cascading Effects as Vicious Cycles -- 5 Discussion -- References. 327 $aIT Use for Risk and Disaster Management -- Leveraging Geospatial Technology in Disaster Management -- 1 Introduction -- 1.1 Disaster Scenario -- 1.2 Geospatial Technology -- 1.3 Geospatial Technology Innovations in Disaster Management -- 2 Karnataka State Disaster Management Information System (KSDMIS) - A Geospatial Web Application for Collecting Data on Disaster Events -- 2.1 About Karnataka -- 2.2 Objectives of KSDMIS Application Software -- 2.3 Benefits of KSDMIS Application Software -- 2.4 KSDMIS Application Software Structure -- 2.5 Mobile Interface for Data Collection and Updation -- 2.6 Web Interface to the KSDMIS Application Software -- 2.7 Adopting Geospatial Technology in KSDMIS -- 3 Geospatial Enabled - District Disaster Management Plan - GEDDMP System -- 3.1 Operation Strategy -- 3.2 Continuous Data Updation -- 3.3 Benefits of Geospatial Enabled-DDMP System -- 3.4 Geospatial Technology for Different Phases of Disaster Management -- 3.5 Geospatial Enabled DDMP Application Software Structure -- 3.6 Core Technology of Geospatial-DDMP Application Software -- 3.7 Workflow -- 3.8 Features of Survey Template HOOKs -- 3.9 Roadmap and Futuristic View -- References -- Information Technologies for Assessing the Effectiveness of the Quarantine Measures -- 1 Importance of Quarantine Measures -- 2 Variety of Quarantine Measures -- 3 Application of Restrictive Measures and the Need to Assess Their Efficiency -- 4 Selection of Modelling Approach for Quarantine Measure Efficiency -- 5 Overview of Epidemiology Models -- 6 Model Description and Development of Software -- 7 Model Description and Software Development -- 8 Discussion of Modelling Results -- 9 Scope of Application and Further Improvement of the Simulation Tool -- 10 Comparison of Modeling Results -- 11 Conclusions -- References -- Author Index. 330 $aThis volume constitutes the refereed and revised post-conference proceedings of the 6th IFIP WG 5.15 International Conference on Information Technology in Disaster Risk Reduction, ITDRR 2021, in Morioka, Japan, in October 2021. The 11 full papers presented were carefully reviewed and selected from 18 submissions. The papers focus on various aspects and challenges of coping with disaster risk reduction. The papers are categorized in the following topical subheadings: Information Analysis for Situation Awareness; Evacuation and Rescue; COVID-19 Issues; and IT Use for Risk and Disaster Management. . 410 0$aIFIP Advances in Information and Communication Technology,$x1868-422X ;$v638 606 $aApplication software 606 $aComputer engineering 606 $aComputer networks 606 $aCoding theory 606 $aInformation theory 606 $aSocial sciences$xData processing 606 $aComputer and Information Systems Applications 606 $aComputer Engineering and Networks 606 $aCoding and Information Theory 606 $aComputer Application in Social and Behavioral Sciences 615 0$aApplication software. 615 0$aComputer engineering. 615 0$aComputer networks. 615 0$aCoding theory. 615 0$aInformation theory. 615 0$aSocial sciences$xData processing. 615 14$aComputer and Information Systems Applications. 615 24$aComputer Engineering and Networks. 615 24$aCoding and Information Theory. 615 24$aComputer Application in Social and Behavioral Sciences. 676 $a353.950285 676 $a353.950285 702 $aSasaki$b Jun 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910561294303321 996 $aInformation Technology in Disaster Risk Reduction$92834273 997 $aUNINA