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Smart Disaster Risk Reduction and Emergency Management in the Built Environment

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Industry 4.0 for the Built Environment

Abstract

Smart technologies such as artificial intelligence, the Internet of Things, and other cyber-physical systems are often associated to Industry 4.0 given their potential for transforming current manufacturing and industrial practices. In particular, the significant potential of these technologies for increasing automation, improving communication and self-monitoring, and optimizing production overall for industries is well known. However, the influential power of these technologies is not bounded by these applications and has significant potential for fields such as disaster risk reduction and emergency management. In this context, the proposed chapter discusses several applications of digital technologies and innovations from Industry 4.0 in these fields, such as big data, the Internet of Things and machine learning techniques for big data analytics. Additionally, research and governance needs in this context are highlighted, as well as certain challenges to widespread and mainstream the reliable use of these technologies in disaster management.

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References

  1. CRED: Natural disasters 2019: Now is the time to not give up. Université Catholique de Louvain, Centre for Research on the Epidemiology of Disasters, Brussels, Belgium (2020)

    Google Scholar 

  2. United Nations General Assembly: Transforming our world—the 2030 agenda for sustainable development, outcome document of the United Nations summit for the adoption of the post-2015 agenda, RES/A/70/L.1. United Nations, New York (2015)

    Google Scholar 

  3. Minges, M.: Disruptive Technologies and Their Use in Disaster Risk Reduction and MANAGEMENT. International Telecommunication Union, Geneva (2019)

    Google Scholar 

  4. UNISDR: Progress and Challenges in Disaster Risk Reduction: A Contribution Towards the Development of Policy Indicators for the Post-2015 Framework for Disaster Risk Reduction. United Nations Office for Disaster Risk Reduction (2014)

    Google Scholar 

  5. Enia, J.: Is there an international disaster risk reduction regime? Does it matter? Progr. Disaster Sci. 100098 (2020)

    Google Scholar 

  6. McEntire, D. A.: Disaster response and recovery: strategies and tactics for resilience. John Wiley & Sons (2015)

    Google Scholar 

  7. Ranghieri, F., Ishiwatari, M. (Eds.): Learning from megadisasters: lessons from the Great East Japan Earthquake. The World Bank (2014)

    Google Scholar 

  8. Hochrainer-Stigler, S., Colon, C., Boza, G., Poledna, S., Rovenskaya, E., Dieckmann, U.: Enhancing resilience of systems to individual and systemic risk: steps toward an integrative framework. Int. J. Disaster Risk Reduct. 101868 (2020)

    Google Scholar 

  9. Centeno, M.A., Nag, M., Patterson, T.S., Shaver, A., Windawi, A.J.: The emergence of global systemic risk. Ann. Rev. Sociol. 41, 65–85 (2015)

    Google Scholar 

  10. Mazzocchi, M., Hansstein, F., Ragona, M.: The 2010 volcanic ash cloud and its financial impact on the European airline industry. CESifo Forum 11(2), 92–100 (2010)

    Google Scholar 

  11. Chongvilaivan, A.: Thailand’s 2011 flooding: its impact on direct exports, and disruption of global supply chains. ARTNeT Working Paper No. 113. Bangkok, Thailand: UNESCAP (2012)

    Google Scholar 

  12. Nicola, M., Alsafi, Z., Sohrabi, C., Kerwan, A., Al-Jabir, A., Iosifidis, C., Agha, M., Agha, R.: The socio-economic implications of the coronavirus pandemic (COVID-19): a review. Int. J. Surg. 78, 185–193 (2020)

    Google Scholar 

  13. Mann, M.E., Lloyd, E.A., Oreskes, N.: Assessing climate change impacts on extreme weather events: the case for an alternative (Bayesian) approach. Clim. Change 144(2), 131–142 (2017)

    Google Scholar 

  14. Hasse, D., Gauthier, F.A., de Rolt, C.R., Klein, G.H.: Coordinating emergency response by competent teams. IADIS Int. J. Comput. Sci. Inf. Syst. 13(1), 33–51 (2018)

    Google Scholar 

  15. Endsley, M.R., Jones, D.G.: Designing for Situation Awareness: An Approach to User-Centered Design, 2nd edn. CRC Press, Boca Raton (2016)

    Google Scholar 

  16. Liu, B., Siu, Y.L., Mitchell, G.: Hazard interaction analysis for multi-hazard risk assessment: a systematic classification based on hazard-forming environment. Nat. Hazard. 16(2), 629–642 (2016)

    Google Scholar 

  17. Menoni, S., Boni, M. P.: A Systemic Approach for Dealing with Chained Damages Triggered by Natural Hazards in Complex Human Settlements (2020)

    Google Scholar 

  18. De Grove, T., Poljansek, K., Ehrlich, D.: Recording Disaster Losses. Recommendations for a European Research. JRC Scientific and Policy reports. Joint Research Centre, European Commission (2013)

    Google Scholar 

  19. Romão, X., Paupério, E.: A framework to assess quality and uncertainty in disaster loss data. Nat. Hazards 83(2), 1077–1102 (2016)

    Google Scholar 

  20. Danielsson, E., Alvinius, A., Larsson, G.: From common operating picture to situational awareness. Int. J. Emerg. Manage. 10(1), 28–47 (2014)

    Google Scholar 

  21. Skakun, S., Kussul, N., Shelestov, A., Kussul, O.: Flood hazard and flood risk assessment using a time series of satellite images: a case study in Namibia. Risk Anal. 34, 1521–1537 (2014)

    Google Scholar 

  22. Yebra, M., Chuvieco, E., Riaño, D.: Estimation of live fuel moisture content from MODIS images for fire risk assessment. Agric. For. Meteorol. 148(4), 523–536 (2008)

    Google Scholar 

  23. Dahigamuwa, T., Yu, Q., Gunaratne, M.: Feasibility study of land cover classification based on normalized difference vegetation index for landslide risk assessment. Geosciences 6(4), 45 (2016)

    Google Scholar 

  24. Ehrlich, D., Kemper, T., Blaes, X., Soille, P.: Extracting building stock information from optical satellite imagery for mapping earthquake exposure and its vulnerability. Nat. Hazards 68, 79–95 (2013)

    Google Scholar 

  25. Tian, J., Nielsen, A.A., Reinartz, P.: Building damage assessment after the earthquake in Haiti using two post-event satellite stereo imagery and DSMs. Int. J. Image Data Fusion 6(2), 155–169 (2015)

    Google Scholar 

  26. Finn, R.L., Wright, D.: Unmanned aircraft systems: surveillance, ethics and privacy in civil applications. Comput. Law Secur. Rev. 28(2), 184–194 (2012)

    Google Scholar 

  27. Matin, M. A., Islam, M. M.: Overview of wireless sensor network. Wireless Sensor Networks-Technology and Protocols, pp. 1–3 (2012). https://bit.ly/34hC82G. Last accessed 2020/11/30

  28. Aslan, Y.E., Korpeoglu, I., Ulusoy, Ö.: A framework for use of wireless sensor networks in forest fire detection and monitoring. Comput. Environ. Urban Syst. 36(6), 614–625 (2012)

    Google Scholar 

  29. Nguyen, C. D., Tran, T. D., Tran, N. D., Huynh, T. H., Nguyen, D. T.: Flexible and efficient wireless sensor networks for detecting rainfall-induced landslides. Int. J. Distrib. Sens. Netw. 11(11), 235954 (2015)

    Google Scholar 

  30. Hu, X., Wang, B., Ji, H.: A wireless sensor network-based structural health monitoring system for highway bridges. Comput. Aided Civil Infrastruct. Eng. 28(3), 193–209 (2013)

    Google Scholar 

  31. Swartz, R.A., Lynch, J.P., Zerbst, S., Sweetman, B., Rolfes, R.: Structural monitoring of wind turbines using wireless sensor networks. Smart Struct. Syst. 6(3), 183–196 (2010)

    Google Scholar 

  32. Erdelj, M., Natalizio, E., Chowdhury, K.R., Akyildiz, I.F.: Help from the sky: Leveraging UAVs for disaster management. IEEE Pervasive Comput. 16(1), 24–32 (2017)

    Google Scholar 

  33. Khalil, I.M., Khreishah, A., Ahmed, F., Shuaib, K.: Dependable wireless sensor networks for reliable and secure humanitarian relief applications. Ad Hoc Netw. 13, 94–106 (2014)

    Google Scholar 

  34. Tuna, G., Gungor, V.C., Gulez, K.: An autonomous wireless sensor network deployment system using mobile robots for human existence detection in case of disasters. Ad Hoc Netw. 13, 54–68 (2014)

    Google Scholar 

  35. Patil, H. K., Chen, T. M.: Wireless sensor network security: The internet of things. In: Vacca, J.R. (Ed.) Computer and Information Security Handbook, 3rd Ed., 317–337. Elsevier (2017)

    Google Scholar 

  36. Aktas, M. S., Astekin, M.: Provenance aware run‐time verification of things for self‐healing Internet of Things applications. Concurr. Comput. Pract. Exp. 31(3), e4263 (2019)

    Google Scholar 

  37. Farash, M.S., Turkanović, M., Kumari, S., Hölbl, M.: An efficient user authentication and key agreement scheme for heterogeneous wireless sensor network tailored for the Internet of Things environment. Ad Hoc Netw. 36, 152–176 (2016)

    Google Scholar 

  38. Abdulwahid, W.M., Pradhan, B.: Landslide vulnerability and risk assessment for multi-hazard scenarios using airborne laser scanning data (LiDAR). Landslides 14(3), 1057–1076 (2017)

    Google Scholar 

  39. Gibson, L., Adeleke, A., Hadden, R., Rush, D.: Spatial metrics from LiDAR roof mapping for fire spread risk assessment of informal settlements in Cape Town, South Africa. Fire Safety J. 103053 (2020)

    Google Scholar 

  40. Chen, B., Krajewski, W.F., Goska, R., Young, N.: Using LiDAR surveys to document floods: a case study of the 2008 Iowa flood. J. Hydrol. 553, 338–349 (2017)

    Google Scholar 

  41. Moya, L., Yamazaki, F., Liu, W., Yamada, M.: Detection of collapsed buildings due to the 2016 Kumamoto, Japan, earthquake from LiDAR data. Nat. Hazard. 17, 143–156 (2017)

    Google Scholar 

  42. Bisson, M., Spinetti, C., Neri, M., Bonforte, A.: Mt. Etna volcano high-resolution topography: airborne LiDAR modelling validated by GPS data. Int. J. Digit. Earth 9(7), 710–732 (2016)

    Google Scholar 

  43. Goldenberg, S., Gopalakrishnan, S., Tallapragada, V., Quirino, T., Marks, F., Jr., Trahan, S., Zhang, X., Atlas, R.: The 2012 triply nested, high-resolution operational version of the Hurricane Weather Research and Forecasting Model (HWRF): track and intensity forecast verifications. Weather Forecast. 30(3), 710–729 (2015)

    Google Scholar 

  44. Murakami, H., Vecchi, G., Underwood, S., Delworth, T., Wittenberg, A., Anderson, W., Chen, J.-H., Gudgel, R., Harris, L., Lin, S.-J., Zeng, F.: Simulation and prediction of category 4 and 5 hurricanes in the high-resolution GFDL HiFLOR coupled climate model. J. Clim. 28(23), 9058–9079 (2015)

    Google Scholar 

  45. Heitzler, M., Lam, J., Hackl, J., Adey, B., Hurni, L.: A simulation and visualization environment for spatiotemporal disaster risk assessments of network infrastructures. Cartographica Int. J. Geogr. Inf. Geovis. 52(4), 349–363 (2017)

    Google Scholar 

  46. Lin, N., Shullman, E.: Dealing with hurricane surge flooding in a changing environment: part I. Risk assessment considering storm climatology change, sea level rise, and coastal development. Stochastic Environ. Res. Risk Assess. 31(9), 2379–2400 (2017)

    Google Scholar 

  47. Clare, R., Bradley, B., Sun, D., Bae, S., Mc Gann, C.: QuakeCoRE and NeSI’s strategic partnership towards earthquake resilience via High Performance Computing. In: eResearch NZ Conference, New Zealand (2016)

    Google Scholar 

  48. Rathje, E., Dawson, C., Padgett, J., Pinelli, J., Stanzione, D., Adair, A., Arduino, P., Brandenberg, S., Cockerill, T., Dey, C., Esteva, M., Haan, F., Hanlon, M., Kareem, A., Lowes, L., Mock, S., Mosqueda, G.: DesignSafe: new cyberinfrastructure for natural hazards engineering. Nat. Hazard. Rev. 18(3), 06017001 (2017)

    Google Scholar 

  49. Wang, F., Magoua, J., Li, N., Fang, D.: Assessing the impact of systemic heterogeneity on failure propagation across interdependent critical infrastructure systems. Int. J. Disaster Risk Reduct. 50, 101818 (2020)

    Google Scholar 

  50. Dong, S., Yu, T., Farahmand, H., Mostafavi, A.: Probabilistic modeling of cascading failure risk in interdependent channel and road networks in urban flooding. Sustain. Cities Soc. 62, 102398 (2020)

    Google Scholar 

  51. An, L.: Modeling human decisions in coupled human and natural systems: review of agent-based models. Ecol. Model. 229, 25–36 (2012)

    Google Scholar 

  52. Wang, Z., Jia, G.: A novel agent-based model for tsunami evacuation simulation and risk assessment. Nat. Hazards (2020). https://doi.org/10.1007/s11069-020-04389-8

    Article  Google Scholar 

  53. Aros, S., Gibbons, D.: Exploring communication media options in an inter-organizational disaster response coordination network using agent-based simulation. Eur. J. Oper. Res. 269(2), 451–465 (2018)

    MATH  Google Scholar 

  54. Hajhashemi, E., Murray-Tuite, P., Hotle, S., Wernstedt, K.: Using agent-based modeling to evaluate the effects of Hurricane Sandy’s recovery timeline on the ability to work. Transp. Res. Part D Transp. Environ. 77, 506–524 (2019)

    Google Scholar 

  55. Sun, Z., Lorscheid, I., Millington, J., Lauf, S., Magliocca, N., Groeneveld, J., Balbi, S., Nolzen, H., Müller, B., Schulze, J., Buchmann, C.: Simple or complicated agent-based models? A complicated issue. Environ. Model. Softw. 86, 56–67 (2016)

    Google Scholar 

  56. Batista e Silva, F., Lavalle, C., Koomen, E.: A procedure to obtain a refined European land use/cover map. J. Land Use Sci. 8(3), 255–283 (2013)

    Google Scholar 

  57. Freire, S., Aubrecht, C.: Integrating population dynamics into mapping human exposure to seismic hazard. Nat. Hazards Earth Syst. Sci. 12(11) (2012)

    Google Scholar 

  58. Mohanty, M., Simonovic, S.: Understanding dynamics of population flood exposure in Canada with multiple high-resolution population datasets. Sci. Total Environ. 143559 (2020)

    Google Scholar 

  59. e Silva, F., Forzieri, G., Herrera, M., Bianchi, A., Lavalle, C., Feyen, L.: HARCI-EU, a harmonized gridded dataset of critical infra-structures in Europe for large-scale risk assessments. Sci. Data 6(1), 1–11 (2019)

    Google Scholar 

  60. Wieland, M., Pittore, M.: A spatio-temporal building exposure database and information life-cycle management solution. ISPRS Int. J. Geo Inf. 6(4), 114 (2017)

    Google Scholar 

  61. Crowley, H., Despotaki, V., Rodrigues, D., Silva, V., Toma-Danila, D., Riga, E., Karatzetzou, A., Fotopoulou, S., Zugic, Z., Sousa, L., Ozcebe, S., Gamba, P.: Exposure model for European seismic risk assessment. Earthq. Spectra 36(1_suppl), 252–273 (2020)

    Google Scholar 

  62. Amadio, M., Mysiak, J., Marzi, S.: Mapping socioeconomic exposure for flood risk assessment in Italy. Risk Anal. 39(4), 829–845 (2019)

    Google Scholar 

  63. Alfieri, L., Salamon, P., Bianchi, A., Neal, J., Bates, P., Feyen, L.: Advances in pan-European flood hazard mapping. Hydrol. Process. 28(13), 4067–4077 (2014)

    Google Scholar 

  64. Pagani, M., Garcia-Pelaez, J., Gee, R., Johnson, K., Poggi, V., Silva, V., Simionato, M., Styron, R., Viganò, D., Danciu, L., Monelli, D., Weatherill, G.: The 2018 version of the global earthquake model: hazard component. Earthq. Spectra 8755293020931866 (2020)

    Google Scholar 

  65. Li, S., Dragicevic, S., Castro, F., Sester, M., Winter, S., Coltekin, A., Pettit, C., Jiang, B., Haworth, J., Stein, A., Cheng, T.: Geospatial big data handling theory and methods: A review and research challenges. ISPRS J. Photogramm. Remote. Sens. 115, 119–133 (2016)

    Google Scholar 

  66. Lwin, K., Sekimoto, Y., Takeuchi, W., Zettsu, K.: City geospatial dashboard: IoT and big data analytics for geospatial solutions provider in disaster management. In: 2019 International Conference on Information and Communication Technologies for Disaster Management (ICT-DM) (2019)

    Google Scholar 

  67. Albrecht, C., Elmegreen, B., Gunawan, O., Hamann, H., Klein, L., Lu, S., Mariano, F., Siebenschuh, C., Schmude, J.: Next-generation geospatial-temporal information technologies for disaster management. IBM J. Res. Dev. 64(1/2), 5:1 (2020)

    Google Scholar 

  68. Sastry N.: Crowdsourcing and social networks. In: Alhajj R., Rokne J. (Eds.) Encyclopedia of Social Network Analysis and Mining. Springer, New York (2018)

    Google Scholar 

  69. Goodchild, M.: Citizens as sensors: the world of volunteered geography. Geo J. 69(4), 211–221 (2007)

    Google Scholar 

  70. Arsanjani, J., Zipf, A., Mooney, P., Helbich, M. (eds.): OpenStreetMap in GIScience—Experiences, Research and Applications. Springer, Berlin (2015)

    Google Scholar 

  71. Iwao, K., Nishida, K., Kinoshita, T., Yamagata, Y.: Validating land cover maps with Degree Confluence Project information. Geophys. Res. Lett. 33(23), L23404 (2006)

    Google Scholar 

  72. Fritz, S., McCallum, I., Schill, C., Perger, C., Grillmayer, R., Achard, F., Kraxner, F, Obersteiner, M.: Geo-Wiki. Org: The use of crowdsourcing to improve global land cover. Remote Sens. 1(3), 345–354 (2009)

    Google Scholar 

  73. Bubalo, M., van Zanten, B., Verburg, P.: Crowdsourcing geo-information on landscape perceptions and preferences: a review. Landsc. Urban Plan. 184, 101–111 (2019)

    Google Scholar 

  74. Ma, D., Fan, H., Li, W., Ding, X.: The state of mapillary: an exploratory analysis. ISPRS Int. J. Geo Inf. 9(1), 10 (2020)

    Google Scholar 

  75. Hirata, E., Giannotti, M., Larocca, A., Quintanilha, J.: Flooding and inundation collaborative mapping–use of the Crowdmap/Ushahidi platform in the city of Sao Paulo, Brazil. J. Flood Risk Manag. 11, S98–S109 (2018)

    Google Scholar 

  76. Meier, P.: Crisis mapping in action: how open source software and global volunteer networks are changing the world, one map at a time. J. Map Geogr. Libr. 8(2), 89–100 (2012)

    Google Scholar 

  77. Ziemke, J.: Crisis mapping: the construction of a new interdisciplinary field? J. Map Geogr. Libr. 8(2), 101–117 (2012)

    Google Scholar 

  78. Büscher, M., Liegl, M., Thomas, V.: Collective intelligence in crises. In: Social Collective Intelligence, pp. 243–265. Springer, Cham (2014)

    Google Scholar 

  79. Heipke, C.: Crowdsourcing geospatial data. ISPRS J. Photogramm. Remote. Sens. 65(6), 550–557 (2010)

    Google Scholar 

  80. Dos Santos Rocha, R., Widera, A., van den Berg, R., de Albuquerque, J., Helingrath, B.: Improving the involvement of digital volunteers in disaster management. In: Murayama, Y., Velev, D., Zlateva, P., Gonzalez, J. (eds.), Proceedings of the International Conference on Information Technology in Disaster Risk Reduction, 214–224. Springer, Cham (2016)

    Google Scholar 

  81. Sievers, J.: Embracing crowdsourcing: a strategy for state and local governments approaching “Whole Community” emergency planning. State and Local Government Review 47(1), 57–67 (2015)

    Google Scholar 

  82. Nonnecke, B., Mohanty, S., Lee, A., Lee, J., Beckman, S., Mi, J., Krishnan, S., Roxas, R., Oco, N., Crittenden, C., Goldberg, K.: Malasakit 1.0: A participatory online platform for crowdsourcing disaster risk reduction strategies in the Philippines. In: 2017 IEEE Global Humanitarian Technology Conference (GHTC). IEEE (2017)

    Google Scholar 

  83. Acar, A., Muraki, Y.: Twitter for crisis communication: lessons learned from Japan’s tsunami disaster. Int. J. Web Based Commun. 7(3), 392–402 (2011)

    Google Scholar 

  84. Sarma, D., Das, A., Bera, U.: Uncertain demand estimation with optimization of time and cost using Facebook disaster map in emergency relief operation. Appl. Soft Comput. 87, 105992 (2020)

    Google Scholar 

  85. Bhuvana, N., Aram, I.: Facebook and WhatsApp as disaster management tools during the Chennai (India) floods of 2015. Int. J. Disaster Risk Reduct. 39, 101135 (2019)

    Google Scholar 

  86. Granell, C., Ostermann, F.: Beyond data collection: objectives and methods of research using VGI and geo-social media for disaster management. Comput. Environ. Urban Syst. 59, 231–243 (2016)

    Google Scholar 

  87. Yan, Y., Schultz, M., Zipf, A.: An exploratory analysis of usability of Flickr tags for land use/land cover attribution. Geospat. Inform. Sci. 22(1), 12–22 (2019)

    Google Scholar 

  88. Wang, Z., Ye, X., Tsou, M.: Spatial, temporal, and content analysis of Twitter for wildfire hazards. Nat. Hazards 83(1), 523–540 (2016)

    Google Scholar 

  89. Yue, Y., Dong, K., Zhao, X., Ye, X.: Assessing wild fire risk in the United States using social media data. J. Risk Res. (2019). https://doi.org/10.1080/13669877.2019.1569098

    Article  Google Scholar 

  90. Panagiotopoulos, P., Barnett, J., Bigdeli, A., Sams, S.: Social media in emergency management: twitter as a tool for communicating risks to the public. Technol. Forecast. Soc. Chang. 111, 86–96 (2016)

    Google Scholar 

  91. Jamali, M., Nejat, A., Moradi, S., Ghosh, S., Cao, G., Jin, F.: Social media data and housing recovery following extreme natural hazards. Int. J. Disaster Risk Reduct. 51, 101788 (2020)

    Google Scholar 

  92. Patel, N., Stevens, F., Huang, Z., Gaughan, A., Elyazar, I., Tatem, A.: Improving large area population mapping using geotweet densities. Trans. GIS 21(2), 317–331 (2017)

    Google Scholar 

  93. Yao, W., Zhang, C., Saravanan, S., Huang, R., Mostafavi, A.: Weakly-Supervised Fine-Grained Event Recognition on Social Media Texts for Disaster Management. Proc. AAAI Conf. Artif. Intell. 34(01), 532–539 (2020)

    Google Scholar 

  94. Chen, Y., Wang, Q., Ji, W.: Rapid assessment of disaster impacts on highways using social media. J. Manag. Eng. 36(5), 04020068 (2020)

    Google Scholar 

  95. Harrison, S., Johnson, P.: Challenges in the adoption of crisis crowdsourcing and social media in Canadian emergency management. Gov. Inf. Q. 36(3), 501–509 (2019)

    Google Scholar 

  96. Lv, X., Liao, Y., Deng, L.: Natural disaster emergency rescue system based on the mobile phone’s high-precision positioning. In: 3rd International Conference on Image, Vision and Computing, Chongqing, China. IEEE (2018)

    Google Scholar 

  97. Song, X., Zhang, Q., Sekimoto, Y., Shibasaki, R.: Prediction of human emergency behavior and their mobility following large-scale disaster. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, New York (2014)

    Google Scholar 

  98. Romano, M., Onorati, T., Aedo, I., Diaz, P.: Designing mobile applications for emergency response: citizens acting as human sensors. Sensors 16(3), 406 (2016)

    Google Scholar 

  99. Tan, M., Prasanna, R., Stock, K., Hudson-Doyle, E., Leonard, G., Johnston, D.: Mobile applications in crisis informatics literature: A systematic review. Int. J. Disaster Risk Reduct. 24, 297–311 (2017)

    Google Scholar 

  100. Cimellaro, G., Scura, G., Renschler, C., Reinhorn, A., Kim, H.: Rapid building damage assessment system using mobile phone technology. Earthq. Eng. Eng. Vib. 13(3), 519–533 (2014)

    Google Scholar 

  101. Salat, H., Smoreda, Z., Schläpfer, M.: A method to estimate population densities and electricity consumption from mobile phone data in developing countries. PloS one 15(6), e0235224 (2020)

    Google Scholar 

  102. Deville, P., Linard, C., Martin, S., Gilbert, M., Stevens, F., Gaughan, A., Blondela, V., Tatem, A.: Dynamic population mapping using mobile phone data. Proc. Natl. Acad. Sci. 111(45), 15888–15893 (2014)

    Google Scholar 

  103. Bachir, D., Khodabandelou, G., Gauthier, V., El Yacoubi, M., Puchinger, J.: Inferring dynamic origin-destination flows by transport mode using mobile phone data. Transp. Res. Part C Emerg. Technol. 101, 254–275 (2019)

    Google Scholar 

  104. Huang, H., Cheng, Y., Weibel, R.: Transport mode detection based on mobile phone network data: a systematic review. Transp. Res. Part C Emerg. Technol. 101, 297–312 (2019)

    Google Scholar 

  105. Wilson, R., zu Erbach-Schoenberg, E., Albert, M., Power, D., Tudge, S., Gonzalez, M., Guthrie, S., Chamberlain, H., Brooks, C., Hughes, C., Pitonakova, L., Buckee, C., Lu, X., Wetter, E., Tatem, A., Bengtsson, L.: Rapid and near real-time assessments of population displacement using mobile phone data following disasters: the 2015 Nepal Earthquake. PLoS Curr. 8 (2016)

    Google Scholar 

  106. Bharti, N., Lu, X., Bengtsson, L., Wetter, E., Tatem, A.: Remotely measuring populations during a crisis by overlaying two data sources. Int. Health 7(2), 90–98 (2015)

    Google Scholar 

  107. Pastor-Escuredo, D., Morales-Guzmán, A., Torres-Fernández, Y., Bauer, J., Wadhwa, A., Castro-Correa, C., Romanoff, L., Lee, J., Rutherford, A., Frias-Martinez, V., Oliver, N.: Flooding through the lens of mobile phone activity. In: IEEE Global Humanitarian Technology Conference (GHTC 2014), pp. 279–286. IEEE (2014)

    Google Scholar 

  108. Ricciato, F., Lanzieri, G., Wirthmann, A., Seynaeve, G.: Towards a methodological framework for estimating present population density from mobile network operator data. Pervasive Mobile Comput. 68, 101263 (2020)

    Google Scholar 

  109. Pestre, G., Letouzé, E., Zagheni, E.: The ABCDE of big data: assessing biases in call-detail records for development estimates. World Bank Econ. Rev. 34(Supplement_1), S89-S97 (2020).

    Google Scholar 

  110. Zhao, Z., Shaw, S., Xu, Y., Lu, F., Chen, J., Yin, L.: Understanding the bias of call detail records in human mobility research. Int. J. Geogr. Inf. Sci. 30(9), 1738–1762 (2016)

    Google Scholar 

  111. Mosavi, A., Ozturk, P., Chau, K.: Flood prediction using machine learning models: literature review. Water 10(11), 1536 (2018)

    Google Scholar 

  112. Wagenaar, D., Curran, A., Balbi, M., Bhardwaj, A., Soden, R., Hartato, E., Sarica, G., Ruangpan, L., Molinario, G., Lallemant, D.: Invited perspectives: How machine learning will change flood risk and impact assessment. Nat. Hazards Earth Syst. Sci. 20(4) (2020)

    Google Scholar 

  113. Yaseen, Z., Sulaiman, S., Deo, R., Chau, K.: An enhanced extreme learning machine model for river flow forecasting: state-of-the-art, practical applications in water resource engineering area and future research direction. J. Hydrol. 569, 387–408 (2019)

    Google Scholar 

  114. Xie, S., Wu, W., Mooser, S., Wang, Q., Nathan, R., Huang, Y.: Artificial neural network based hybrid modeling approach for flood inundation modeling. J. Hydrol. 125605 (2020)

    Google Scholar 

  115. Wu, W., Emerton, R., Duan, Q., Wood, A., Wetterhall, F., Robertson, D.: Ensemble flood forecasting: current status and future opportunities. Wiley Interdiscip. Rev. Water 7(3), e1432 (2020)

    Google Scholar 

  116. Yariyan, P., Janizadeh, S., Van Phong, T., Nguyen, H., Costache, R., Van Le, H., Pham, B., Pradhan, B., Tiefenbacher, J.: Improvement of best first decision trees using bagging and dagging ensembles for flood probability mapping. Water Resour. Manage 34(9), 3037–3053 (2020)

    Google Scholar 

  117. Chang, L., Amin, M., Yang, S., Chang, F.: Building ANN-based regional multi-step-ahead flood inundation forecast models. Water 10(9), 1283 (2018)

    Google Scholar 

  118. Zanchetta, A., Coulibaly, P.: Recent advances in real-time pluvial flash flood forecasting. Water 12(2), 570 (2020)

    Google Scholar 

  119. Wagenaar, D., Jong, J., Bouwer, L.: Multi-variable flood damage modelling with limited data using supervised learning approaches. Nat. Hazard. 17(9), 1683–1696 (2017)

    Google Scholar 

  120. Amadio, M., Scorzini, A., Carisi, F., Essenfelder, A., Domeneghetti, A., Mysiak, J., Castellarin, A.: Testing empirical and synthetic flood damage models: the case of Italy. Nat. Hazards Earth Syst. Sci. 19(3) (2019)

    Google Scholar 

  121. Cesarini, L., Figueiredo, R., Monteleone, B., Martina, M.: The potential of machine learning for weather index insurance. Nat. Hazards Earth Syst. Sci. (2021). https://doi.org/10.5194/nhess-2020-220

    Article  Google Scholar 

  122. Xie, Y., Ebad Sichani, M., Padgett, J., DesRoches, R.: The promise of implementing machine learning in earthquake engineering: A state-of-the-art review. Earthq. Spectra 8755293020919419 (2020)

    Google Scholar 

  123. Khosravikia, F., Clayton, P., Nagy, Z.: Artificial neural network-based framework for developing ground-motion models for natural and induced earthquakes in Oklahoma, Kansas, and Texas. Seismol. Res. Lett. 90(2A), 604–613 (2019)

    Google Scholar 

  124. Derakhshani, A., Foruzan, A.: Predicting the principal strong ground motion parameters: a deep learning approach. Appl. Soft Comput. 80, 192–201 (2019)

    Google Scholar 

  125. Mangalathu, S., Jeon, J.-S.: Classification of failure mode and prediction of shear strength for reinforced concrete beam-column joints using machine learning techniques. Eng. Struct. 160, 85–94 (2018)

    Google Scholar 

  126. Huang, H., Burton, H.: Classification of in-plane failure modes for reinforced concrete frames with infills using machine learning. J. Build. Eng. 25, 100767 (2019)

    Google Scholar 

  127. Gao, Y., Mosalam, K.: Deep transfer learning for image-based structural damage recognition. Comput. Aided Civil Infrastruct. Eng. 33(9), 748–768 (2018)

    Google Scholar 

  128. Seydi, S., Rastiveis, H.: A deep learning framework for roads network damage assessment using post-earthquake LiDAR data. Int. Archives Photogram. Remote Sens. Spat. Inf. Sci. 42, 955–961 (2019)

    Google Scholar 

  129. Mangalathu, S., Hwang, S., Choi, E., Jeon, J-S.: Rapid seismic damage evaluation of bridge portfolios using machine learning techniques. Eng. Struct. 201, 109785 (2019)

    Google Scholar 

  130. Liu, Z., Zhang, Z.: Artificial neural network based method for seismic fragility analysis of steel frames. KSCE J. Civ. Eng. 22(2), 708–717 (2018)

    Google Scholar 

  131. Mangalathu, S., Jeon, J.-S.: Stripe-based fragility analysis of concrete bridge classes using machine learning techniques. Earthq. Eng. Struct. Dynam. 48, 1238–2125 (2019)

    Google Scholar 

  132. Mangalathu, S., Hwang, S-H., Jeon, J-S.: Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach. Eng. Struct. 219, 110927 (2020)

    Google Scholar 

  133. Pereira, N., Romão, X.: Damage localization length in RC frame components: mechanical analysis and experimental observations. Eng. Struct. 221, 111026 (2020)

    Google Scholar 

  134. Gatti, F., Clouteau, D.: Towards blending Physics-Based numerical simulations and seismic databases using Generative Adversarial Network. Comput. Methods Appl. Mech. Eng. 372, 113421 (2020)

    Google Scholar 

  135. Jain, P., Coogan, S., Subramanian, S., Crowley, M., Taylor, S., Flannigan, M.: A review of machine learning applications in wildfire science and management. Environ. Rev. 28(4), 478–505 (2020)

    Google Scholar 

  136. Liu, Z., Yang, J., He, H.: Identifying the threshold of dominant controls on fire spread in a boreal forest landscape of northeast China. PLoS One 8(1), e55618 (2013)

    Google Scholar 

  137. Lydersen, J., Collins, B., Brooks, M., Matchett, J., Shive, K., Povak, N., Kane, V., Smith, D.: Evidence of fuels management and fire weather influencing fire severity in an extreme fire event. Ecol. Appl. 27(7), 2013–2030 (2017)

    Google Scholar 

  138. McGovern, A., Lagerquist, R., John Gagne, D., Jergensen, G., Elmore, K., Homeyer, C., Smith, T.: Making the black box more transparent: understanding the physical implications of machine learning. Bull. Am. Meteor. Soc. 100(11), 2175–2199 (2019)

    Google Scholar 

  139. Cortez, P., Morais, A: A data mining approach to predict forest fires using meteorological data (2007). Available from https://repositorium.sdum.uminho.pt/handle/1822/8039

  140. Sayad, Y., Mousannif, H., Al Moatassime, H.: Predictive modeling of wildfires: a new dataset and machine learning approach. Fire Saf. J. 104, 130–146 (2019)

    Google Scholar 

  141. Liang, H., Zhang, M., Wang, H.: A neural network model for wildfire scale prediction using meteorological factors. IEEE Access 7, 176746–176755 (2019)

    Google Scholar 

  142. Michael, Y., Helman, D., Glickman, O., Gabay, D., Brenner, S., Lensky, I.: Forecasting fire risk with machine learning and dynamic information derived from satellite vegetation index time-series. Sci. Total Environ. 142844 (2020)

    Google Scholar 

  143. Mohan, A., Singh, A., Kumar, B., Dwivedi, R.: Review on remote sensing methods for landslide detection using machine and deep learning. Trans. Emerg. Telecommun. Technol. e3998 (2020)

    Google Scholar 

  144. Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., Meena, S., Tiede, D., Aryal, J.: Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection. Remote Sens. 11(2), 196 (2019)

    Google Scholar 

  145. Merghadi, A., Yunus, A., Dou, J., Whiteley, J., ThaiPham, B., Bui, D., Avtar, R., Abderrahmane, B.: Machine learning methods for landslide susceptibility studies: a comparative overview of algorithm performance. Earth-Sci. Rev. 103225 (2020)

    Google Scholar 

  146. Prakash, N., Manconi, A., Loew, S.: Mapping landslides on EO data: performance of deep learning models vs. traditional machine learning models. Remote Sens. 12(3), 346 (2020)

    Google Scholar 

  147. Lee, S., Baek, W., Jung, H., Lee, S.: Susceptibility Mapping on Urban Landslides Using Deep Learning Approaches in Mt. Umyeon. Appl. Sci. 10(22), 8189 (2020)

    Google Scholar 

  148. Kadavi, P., Lee, C., Lee, S.: Application of ensemble-based machine learning models to landslide susceptibility mapping. Remote Sens. 10(8), 1252 (2018)

    Google Scholar 

  149. Di Napoli, M., Carotenuto, F., Cevasco, A., Confuorto, P., Di Martire, D., Firpo, M., Pepe, G., Raso, E., Calcaterra, D.: Machine learning ensemble modelling as a tool to improve landslide susceptibility mapping reliability. Landslides 17(8), 1897–1914 (2020)

    Google Scholar 

  150. Thai Pham, B., Shirzadi, A., Shahabi, H., Omidvar, E., Singh, S., Sahana, M., Asl, D., Ahmad, B., Quoc, N., Lee, S.: Landslide susceptibility assessment by novel hybrid machine learning algorithms. Sustainability 11(16), 4386 (2019)

    Google Scholar 

  151. Pham, B., Prakash, I., Singh, S., Shirzadi, A., Shahabi, H., Bui, D.: Landslide susceptibility modeling using Reduced Error Pruning Trees and different ensemble techniques: hybrid machine learning approaches. CATENA 175, 203–218 (2019)

    Google Scholar 

  152. Catani, F.: Landslide detection by deep learning of non-nadiral and crowdsourced optical images. Landslides (2020). https://doi.org/10.1007/s10346-020-01513-4

    Article  Google Scholar 

  153. Zhong, C., Liu, Y., Gao, P., Chen, W., Li, H., Hou, Y., Nuremanguli, T., Ma, H.: Landslide mapping with remote sensing: challenges and opportunities. Int. J. Remote Sens. 41(4), 1555–1581 (2020)

    Google Scholar 

  154. Kalantar, B., Ueda, N., Saeidi, V., Ahmadi, K., Halin, A., Shabani, F.: Landslide susceptibility mapping: machine and ensemble learning based on remote sensing big data. Remote Sens. 12(11), 1737 (2020)

    Google Scholar 

  155. van Natijne, A., Lindenbergh, R., Bogaard, T.: Machine learning: new potential for local and regional deep-seated landslide nowcasting. Sensors 20(5), 1425 (2020)

    Google Scholar 

  156. Thirugnanam, H., Ramesh, M., Rangan, V.: Enhancing the reliability of landslide early warning systems by machine learning. Landslides 17(9), 2231–2246 (2020)

    Google Scholar 

  157. Zhang, W.: Geological disaster monitoring and early warning system based on big data analysis. Arab. J. Geosci. 13(18), 1–9 (2020)

    Google Scholar 

  158. Karunarathne, S., Dray, M., Popov, L., Butler, M., Pennington, C., Angelopoulos, C.: A technological framework for data-driven IoT systems: Application on landslide monitoring. Comput. Commun. 154, 298–312 (2020)

    Google Scholar 

  159. Hong, M., Akerkar, R.: Analytics and evolving landscape of machine learning for emergency response. In: Machine Learning Paradigms, 351–397. Springer, Cham (2019)

    Google Scholar 

  160. Shah, S., Seker, D., Hameed, S., Draheim, D.: The rising role of big data analytics and IoT in disaster management: recent advances, taxonomy and prospects. IEEE Access 7, 54595–54614 (2019)

    Google Scholar 

  161. Alam, F., Ofli, F., Imran, M.: Descriptive and visual summaries of disaster events using artificial intelligence techniques: case studies of Hurricanes Harvey, Irma, and Maria. Behav. Inf. Technol. 39(3), 288–318 (2020)

    Google Scholar 

  162. Kruspe, A., Kersten, J., Klan, F.: Detection of informative tweets in crisis events. Nat. Hazards Earth Syst. Sci. Discuss. (2020). https://doi.org/10.5194/nhess-2020-214

    Article  Google Scholar 

  163. Schulz, A., Mencía, E., Schmidt, B.: A rapid-prototyping framework for extracting small-scale incident-related information in microblogs: application of multi-label classification on tweets. Inf. Syst. 57, 88–110 (2016)

    Google Scholar 

  164. Liu, W., Shen, X., Wang, H., Tsang, I.: The Emerging Trends of Multi-Label Learning (2020). arXiv preprint arXiv:2011.11197

  165. Nizzoli, L., Avvenuti, M., Tesconi, M., Cresci, S.: Geo-semantic-parsing: AI-powered geoparsing by traversing semantic knowledge graphs. Decis. Supp. Syst. 136, 113346 (2020)

    Google Scholar 

  166. Avvenuti, M., Cresci, S., Nizzoli, L., Tesconi, M.: GSP (Geo-Semantic-Parsing): geoparsing and geotagging with machine learning on top of linked data. In: European Semantic Web Conference, pp. 17–32. Springer, Cham (2018)

    Google Scholar 

  167. Hunt, K., Agarwal, P., Zhuang, J.: Monitoring misinformation on Twitter during crisis events: a machine learning approach. Risk Anal. (2020). https://doi.org/10.1111/risa.13634

    Article  Google Scholar 

  168. Faustini, P., Covões, T.: Fake news detection in multiple platforms and languages. Expert Syst. Appl. 113503 (2020)

    Google Scholar 

  169. Kaufhold, M., Bayer, M., Reuter, C.: Rapid relevance classification of social media posts in disasters and emergencies: A system and evaluation featuring active, incremental and online learning. Inform. Process. Manag. 57(1), 102132 (2020)

    Google Scholar 

  170. Ofli, F., Imran, M., Alam, F.: Using artificial intelligence and social media for disaster response and management: an overview. AI Rob. Disaster Stud. 63–81 (2020)

    Google Scholar 

  171. Alam, F., Ofli, F., Imran, M., Alam, T., Qazi, U.: Deep Learning Benchmarks and Datasets for Social Media Image Classification for Disaster Response (2020). arXiv preprint arXiv:2011.08916

  172. Zhu, X., Zhang, G., Sun, B.: A comprehensive literature review of the demand forecasting methods of emergency resources from the perspective of artificial intelligence. Nat. Hazards 97(1), 65–82 (2019)

    Google Scholar 

  173. Gul, M., Guneri, A.: An artificial neural network-based earthquake casualty estimation model for Istanbul city. Nat. Hazards 84(3), 2163–2178 (2016)

    Google Scholar 

  174. Huang, X., Song, J., Jin, H.: The casualty prediction of earthquake disaster based on Extreme Learning Machine method. Nat. Hazards 102, 873–886 (2020)

    Google Scholar 

  175. Feng, Y., Wang, D., Yin, Y., Li, Z., Hu, Z.: An XGBoost-based casualty prediction method for terrorist attacks. Complex Intell Syst 6(3), 721–740 (2020)

    Google Scholar 

  176. Almalki, F.A., Angelides, M.: Deployment of an aerial platform system for rapid restoration of communications links after a disaster: a machine learning approach. Computing 102, 829–864 (2020)

    Google Scholar 

  177. Papadopoulos, H., Korakis, A.: Predicting medical resources required to be dispatched after earthquake and flood, using historical data and machine learning techniques: the COncORDE emergency medical service use case. Int. J. Interact. Commun. Syst. Technol. (IJICST) 8(2), 13–35 (2018)

    Google Scholar 

  178. Lin, A., Wu, H., Liang, G., Cardenas-Tristan, A., Wu, X., Zhao, C., Li, D.: A big data-driven dynamic estimation model of relief supplies demand in urban flood disaster. Int. J. Disaster Risk Reduct. 101682 (2020)

    Google Scholar 

  179. Nadi, A., Edrissi, A.: A reinforcement learning approach for evaluation of real-time disaster relief demand and network condition. Int. J. Econ. Manag. Eng. 11(1), 5–10 (2016)

    Google Scholar 

  180. Goldblatt, R., Stuhlmacher, M., Tellman, B., Clinton, N., Hanson, G., Georgescu, M., Wang, C., Serrano-Candela, F., Khandelwal, A., Cheng, W., Balling, R., Jr.: Using Landsat and night time lights for supervised pixel-based image classification of urban land cover. Remote Sens. Environ. 205, 253–275 (2018)

    Google Scholar 

  181. Levin, N., Kyba, C., Zhang, Q., de Miguel, A., Román, M., Li, X., Portnov, B., Molthan, A., Jechow, A., Miller, S., Wang, Z., Shrestha, R., Elvidge, C.: Remote sensing of night lights: a review and an outlook for the future. Remote Sens. Environ. 237, 111443 (2020)

    Google Scholar 

  182. Tan, Y., Xiong, S., Li, Z., Tian, J., Li, Y.: Accurate detection of built-up areas from high-resolution remote sensing imagery using a fully convolutional network. Photogramm. Eng. Remote. Sens. 85(10), 737–752 (2019)

    Google Scholar 

  183. Tan, Y., Xiong, S., Yan, P.: Multi-branch convolutional neural network for built-up area extraction from remote sensing image. Neurocomputing 396, 358–374 (2020)

    Google Scholar 

  184. Alshehhi, R., Marpu, P., Woon, W., Dalla Mura, M.: Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks. ISPRS J. Photogramm. Remote. Sens. 130, 139–149 (2017)

    Google Scholar 

  185. Chen, Q., Wang, L., Waslander, S., Liu, X.: An end-to-end shape modeling framework for vectorized building outline generation from aerial images. ISPRS J. Photogramm. Remote. Sens. 170, 114–126 (2020)

    Google Scholar 

  186. Saadi, S., Bensaibi, M.: Detection of buildings height using satellite monoscopic image. In: Second European Conference on Earthquake Engineering and Seismology, Istanbul (2014)

    Google Scholar 

  187. Biljecki, F., Ledoux, H., Stoter, J.: Generating 3D city models without elevation data. Comput. Environ. Urban Syst. 64, 1–18 (2017)

    Google Scholar 

  188. Gao, X., Sun, X., Zhang, Y., Yan, M., Xu, G., Sun, H., Jiao, J., Fu, K.: An end-to-end neural network for road extraction from remote sensing imagery by multiple feature pyramid network. IEEE Access 6, 39401–39414 (2018)

    Google Scholar 

  189. Gao, L., Song, W., Dai, J., Chen, Y.: Road extraction from high-resolution remote sensing imagery using refined deep residual convolutional neural network. Remote Sens. 11(5), 552 (2019)

    Google Scholar 

  190. Hoffmann, E., Wang, Y., Werner, M., Kang, J., Zhu, X.: Model fusion for building type classification from aerial and street view images. Remote Sens. 11(11), 1259 (2019)

    Google Scholar 

  191. Lenjani, A., Yeum, C., Dyke, S., Bilionis, I.: Automated building image extraction from 360° panoramas for postdisaster evaluation. Comput. Aided Civil Infrastruct. Eng. 35(3), 241–257 (2020)

    Google Scholar 

  192. Srivastava, S., Vargas Munoz, J., Lobry, S., Tuia, D.: Fine-grained landuse characterization using ground-based pictures: a deep learning solution based on globally available data. Int. J. Geogr. Inf. Sci. 34(6), 1117–1136 (2020)

    Google Scholar 

  193. Gómez, J., Patiño, J., Duque, J., Passos, S.: Spatiotemporal modeling of urban growth using machine learning. Remote Sens. 12(1), 109 (2020)

    Google Scholar 

  194. Aburas, M., Ahamad, M., Omar, N.: Spatio-temporal simulation and prediction of land-use change using conventional and machine learning models: a review. Environ. Monit. Assess. 191(4), 205 (2019)

    Google Scholar 

  195. Aarthi, A., Gnanappazham, L.: Comparison of urban growth modeling using deep belief and neural network based cellular automata model—a case study of Chennai metropolitan area, Tamil Nadu, India. J. Geogr. Inf. Syst. 11(01), 1 (2019)

    Google Scholar 

  196. Xu, T., Gao, J., Coco, G.: Simulation of urban expansion via integrating artificial neural network with Markov chain–cellular automata. Int. J. Geogr. Inf. Sci. 33(10), 1960–1983 (2019)

    Google Scholar 

  197. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., Galstyan, A.: A survey on bias and fairness in machine learning (2019). arXiv preprint arXiv:1908.09635

  198. Makhlouf, K., Zhioua, S., Palamidessi, C.: On the Applicability of ML Fairness Notions (2020). arXiv preprint arXiv:2006.16745

  199. Dabbeek, J., Silva, V.: Modeling the residential building stock in the Middle East for multi-hazard risk assessment. Nat. Hazards 100(2), 781–810 (2020)

    Google Scholar 

  200. Soden, R., Kauffman, N.: Infrastructuring the imaginary: how sea-level rise comes to matter in the San Francisco Bay area. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (2019)

    Google Scholar 

  201. Mellon, J., Prosser, C.: Twitter and Facebook are not representative of the general population: political attitudes and demographics of British social media users. Res. Politics 4(3), 2053168017720008 (2017)

    Google Scholar 

  202. Gambo, S., Özad, B.: The demographics of computer-mediated communication: a review of social media demographic trends among social networking site giants. Comput. Hum. Behav. Rep. 2, 100016 (2020)

    Google Scholar 

  203. Fan, C., Esparza, M., Dargin, J., Wu, F., Oztekin, B., Mostafavi, A.: Spatial biases in crowdsourced data: Social media content attention concentrates on populous areas in disasters. Comput. Environ. Urban Syst. 83, 101514 (2020)

    Google Scholar 

  204. Taddeo, M., Floridi, L.: How AI can be a force for good. Science 361(6404), 751–752 (2018)

    MathSciNet  MATH  Google Scholar 

  205. Tomašev, N., Cornebise, J., Hutter, F., Mohamed, S., Picciariello, A., Connelly, B., Bel-grave, D., Ezer, D., van der Haert, F., Mugisha, F., Abila, G., Arai, H., Almiraat, H., Proskurnia, J., Snyder, K., Otake-Matsuura, M., Othman, M., Glasmachers, T., de Wever, W., Teh, Y., Khan, M., De Winne, R., Tom Schaul, T., Clopath, C.: AI for social good: unlocking the opportunity for positive impact. Nat. Commun. 11(1), 1–6.e (2020)

    Google Scholar 

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Acknowledgements

The first author would like to acknowledge the financial support by Base Funding—UIDB/04708/2020 of CONSTRUCT—Instituto de I&D em Estruturas e Construções, funded by national funds through FCT/MCTES (PIDDAC), that covered part of the research results presented in this Chapter.

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Romão, X., Pereira, F.L. (2022). Smart Disaster Risk Reduction and Emergency Management in the Built Environment. In: Bolpagni, M., Gavina, R., Ribeiro, D. (eds) Industry 4.0 for the Built Environment. Structural Integrity, vol 20. Springer, Cham. https://doi.org/10.1007/978-3-030-82430-3_14

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