EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 604 (2025) E3S Web Conf., 604 (2025) 04006 References
Open Access
Issue
E3S Web Conf.
Volume 604, 2025
The 4th International Conference on Disaster Management (The 4th ICDM 2024)
Article Number 04006
Number of page(s) 8
Section Disaster Monitoring, Broadcasting, Early Warning and Information System
DOI https://doi.org/10.1051/e3sconf/202560404006
Published online 16 January 2025
  1. R. Mechler, Natural disaster risk management and financing disaster losses in developing countries. (Verlag Versicherungswirtsch, Karlsruhe 2004). ISBN13:978-3899521207 [Google Scholar]
  2. M. Dilley, Natural disaster hotspots: a global risk analysis. World Bank Disaster Management Series, 5 (2005). DOI:10.1007/978-3-322-82113-3_1 [Google Scholar]
  3. S. Cole, H. A, E. Werker. Do voters demand responsive governments? Evidence from Indian disaster relief. Journal of Development Economics, 97 (2012). DOI:10.1016/j.jdeveco.2011.05.005 [Google Scholar]
  4. M. Erdik, K. Şeşetyan, M. Demircioğlu, U. Hancılar, C. Zülfikar, Rapid earthquake loss assessment after damaging earthquakes. Soil Dynamics, and Earthquake Engineering, 31 (2011). DOI:10.1007/978-90-481-9544-2_21 [Google Scholar]
  5. L.N. Rickard, Z.J. Yang, J.P. Schuldt, G.M. Eosco, C.W. Scherer, R.A. Daziano, Sizing up a superstorm: exploring the role of recalled experience and attribution of responsibility in judgments of future hurricane risk. Risk Analysis, 37 (2017). DOI:10.1111/risa.12779 [Google Scholar]
  6. B. Nelson, Natural disasters: a calculated risk. Nature, 495 (2013). https://doi.org/10.1038/nj7440-271a [CrossRef] [PubMed] [Google Scholar]
  7. T. Yigitcanlar, J. Corchado, R. Mehmood, R. Li, K. Mossberger, K. Desouza, Responsible urban innovation with local government artificial intelligence (AI): A conceptual framework and research agenda. J. Open Innov. Technol. Mark. Complex., 7 (2021). DOI:10.3390/joitmc7010071 [Google Scholar]
  8. D. Lazer, A. Pentland, L. Adamic, S. Aral, A.L. Barabasi, D. Brewer, M. Gutmann, Life in the network: the coming age of computational social science. Science, 323 (2010). DOI:10.1126/science.1167742 [Google Scholar]
  9. J. Scott, F. N. Stokman, Social networks. International Encyclopedia of the Social & Behavioral Sciences, 22 (2015). DOI:10.1016/B978-0-08-097086-8.32101-8 [Google Scholar]
  10. Y. Sun, Y. Shao, E. Chan, Co-visitation network in tourism-driven peri-urban area based on social media analytics: A case study in Shenzhen, China. Landsc. Urban Plan, 204 103934 (2020). DOI:10.1016/j.landurbplan.2020.103934 [CrossRef] [Google Scholar]
  11. S. Garske, S. Elayan, M. Sykora, T. Edry, L. Grabenhenrich, S. Galea, O. Gruebner, Space-time dependence of emotions on Twitter after a natural disaster. Int. J. Environ. Res. Public Health, 18, 5292 (2021). DOI:10.3390/ijerph18105292 [CrossRef] [Google Scholar]
  12. S. Alotaibi, R. Mehmood, I. Katib, O. Rana, A. Albeshri, A big data analytics tool for healthcare symptoms and diseases detection using Twitter, Apache Spark, and Machine Learning. Appl. Sci., 10, 1398 (2020). DOI:10.3390/app10041398 [Google Scholar]
  13. E. Alomari, I. Katib, A. Albeshri, R. Mehmood, COVID-19: Detecting government pandemic measures and public concerns from Twitter data using distributed machine learning. Int. J. Environ. Res. Public Health, 18, 282 (2021). DOI:10.3390/ijerph18010282 [CrossRef] [Google Scholar]
  14. T. Yigitcanlar, N. Kankanamge, A. Preston, P. Gill, M. Rezayee, M. Ostadnia, G. Ioppolo, How can social media analytics assist authorities in pandemic-related policy decisions? Insights from Australian states and territories. Health Inf. Sci. Syst., 8, 37 (2020). DOI:10.1007/s13755-020-00121-9 [CrossRef] [Google Scholar]
  15. S. Suma, R. Mehmood, N. Albugami, I. Katib, A. Albeshri, Enabling next generation logistics and planning for smarter societies. Procedia Comput. Sci., 109, 1122–1127, (2017). DOI:10.1016/j.procs.2017.05.440 [CrossRef] [Google Scholar]
  16. N. Kankanamge, T. Yigitcanlar, A. Goonetilleke, How engaging are disaster management related social media channels? The case of Australian state emergency organisations. Int. J. Disaster Risk Reduct., 48, 101097 (2020). DOI:10.1016/j.ijdrr.2020.101571 [CrossRef] [Google Scholar]
  17. T. Yigitcanlar, L. Butler, E. Windle, K. Desouza, R. Mehmood, J. Corchado, Can building “artificially intelligent cities” safeguard humanity from natural disasters, pandemics, and other catastrophes? An urban scholar’s perspective. Sensors, 20, 2988 (2020). DOI:10.3390/s20102988 [CrossRef] [Google Scholar]
  18. N. Kankanamge, T. Yigitcanlar, A. Goonetilleke, Public perceptions on artificial intelligence driven disaster management: Evidence from Sydney, Melbourne and Brisbane. Telemat. Inform., 65, 101729 (2021). DOI:10.1016/j.tele.2021.101729 [CrossRef] [Google Scholar]
  19. R. Meyer, K. Broad, B. Orlove, N. Petrovic, Dynamic simulation as an approach to Decision Support Systems 111 (2018) 48–59 understanding hurricane risk response: insights from the Storm view lab, Risk Analysis, 33 (2013). DOI:10.1111/j.1539-6924.2012.01935.x [Google Scholar]
  20. N. Martin, J. Rice, Emergency communications and warning systems: Determining critical capacities in the Australian context. Disaster Prev. Manag., 21 (2012). DOI:10.3390/su14020810 [Google Scholar]
  21. N. Kankanamge, T. Yigitcanlar, A. Goonetilleke, Public perceptions on artificial intelligence driven disaster management: Evidence from Sydney, Melbourne and Brisbane. Telemat. Inform., 65, 101729 (2021). DOI:10.1016/j.tele.2021.101729 [CrossRef] [Google Scholar]
  22. A.T. Chatfield, C.G. Reddick, All hands-on deck to tweet# sandy: Networked governance of citizen coproduction in turbulent times. Gov. Inf. Q., 35, (2018). DOI:10.1016/j.giq.2017.09.004 [Google Scholar]
  23. Y. Akama, S. Chaplin, P. Fairbrother, Role of social networks in community preparedness for bushfire. Int. J. Disaster Resil. Built Environ., 5, 277–291 (2014). DOI:10.1108/IJDRBE-01-2014-0010 [CrossRef] [Google Scholar]
  24. M. Mirbabaire, C. Ehnis, S. Stieglitz, B. Bunker, T. Rose, Digital Nudging in Social Media Disaster Communication. Inf. Syst. Front., 23 (2021). DOI:10.1007/s10796-020-10062-z [Google Scholar]
  25. L. Susan Bilek, Thorne Lay, Subduction zone megathrust earthquakes. Geosphere. 14 (2018). DOI:10.1130/GES01608.1 [Google Scholar]
  26. X. Gao, and K. Wang, Strength of stick-slip and creeping subduction megathrusts from heat flow observations. Science. 345 (2014). DOI:10.1126/science.1255487 [Google Scholar]
  27. C. Damayanti, A. K. Yamko, C. J. Souisa, W. Barends, and I. L. P. T. Naroly, Pemodelan segmentasi Mentawai-Pagai: studi kasus gempa megathrust di Indonesia. J. Geosains & Remote Sensing, 1 (2020). DOI:10.23960/jgrs.2020.v1i2.56 [Google Scholar]
  28. F. Funiciello, F. Corbi, A. Heuret, C. Piromallo, and M. Rosenau, Empirical analysis of global-scale natural data and analogue seismotectonic modelling data to unravel the seismic behaviour of the subduction megathrust. Front. Earth Sci., (2020). DOI:10.3389/feart.2020.600152 [Google Scholar]
  29. C. Subarya et al., Plate-boundary deformation associated with the great Sumatra-Andaman earthquake, Nature 440, 46, (2006). DOI:10.1038/nature04522 [Google Scholar]
  30. R.W. Briggs, K. Sieh, A. J. Meltzner, D. Natawidjaja, J. Galetzka, B, Suwargadi, Deformation and slip along the Sunda Megathrust in the Great 2005 Nias-Simeulue earthquake. Science, 31 (2006). DOI:10.1126/science.1122602 [Google Scholar]
  31. Sumatran GPS Array Data, (2004) ectonics.caltech.edu/sumatra/data.html (accessed on September 9, 2024). [Google Scholar]
  32. H. Kanamori, The diversity of large earthquakes and its implications for hazard mitigation. Annual Review of Earth & Planetary Sciences, 42, 7–26 (2014). https://doi.org/10.1146/annurev-earth-060313-055034. [CrossRef] [Google Scholar]
  33. T. Egbelakin, S.S. Wilkinson, R. Potangaroa, and J. M. Ingham, Improving regulatory frameworks for earthquake risk mitigation. Building Research and Information, 41 (2013). https://doi.org/10.1080/09613218.2013.819546 [Google Scholar]
  34. S.L. Bilek, H.M.M. Rotman, and W.S. Phillips, Low stress drop earthquakes in the rupture zone of the 1992 Nicaragua tsunami earthquake: Geophysical Research Letters, 43, (2016). https://doi.org/10.1002/2016GL070409 [Google Scholar]
  35. T. Lay and S. L. Bilek Anomalous earthquake ruptures at shallow depths on subduction zone megathrusts, in T.H. Dixon and J.C. Moore, eds., The seismogenic zone of subduction thrust faults (Columbia University Press, New York, 2007). DOI:10.1029/2011JB009133 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.