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Artificial Intelligence Based Early Warning System for Coastal Disasters

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Part of the Disaster Research and Management Series on the Global South book series (DRMSGS)

Abstract

Disaster indicates a serious disruption or deviation from the norm that generally occurs for a short period of time and affects the community and society as a whole by way of widespread harm or damage to humans, wildlife, environment, infrastructure and economy. It requires a response that encompasses multidimensional processes in order to mitigate, respond to and recover from its consequences. Disaster that impact the coastal areas is the focus of this chapter. Coastal areas are one of the most sensitive and altered ecosystems world-wide, as they are subject to many disasters and risks including high winds resulting in cyclones and underwater earthquakes initiating strong tidal waves or tsunamis. These disasters are responsible for the loss of lives and/or infrastructural damages in coastal areas. To address this entails the designing of early warning systems that disseminate relevant information effectively and efficiently, as alarms or warnings, to communities at risk during or before such disasters so that timely and adequate steps can be taken to minimize the loss and damages associated with such disasters. In this chapter, an attempt has been made to design an early warning system that uses artificial intelligence to predict the horizontal in-city flooding caused by underwater earthquakes. For experimental purposes, the tsunami dataset would be considered by the proposed early warning system.

Keywords

  • Tsunami
  • Earthquakes
  • Machine learning
  • In-city flooding

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References

  • Adger, W. N., Hughes, T. P., Folke, C., Carpenter, S. R., & Rockström, J. (2005). Social-Ecological Resilience to Coastal Disasters. Science, 309(5737), 1036–1039.

    CrossRef  Google Scholar 

  • Blewitt, G., Kreemer, C., Hammond, W. C., Plag, H.-P., Stein, S., & Okal, E. (2006). Rapid Determination of Earthquake Magnitude Using GPS for Tsunami Warning Systems. Geophysical Research Letters, 33(11).

    Google Scholar 

  • Blewitt, G., Hammond, W. C., Kreemer, C., Plag, H.-P., Stein, S., & Okal, E. (2009). GPS for Real-Time Earthquake Source Determination and Tsunami. Journal of Geodesy, 83, 335–343.

    CrossRef  Google Scholar 

  • Chatfield, A. T., & Brajawidagda, U. (2012). Twitter Tsunami Early Warning Network: A Social Network Analysis of Twitter Information Flows. In 23rd Australasian Conference on Information Systems (pp. 1–10). Deakin: Deakin University.

    Google Scholar 

  • Cox, D. R. (1958). The Regression Analysis of Binary Sequences. Journal of the Royal Statistical Society: Series B: Methodological, 20(2), 215–242.

    Google Scholar 

  • Database on Coastal States of India. (2017). Retrieved from Centre for Coastal Zone Management and Coastal Shelter Belt. http://iomenvis.nic.in/index2.aspx?slid=758&sublinkid=119&langid=1&mid=1

  • Diamond, J. M., & Ordunio, D. (1999). Guns, Germs, and Steel. UK: Penguin.

    Google Scholar 

  • Duxbury, J., & Dickinson, S. (2007). Principles for Sustainable Governance of the Coastal Zone: In the Context of Coastal Disasters. Ecological Economics, 63(2–3), 319–330.

    CrossRef  Google Scholar 

  • Falck, C., Ramatschi, M., Subarya, C., Bartsch, M., Merx, A., Hoeberechts, J., & Schmidt, G. (2010). Near Real-Time GPS Applications for Tsunami Early Warning Systems. Natural Hazards and Earth System Sciences, 10, 181–189.

    CrossRef  Google Scholar 

  • Finkl, C. W., & Makowski, C. (2005). Encyclopedia of Coastal Science. Cham: Springer International Publishing.

    Google Scholar 

  • Godschalk, D. R., Norton, R., Richardson, C., & Salvesen, D. (2000). Avoiding Coastal Hazard Areas: Best State Mitigation Practices. Environmental Geosciences, 7(1), 13–22.

    CrossRef  Google Scholar 

  • GOV.UK. (2013). Emergency Response and Recovery. London: Cabinet Office, Civil Contingencies Secretariat. Retrieved from GOV.UK: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/253488/Emergency_Response_and_Recovery_5th_edition_October_2013.pdf

  • Grasso, V. F., & Singh, A. (2011). Early Warning Systems: State-of-Art Analysis and Future Directions. Draft report, United Nations Environment Programme, 1. Nairobi available at https://na.unep.net/siouxfalls/publications/Early_Warning.pdf

  • Haykin, S. (1994). Neural Networks (Vol. 2). New York: Prentice Hall.

    Google Scholar 

  • Ho, T. K. (1995). Random Decision Forests. In Proceedings of 3rd International Conference on Document Analysis and Recognition (Vol. 1, pp. 278–282).

    CrossRef  Google Scholar 

  • Kent, R. C. (1994). Disaster Preparedness. Geneva: UNDP/DHA Disaster Management Training Programme.

    Google Scholar 

  • Kotsiantis, S. B. (2007). Supervised Machine Learning: A Review of Classification Techniques. In Proceedings of the 2007 Conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies (pp. 3–24). Amsterdam: IOS Press.

    Google Scholar 

  • Kotsiantis, S. B., Kanellopoulos, D., & Pintelas, P. E. (2007). Data Preprocessing for Supervised Learning. International Journal of Computer, Electrical, Automation, Control and Information Engineering, 1(12), 4091–4096.

    Google Scholar 

  • Langley, P., Iba, W., & Thompson, K. (1992). An Analysis of Bayesian Classifiers. In Proceedings of the Tenth National Conference on Artificial Intelligence (AAAI'92) (pp. 223–228). San Jose: AAAI Press.

    Google Scholar 

  • Larose, D. T., & Larose, C. D. (2014). Discovering Knowledge in Data: An Introduction to Data Mining. Hoboken: John Wiley & Sons.

    Google Scholar 

  • Liu, P. L.-F., Wang, X., & Salisbury, A. J. (2009, September 4). Tsunami Hazard and Early Warning System in South China Sea. Journal of Asian Earth Sciences, 36(1), 2–12.

    CrossRef  Google Scholar 

  • NGDC. (2015). Global Historical Tsunami Database. https://doi.org/10.7289/V5PN93H7

  • NGDC. (2018). Natural Hazards Data. Retrieved from National Centers for Environmental Information | National Oceanic and Atmospheric Administration. https://www.ngdc.noaa.gov/hazard/

  • OSHA.gov. (2019). Fact Sheet on Natural Disaster Recovery. Retrieved from Occupational Safety & Health Administration, U.S. Department of Labor. https://www.osha.gov/OshDoc/cleanupHazard.html

  • Phillips, C. (2011, March 16). The 10 most Destructive Tsunamis in History. Retrieved from Australian Geographic: https://www.australiangeographic.com.au/topics/science-environment/2011/03/the-10-most-destructive-tsunamis-in-history/

  • Ready.gov. (2019). Risk Mitigation. Retrieved from https://www.ready.gov/risk-mitigation

  • Rudloff, A., Lauterjung, J., Münch, U., & Tinti, S. (2009). Preface “The GITEWS Project (German-Indonesian Tsunami Early Warning System)”. Natural Hazards and Earth System Sciences, 9, 1381–1382.

    CrossRef  Google Scholar 

  • Sidle, R. C., Taylor, D., Lu, X., Adger, W., Lowe, D., De Lange, W., Newnham, R., & Dodson, J. (2004). Interactions of Natural Hazards and Society in Austral-Asia: Evidence in Past and Recent Records. Quaternary International, 118, 181–203.

    CrossRef  Google Scholar 

  • Sobolev, S. V., Babeyko, A. Y., Wang, R., Hoechner, A., Galas, R., Rothacher, M., Sein, D. V., Schröter, J., Lauterjung, J., & Subarya, C. (2007). Tsunami Early Warning Using GPS-Shield Arrays. Journal of Geophysical Research, 112, B08415. https://doi.org/10.1029/2006JB004640. Available from: https://www.researchgate.net/publication/248803935_Tsunami_early_warning_using_GPS-Shield_arrays. Accessed 24 Aug 2020.

  • Sokolova, M., & Lapalme, G. (2009). A Systematic Analysis of Performance Measures for Classification Tasks. Information Processing & Management, 45(4), 427–437.

    Google Scholar 

  • Telford, J., & Cosgrave, J. (2007). The International Humanitarian System and the 2004 Indian Ocean Earthquake and Tsunamis. Disasters, 31(1), 1–28.

    CrossRef  Google Scholar 

  • Teng, C.-M. (1999). Correcting Noisy Data. In Proceedings of the Sixteenth International Conference on Machine Learning (pp. 239–248). San Francisco: Morgan Kaufmann Publishers Inc.

    Google Scholar 

  • Tsunami early warning using GPS-Shield arrays. Available from: https://www.researchgate.net/publication/248803935_Tsunami_early_warning_using_GPS-Shield_arrays [accessed Aug 24 2020].

  • U. N. Population Division. (2001). World Population Prospects: The 2000 Revision, Volume III. New York: United Nations.

    Google Scholar 

  • UNDP. (2010). Disaster Risk Reduction and Recovery. New York: Bureau for Crisis Prevention and Recovery.

    Google Scholar 

  • United Nations. (2017). Factsheet: People and Oceans. New York: The Ocean Conference. Retrieved from https://www.un.org/sustainabledevelopment/wp-content/uploads/2017/05/Ocean-fact-sheet-package.pdf.

    Google Scholar 

  • Walker, S. H., & Duncan, D. B. (1967). Estimation of the Probability of an Event as a Function of Several Independent Variables. Biometrika, 54(1/2), 167–179.

    CrossRef  Google Scholar 

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Lamsal, R., Vijay Kumar, T.V. (2020). Artificial Intelligence Based Early Warning System for Coastal Disasters. In: Singh, A., Fernando, R.L.S., Haran, N.P. (eds) Development in Coastal Zones and Disaster Management. Disaster Research and Management Series on the Global South. Palgrave Macmillan, Singapore. https://doi.org/10.1007/978-981-15-4294-7_21

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