Abstract
In this paper, the model for predicting tsunami using machine learning classification algorithm and the warning system using IoT has been proposed. The data used for training the model is based on the historical tsunami data comprising tsunami records from 2100 BC. The model has been trained based on the earthquake parameters in the dataset, as earthquakes are the main cause of death-causing tsunamis around the planet. It can classify the earthquake data as Tsunamigenic or Non-Tsunamigenic based on which the warning system is triggered, using the cloud technologies for communication. The model has been tested on the tsunami-causing earthquake records, and it shows an accuracy rate of over 95%. The model and the warning system together can automate the manual tsunami prediction techniques and alert system for the same, to save humankind in a better way than the past.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
National Oceanic and Atmospheric Administration. http://www.tsunami.noaa.gov
Indian Ocean Tsunami Information Center. http://www.iotic.ioc-unesco.org
Truong HV (2012) Wave-propagation velocity, tsunami speed, amplitudes, dynamic water—attenuation factors. In: Proceedings of world conference on earthquake engineering, pp 1–10 (2012)
International Tsunami Information Center. http://www.itic.ioc-unesco.org
Puspito NT, Gunawan I (2005) Tsunami sources in the Sumatra region, Indonesia and simulation of the 26 December 2004 Aceh tsunami. ISET J Earthquake Technol 42:4
Zaytsev A, Kostenko I, Kurkin A, Pelinovsky E, Yalciner AC (2016) The depth effect of earthquakes on tsunami heights in the Sea of Okhotsk. Turk J Earth Sci 25(4):289–299
All About Tsunamis. https://nhmu.utah.edu/sites/default/files/attachments/All%20About%20Tsunamis.pdf
US Geological Survey. https://www.earthquake.usgs.gov
Tayal T, Prema KV (2014) Design and implementation of a fuzzy based tsunami warning system. IJRET: Int J Res Eng Technol (2014)
Cherian CM, Jayaraj N (2010) Artificially intelligent tsunami early warning system. In: 12th international conference on computer modelling and simulation. IEEE Press, Cambridge, pp 39–44
Casey K, Lim A, Dozier G (2008) A sensor network architecture for tsunami detection and response. Int J Distrib Sens Netw 4(1):27–42
Casey K, Lim A, Dozier G (2006) Evolving general regression neural networks for tsunami detection and response. In: IEEE Congress on Evolutionary Computation. IEEE Press, Vancouver, pp 2451–2458
Angove MD, Rabenold CL, Weinstein SA, Eblé MC, Whitmore PM (2015) US tsunami warning system: capabilities, gaps, and future vision. In: OCEANS’15 MTS/IEEE. IEEE Press, Washington, pp 1–5
Le Mehaute B, Hwang LS, Van Dorn W (1971) Methods for improving tsunami warning system. In: IEEE 1971 conference on engineering in the ocean environment. IEEE Press, San Diego, pp 329–331
USGS Real-time Notifications, Feeds and Web Services. http://www.earthquake.usgs.gov/earthquakes/feed
National Geophysical Data Center/World Data Service (NGDC/WDS) (2018) Global historical tsunami database. National Geophysical Data Center, NOAA. https://doi.org/10.7289/v5pn93h7
Distance to the Nearest Coast. https://oceancolor.gsfc.nasa.gov/docs/distfromcoast
Random Forests. http://pages.cs.wisc.edu/~matthewb/pages/notes/pdf/ensembles/RandomForests.pdf
Rouet-Leduc B, Hulbert C, Lubbers N, Barros K, Humphreys CJ, Johnson PA (2017) Machine learning predicts laboratory earthquakes. Geophys Res Lett 44(18):9276–9282
Confusion Matrix. https://en.wikipedia.org/wiki/Confusion_matrix
Precision and Recall. https://en.wikipedia.org/wiki/Precision_and_recall
F1 score. https://en.wikipedia.org/wiki/F1_score
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Pughazhendhi, G., Raja, A., Ramalingam, P., Elumalai, D.K. (2019). Earthosys—Tsunami Prediction and Warning System Using Machine Learning and IoT. In: Chaki, N., Devarakonda, N., Sarkar, A., Debnath, N. (eds) Proceedings of International Conference on Computational Intelligence and Data Engineering. Lecture Notes on Data Engineering and Communications Technologies, vol 28. Springer, Singapore. https://doi.org/10.1007/978-981-13-6459-4_12
Download citation
DOI: https://doi.org/10.1007/978-981-13-6459-4_12
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-6458-7
Online ISBN: 978-981-13-6459-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)