Abstract
In this paper, we propose a route prediction method that uses a self-organizing incremental neural network (SOINN). For the training and testing of the neural network, only the latitude and longitude of the tropical storm and atmospheric information around East Asia are required. Our proposed method can predict the movement of a tropical storm with only a short calculation time, and the prediction accuracy is close to the accuracy of the Japan Meteorological Agency. This paper describes the algorithm used for the neural network training, the process for handling the data sets and the method used to predict the storm trajectory. Additionally, experimental results that indicate the performance of our method are presented in the results section.
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Fair, C.C., Kuhn, P.M., Malhotra, N., Shapiro, J.N.: Natural disasters and political engagement: evidence from the 2010-11 Pakistani Floods. Q. J. Political Sci. 12(1), 99–141 (2017)
Toya, H., Skidmore, M.: Economic development and the impacts of natural disasters. Econ. Lett. 94(1), 20–25 (2007)
Guha-Sapir, D., Vos, F., Below, R., Ponserre, S.: Annual disaster statistical review 2011: the number and trends. Centre for Research on the Epidemiology of Disasters (CRED) (2012)
Jochen, Z., Andreas, N.: Early Warning Systems for Natural Disaster Reduction, 1st edn. Springer, Heidelberg (2003)
Lin, T.C., Hamburg, S.P., Lin, K.C., Wang, L.J., Chang, C.T., Hsia, Y.J., Vadeboncoeur, M.A., McMullen, C.M., Liu, C.-P.: Typhoon disturbance and forest dynamics: lessons from a northwest Pacific subtropical forest. Ecosystems 14(1), 127–143 (2011)
Bellingham, P.J., Takashi, K., Shin-ichiro, A.: The effects of a typhoon on Japanese warm temperate rainforests. Ecol. Res. 11(3), 229–247 (1996)
Shen, F., Hasegawa, O.: An incremental network for on-line unsupervised classification and topology learning. Neural Netw. 19(1), 90–106 (2006)
Yamasaki, K., Makibuchi, N., Shen, F., Hasegawa, O.: Self-organizing incremental neural Network-SOINN- and its usage. Brain Neural Netw. 17(4), 187–196 (2010)
National Ocean Service Homepage. http://oceanservice.noaa.gov/facts/cyclone.html
Japan Meteorological Agency Homepage. http://www.data.jma.go.jp/fcd/yoho/typhoon/route_map/index.html
Japan Weather Association’s information Homepage. http://www.tenki.jp/guide/chart/
Japan Meteorological Agency Homepage, Average error of the year. http://www.data.jma.go.jp/fcd/yoho/typ_kensho/table.html
Japan Meteorological Agency Homepage, Facility introduction. http://www.mri-jma.go.jp/Facility/supercomputer.html
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Kim, W., Hasegawa, O. (2017). Prediction of Tropical Storms Using Self-organizing Incremental Neural Networks and Error Evaluation. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10636. Springer, Cham. https://doi.org/10.1007/978-3-319-70090-8_86
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DOI: https://doi.org/10.1007/978-3-319-70090-8_86
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