Applicability study on urban flooding risk criteria estimation algorithm using cross-validation and SVM |
Lee, Hanseung
(Disaster Prevention Research Division, National Disaster Management Research Institute)
Cho, Jaewoong (Disaster Prevention Research Division, National Disaster Management Research Institute) Kang, Hoseon (Disaster Prevention Research Division, National Disaster Management Research Institute) Hwang, Jeonggeun (Disaster Prevention Research Division, National Disaster Management Research Institute) |
1 | Behzad, M., Asghari, K., Eazi, M., and Palhang, M. (2009). "Generalization performance of support vector machines and neural networks in runoff modeling, expert systems with applications." An International Journal, Vol. 36, No. 4, pp. 7624-7629. |
2 | Chang, J.G. (2006). "Real-time vehicle recognition mechanism using support vector machines." Journal of Korea Academia Industrial Cooperation Society, Vol. 7, No. 6, pp. 1160-1166. |
3 | Cho, J.W., Choi, C.W., Kang, H.S., Lee, H.S., Bae, C.Y., Hwang, J.G., and Bae, S.J. (2018b). Deep learning based urban flood alert critrria estimation model design. NDMI-PR-2018-09-01-01. National Disaster Management Research Institute, Ulsan. |
4 | Cho, J., Bae, C., and Kang, H. (2018a). "Development and application of urban flood alert criteria considering damage records and runoff characteristics." Journal of Korea Water Resource Association, KWRA, Vol. 51, No. 1, pp. 1-10. DOI |
5 | Hipni, A., El-shafie, A., Najah, A., Karim, O.A., Hussain, A., and Mukhlisin, M. (2013). "Daily forecasting of dam water levels: comparing a support vector machine (SVM) model with adaptive neuro fuzzy inference system (ANFIS)." Water Resources Management, Vol. 27, No. 10, pp. 3803-3823. DOI |
6 | Kim, S., Shiri, J., and Kisi, O. (2012). "Pan evaporation modeling using neural computing approach for different climatic zones." Water resources management, Vol. 26, No. 11, pp. 3231-3249. DOI |
7 | Korea Meteorological Administration (KMA) (2016). Planning study on introduction plan of influence forecast. |
8 | Lin, G.F., Chen, G.R., Huang, P.Y., and Chou, Y.C. (2009). "Support vector machine-based models for hourly reservoir inflow forecasting during typhoon-warning periods." Journal of hydrology, Vol. 372, No. 1-4, pp. 17-29. DOI |
9 | Ministry of the Interior and Safety (MOIS) (2018). 2017 Statistical yearbook of natural disaster. |
10 | Misra, D., Oommen, T., Agarwal, A., Mishra, S.K., and Thompson, A.M. (2009). "Application and analysis of support vector machine based simulation for runoff and sediment yield." Biosystems Engineering, Biosystems Engineering, Vol. 103, No. 4, pp. 527-535. DOI |
11 | Vapnik, V. (1995). The nature of statistical learning theory. Springer, Verlag New York. |
12 | World Meteorological Organization (WMO) (2015). Guidelines on multi-hazard impact-based forecast and warning services. No. 1150, Swiss Geneva. |