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Journal of the Korean Society of Civil Engineers

Journal of the Korean Society of Civil Engineers

ISO Journal TitleKSCE J. Civ. Environ. Eng. Res.
  • Open Access, Bi-monthly

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  3. 2018-10 (v.38 n.5)
  4. 10.12652/Ksce.2018.38.5.0671
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1 
Chang, F. J., Chen, P. A., Lu, Y. R., Huang, E. and Chang, K. Y. (2014a). “Real-time multi-step-ahead water level forecasting by recurrent neural networks for urban flood control.” Journal Hydrol., Vol. 517, pp. 836-846. 10.1016/j.jhydrol.2014.06.013DOI
2 
Chang, L. C., Shen, H. Y. and Chang, F. J. (2014b). “Regional flood inundation nowcast using hybrid SOM and dynamic neural networks.” Journal Hydrol., Vol. 519, pp. 476-489. 10.1016/j.jhydrol.2014.07.036DOI
3 
Chang, L. C., Shen, H. Y., Wang, Y. F., Huang, J. Y. and Lin, Y. T. (2010). “Clustering-based hybrid inundation model for forecasting flood inundation depths.” Journal Hydrol., Vol. 385, pp. 257-268. 10.1016/j.jhydrol.2010.02.028DOI
4 
Choi, S. M., Yoon, S. S. and Choi, Y. J. (2015). “Evaluation of high-resolution QPE data for urban runoff analysis.” Journal Korea Water Resour. Assoc., Vol. 48, pp. 719-728 (in Korean). 10.3741/JKWRA.2015.48.9.719DOI
5 
Ha, C. Y. (2017). Parameter Optimization Analysis in Urban Flood Simulation by Applying 1D-2D Coupled Hydraulic Model. Ph.D. Thesis, Kyungpook National University.
6 
Jang, S. H., Yoon, J. Y. and Yoon, Y. N. (2006). “A study on the improvement of Huff’s method for applying in Korea : Ⅱ. Improvement of Huff’s method.” Journal Korea Water Resour. Assoc., Vol. 39, No. 9, pp. 779-786 (in Korean).
7 
Jung, K. J. (2005). “Development of the infiltration damage prediction model in a catchment using artificial neural networks.” J. Korean Society of Hazard Mitigation, Vol. 5, No. 2, p. 5 (in Korean).
8 
Kang, J. E. and Lee, M. J. (2015). “Analysis of urban infrastructure risk areas to flooding using neural network in Seoul.” J. Korean Soc. Civ. Eng., Vol. 35, No. 4, p. 997 (in Korean). 10.12652/Ksce.2015.35.4.0997DOI
9 
Lee, B. H. (2006). “A study on the characteristics and composition direction of urban flood control system.” Water and Future, pp. 50-54.
10 
Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D. and Veith, T. L. (2007). “Model evaluation guidelines for systematic quantification of accuracy in watershed simulations.” ASABE., Vol. 50, No. 3, pp. 885-900. 10.13031/2013.23153DOI
11 
Oh, J. W., Park, J. H. and Kim, Y. K. (2008). “Missing hydrological data estimation using neural network and real time data reconciliation.” Journal Korea Water Resour. Assoc., Vol. 41, No. 10, p. 1059 (in Korean). 10.3741/JKWRA.2008.41.10.1059DOI
12 
Pan, T. Y., Lai, J. S., Chang, T. J., Chang, H. K., Chang, K. C. and Tan, Y. C. (2011). “Hybrid neural networks in rainfall-inundation forecasting based on a synthetic potential inundation database.” Nat. Hazards Earth Syst. Sci., Vol. 11, pp. 771-787. 10.5194/nhess-11-771-2011DOI
13 
Seoul Metropolitan City. (2015). Comprehensive Plan for Storm and Flood Damage Reduction (in Korean).
14 
Shen, H. Y. and Chang, L. C. (2013). “Online multistep-ahead inundation depth forecasts by recurrent NARX networks.” Hydrol. Earth Syst. Sci., Vol. 17, pp. 935-945. 10.5194/hess-17-935-2013DOI
15 
Son, A. L. and Han, K. Y. (2014). “The development of urban inundation reduction model combined real-time data-driven estimation and 2D hydraulic analysis.” Proc. of Conf. Korean Society of Hazard Mitigation, Vol. 2014, p. 90.
16 
Toth, E., Brath, A. and Montanari, A. (2000). “Comparison of short-term rainfall prediction models for real-time flood forecasting.” Journal Hydrol., Vol. 239, pp. 132-147. 10.1016/S0022-1694(00)00344-9DOI
17 
Tsai, M. H., Sung, E. X. and Kang, S. C. (2016). “Data-driven flood analysis and decision support.” Nat. Hazards Eearth Syst, Sci. Discuss., doi:10.5194/nhess-2016-141. 10.5194/nhess-2016-141DOI
18 
Yoon, K. H., Seo, B. C. and Shin, H. S. (2004). “Dam inflow forecasting for short term flood based on neural networks in nakdong river basin.” Journal Korea Water Resour. Assoc., Vol. 37, No. 1, pp. 67-75 (in Korean). 10.3741/JKWRA.2004.37.2.145DOI