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2021 Vol.54, Issue 12S Preview Page

Research Article

31 December 2021. pp. 1061-1069
Adnan, R.M., Yuan, X., Kisi, O., and Yuan, Y. (2017). "Streamflow forecasting using artificial neural network and support vector machine models." American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS), Vol. 29, No. 1, pp. 286-294.
Bae, Y., Kim, J., Wang, W., Yoo, Y., Jung, J., and Kim, H.S. (2019). "Monthly inflow forecasting of Soyang River Dam using VARMA and machine learning models." Journal of Climate Research, Vol. 14, No. 3, pp. 183-198. 10.14383/cri.2019.14.3.183
Chang, F.J., Chen, P.A., Lu, Y.R., Huang, E. and Chang, K.Y. (2014). "Real-time multi-step-ahead water level forecasting by recurrent neural networks for urban flood control." Journal of Hydrology, Vol. 517, pp. 836-846. 10.1016/j.jhydrol.2014.06.013
Coulibaly, P., and Anctil, F. (1999). "Real-time short-term natural water inflows forecasting using recurrent neural networks." International Joint Conference on IEEE, Washington DC, U.S., pp. 3802-3805. 10.1109/IJCNN.1999.830759
Dawson, C.W., and Wilby, R. (1998). "An artificial neural network approach to rainfall-runoff modelling". Hydrological Sciences Journal, Vol. 43, No. 1, pp. 47-66. 10.1080/02626669809492102
Hinton, G.E., Osindero, S., and Teh, Y.W. (2006). "A fast learning algorithm for deep belief nets." Neural Computation, Vol. 18, No. 7, pp. 1527-1554. 10.1162/neco.2006.18.7.152716764513
Hochreiter, S., and Schmidhuber, J. (1997). "Long short-term memory." Neural Computation, Vol. 9, No. 8, pp. 1735-1780. 10.1162/neco.1997.9.8.17359377276
Huang, X., Li, Y., Tian, Z., Ye, Q., Ke, Q., Fan, D., Mao, G., Chen, A. Liu, J. (2021). "Evaluation of short-term streamflow prediction methods in Urban river basins." Physics and Chemistry of the Earth, Parts A/B/C, Vol. 123. 10.1016/j.pce.2021.103027
Jung, J., Mo, H., Lee, J., Yoo, Y., and Kim, H.S. (2021). "Flood stage forecasting at the Gurye-Gyo station in Sumjin River using LSTM-based deep learning models." Journal of the Korean Society of Hazard Mitigation, Vol. 21, No. 3, pp. 193-201. 10.9798/KOSHAM.2021.21.3.193
Jung, S., Cho, H., Kim, J., and Lee, G. (2018). "Prediction of water level in a tidal river using a deep-learning based LSTM model." Journal of Korea Water Resources Association, Vol. 51, No. 12, pp. 1207-1216.
Kim, B.J. (2007). Comparative study of storage function and SSARR models for the flood hydrograph forecasting of a Miho Stream. Ph. D. dissertation, Inha University,
Kim, D., Kim, J., Kwak, J., Necesito, I.V., Kim, J., and Kim, H.S. (2020a). "Development of water level prediction models using deep neural network in mountain wetlands." Journal of Wetlands Research, Vol. 22, No. 2, pp. 106-112.
Kim, H.I., Lee, J.Y., Han, K.Y. and Cho, J.W. (2020b). "Applying observed rainfall and deep neural network for urban flood analysis." Journal of the Korean Society of Hazard Mitigation, Vol. 20, No. 1, pp. 339-350. 10.9798/KOSHAM.2020.20.1.339
Kim, Y.J., Kim, T.W., Yoon, J.S., and Kim, I.H. (2019a). "Study on prediction of similar typhoons through neural network optimization." Journal of Ocean Engineering and Technology, Vol. 33, No. 5, pp. 427-434. 10.26748/KSOE.2019.065
Kim, Y.J., Kim, T.W., Yoon, J.S., and Kim, M.K. (2019b). "Study of the construction of a coastal disaster prevention system using deep learning." Journal of Ocean Engineering and Technology, Vol. 33, No. 6, pp. 590-596. 10.26748/KSOE.2019.066
Koç, C.K. (1995). "Analysis of sliding window techniques for exponentiation." Computers & Mathematics with Applications, Vol. 30, No. 10, pp. 17-24. 10.1016/0898-1221(95)00153-P
Lee, G.H., Ryu, Y.U., and Park, J.S. (2020). "Investigation and analysis of causes of flood damage in the Yeongsan River and Seomjin River basins in August 2020." Water for Future, Vol. 53, No. 11, pp. 21-48.
Lee, M., You, Y., Kim, S., Kim, K., and Kim, H. (2018). "Decomposition of water level time series of a tidal river into tide, wave and rainfall-runoff components." Water, Vol. 10, No. 11, pp. 1568. 10.3390/w10111568
Olah, C. (2015). Understanding lstm networks, Accessed on August, 2011, <http://>.
Pan, M., Zhou, H., Cao, J., Liu, Y., Hao, J., Li, S., and Chen, C.H. (2020). "Water level prediction model based on GRU and CNN." Ieee Access, Vol. 8, pp. 60090-60100. 10.1109/ACCESS.2020.2982433
Seo, Y., Choi, E., and Yeo, W. (2017). "Reservoir water level forecasting using machine learning models." Journal of the Korean Society of Agricultural Engineers, Vol. 59, No. 3, pp. 97-110. 10.5389/KSAE.2017.59.3.097
Yoo, H.J., Lee, S.O., Choi, S.H., and Park, M.H. (2019). "A study on the data driven neural network model for the prediction of time series data: Application of water surface elevation forecasting in Hangang River bridge." Korean Society of Disaster & Security, Vol. 12, No. 2, pp. 73-82.
  • Publisher(Ko) :한국수자원학회
  • Journal Title :Journal of Korea Water Resources Association
  • Journal Title(Ko) :한국수자원학회 논문집
  • Volume : 54
  • No :12
  • Pages :1061-1069
  • Received Date :2021. 09. 27
  • Revised Date :2021. 10. 25
  • Accepted Date : 2021. 10. 26