Fig. 1. Structure of ANN model
Fig. 2. Structure of NARX model
Fig. 3. The tidal level at Incheon and water surface elevation at Hangang RIver Bridge in July 2011
Fig. 4. Discrete fourier transform of water surface elevation, tidal level and precipitation in July 2011
Fig. 5. Hidden layer sensitivity analysis result
Fig. 6. Time delay sensitivity analysis result
Fig. 7. The water surface elevation distribution (left) and scatter plot (right) according to neural network model
Table 1. Input data characteristics
Table 2. Model setup
Table 3. Comparison of accuracy about ANN, RNN, and NARX model
참고문헌
- Chen, W. B., Liu, W. C., and Hsu, M. H. (2012). Comparison of ANN Approach with 2D and 3D Hydrodynamic Models for Simulating Estuary Water Stage. Advances in Engineering Software. 45(1): 69-79. https://doi.org/10.1016/j.advengsoft.2011.09.018
- Coulibaly, P. and Anctil, F. (1999). Real-time Short-term Natural Water Inflows Forecasting using Recurrent Neural Networks. Neural Networks. 1999. IJCNN'99. International Joint Conference on, IEEE: 3802-3805.
- Kim, S. and Tachikawa, Y. (2017). Real-time River-stage Prediction with Artificial Neural Network based on Only Upstream Observation Data. Annual Journal of Hydraulic Engineering. JSCE. 62: 1375-1380.
- Lee, E.R., Kim W., and Kim, S.H. (2005). Effect of Flood Stage by Hydraulic Factors in Han River. Journal of Korea Water Resources Association. 38(2): 121-131. https://doi.org/10.3741/JKWRA.2005.38.2.121
- Lee, J. K. and Lee, J. H. (2010). A Study on Water Level Rising Travel Time due to Discharge of Paldang Dam and Tide of Yellow Sea in Downstream Part of Paldang Dam. Journal of the Korean Society of Hazard Mitigation. 10(2): 111-122.
- 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. 50(3): 885-900. https://doi.org/10.13031/2013.23153
- Shen, H. Y. and Chang, L. C. (2013). Online Multistep-ahead Inundation Depth Forecasts by Recurrent NARX Networks. Hydrol Earth Syst. Sci. 17: 935-945. https://doi.org/10.5194/hess-17-935-2013
- Song, C. G., Kim, H. J., and Rhee, D. S. (2014). Analysis of Flow Reversal by Tidal Elevation and Discharge Conditions in a Tidal River. Journal of the Korean Society of Safety. 29(6): 104-110. https://doi.org/10.14346/JKOSOS.2014.29.6.104
- Thirumalaiah, K. and Deo, M.C. (1998). Real-Time Flood Forecasting Using Neural Networks. Computer-Aided Civil and Infrastructure Engineering. 13(2): 101-111. https://doi.org/10.1111/0885-9507.00090
- 기상청 (2017). 이상기후 보고서.
- 김현일, 금호준, 한건연 (2018). 도시침수 해석을 위한 동적 인공신경망의 적용 및 비교. 대한토목학회논문집. 38(5): 671-683. https://doi.org/10.12652/Ksce.2018.38.5.0671
- 한강홍수통제소 (2016). 한강하천예보연감.
피인용 문헌
- 기계학습을 활용한 하절기 기온 및 폭염발생여부 예측 vol.13, pp.2, 2020, https://doi.org/10.21729/ksds.2020.13.2.27
- 합리적인 하천수 관리체계 구축을 위한 자료기반 방류량 예측모형 개발 vol.13, pp.4, 2020, https://doi.org/10.21729/ksds.2020.13.4.75