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http://dx.doi.org/10.3741/JKWRA.2018.51.12.1207

Prediction of water level in a tidal river using a deep-learning based LSTM model  

Jung, Sungho (Department of Disaster Prevention and Environmental Engineering, Kyungpook National University)
Cho, Hyoseob (Water Resources information center of Han River Flood Control Office, Ministry of Environment)
Kim, Jeongyup (Water Resources information center of Han River Flood Control Office, Ministry of Environment)
Lee, Giha (Department of Disaster Prevention and Environmental Engineering, Kyungpook National University)
Publication Information
Journal of Korea Water Resources Association / v.51, no.12, 2018 , pp. 1207-1216 More about this Journal
Abstract
Discharge or water level predictions at tidally affected river reaches are currently still a great challenge in hydrological practices. This research aims to predict water level of the tide dominated site, Jamsu bridge in the Han River downstream. Physics-based hydrodynamic approaches are sometimes not applicable for water level prediction in such a tidal river due to uncertainty sources like rainfall forecasting data. In this study, TensorFlow deep learning framework was used to build a deep neural network based LSTM model and its applications. The LSTM model was trained based on 3 data sets having 10-min temporal resolution: Paldang dam release, Jamsu bridge water level, predicted tidal level for 6 years (2011~2016) and then predict the water level time series given the six lead times: 1, 3, 6, 9, 12, 24 hours. The optimal hyper-parameters of LSTM model were set up as follows: 6 hidden layers number, 0.01 learning rate, 3000 iterations. In addition, we changed the key parameter of LSTM model, sequence length, ranging from 1 to 6 hours to test its affect to prediction results. The LSTM model with the 1 hr sequence length led to the best performing prediction results for the all cases. In particular, it resulted in very accurate prediction: RMSE (0.065 cm) and NSE (0.99) for the 1 hr lead time prediction case. However, as the lead time became longer, the RMSE increased from 0.08 m (1 hr lead time) to 0.28 m (24 hrs lead time) and the NSE decreased from 0.99 (1 hr lead time) to 0.74 (24 hrs lead time), respectively.
Keywords
Tidal river; Deep learning; lead time; Jamsu bridge; LSTM model;
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Times Cited By KSCI : 4  (Citation Analysis)
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1 Oh, C. Y., Yang, D. H., Lee, J. H., and Won, Y. S. (2014). "Application of Artificial Neural Network Model for Flood Forecasting." Water for Future, Vol. 47, No. 5, pp. 50-62.
2 Olah, C. (2015). "Understanding lstm networks." GITHUB blog, http://colah.github.io/posts/2015-08-Understanding-LSTMs/.
3 Park, C. G., and Baek, K. O. (2017). "Reconsideration of evaluating design flood level at Imjin River estuary." Journal of Korea Water Resources Association, Vol. 50, No. 9, pp. 617-625.   DOI
4 Song, C. G., Kim, H. J., and Lee, D. S. (2014). "Analysis of Flow Reversal by Tidal Elevation and Discharge Conditions in a Tidal River." Journal of the Korean society of Safety, Vol. 29, No. 6, pp. 104-110.   DOI
5 Seo, I. W., Song, C. G., and Lee, M. E. (2008). "Flow and Mixing behavior at the Tidal Reach of Han River." Journal of the Korean Society of Civil Engineers, KSCE, Vol. 28, No. 6B, pp. 731-741.
6 Supharatid, S. (2003). "Application of a neural network model in establishing a stage-discharge relationship for a tidal river." Hydrological processes, Vol. 17, No. 15, pp. 3085-3099.   DOI
7 Shin, Y. K., and Yoon, K. S. (2005). "The Spatial Distribution of Water Quality and Sediments Characteristics in the Han River Estuary." Journal of the Korean Geomorphological Association, Vol. 12, No. 4, pp. 13-23.
8 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, Vol. 45, No. 1, pp. 69-79.   DOI
9 Chiang, Y. M., Chang, L. C., Tsai, M. J., Wang, Y.F., and Chang, F.J. (2010). "Dynamic neural networks for real-time water level predictions of sewerage systems-covering gauged and ungauged sites." Hydrology and Earth System Sciences, Vol. 14, No. 7, pp. 1309-1319.   DOI
10 Geron, A. (2017). "Hands-on machine learning with Scikit-Learn and TensorFlow." O'Reilly Media.
11 Greff, K., Srivastava, R. K., Koutnik, J., Steunebrink, B.R., and Schmidhuber, J. (2017). "LSTM: A search space odyssey." IEEE transactions on neural networks and learning systems, Vol. 28, No. 10, pp. 2222-2232.   DOI
12 Hidayat, H., Hoitink, A. J. F., Sassi, M.G., and Torfs, P. J. J. F. (2014). "Prediction of discharge in a tidal river using artificial neural networks." Journal of Hydrologic Engineering, ASCE, Vol. 19, No. 8, 04014006.   DOI
13 Hochreiter, S., and Schmidhuber, J. (1997). "Long short-term memory." Neural computation, Vol. 9, No. 8, pp. 1735-1780.   DOI
14 Jung, S. H., Lee, D. E., and Lee, K. S. (2018). "Prediction of River Water Level Using Deep-Learning Open Library." Journal of Korean Society of Hazard Mitigation, Vol. 18, No. 1, pp. 1-11.
15 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, Vol. 62, pp. 1375-1380.
16 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, Vol. 38, No. 2, pp. 121-131.   DOI
17 Lee, G. H., Jung, S. H., and Lee, D. E. (2018). "Comparison of physicsbased and data-driven models for streamflow simulation of the Mekong river." Journal of Korea Water Resources Association, Vol. 51, No. 6, pp. 503-514.   DOI
18 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 Korean Society of Hazard Mitigation, Vol. 10, No. 2, pp. 111-122.
19 Lee, S. J., Ryoo, K. S., Lee, B. S., and Yoon, J. S. (2007). "Development of Regression Equation for Water Quantity Estimation in a Tidal River." Journal of Korean Society on Water Quality, Vol. 23, No. 3, pp. 385-390.
20 Maier, H. R., and Dandy, G. C. (2000). "Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications." Environmental modelling & software, Vol. 15, No. 1, pp. 101-124.   DOI
21 Ministry of Land Infrastructure and Transport, Han River Flood Control Office (2017). Han River Forecast Yearbook.