Browse > Article
http://dx.doi.org/10.3741/JKWRA.2022.55.7.495

Prediction of dam inflow based on LSTM-s2s model using luong attention  

Lee, Jonghyeok (Department of Civil and Environmental Engineering, Yonsei University)
Choi, Suyeon (Department of Civil and Environmental Engineering, Yonsei University)
Kim, Yeonjoo (Department of Civil and Environmental Engineering, Yonsei University)
Publication Information
Journal of Korea Water Resources Association / v.55, no.7, 2022 , pp. 495-504 More about this Journal
Abstract
With the recent development of artificial intelligence, a Long Short-Term Memory (LSTM) model that is efficient with time-series analysis is being used to increase the accuracy of predicting the inflow of dams. In this study, we predict the inflow of the Soyang River dam, using the LSTM model with the Sequence-to-Sequence (LSTM-s2s) and attention mechanism (LSTM-s2s with attention) that can further improve the LSTM performance. Hourly inflow, temperature, and precipitation data from 2013 to 2020 were used to train the model, and validate and test for evaluating the performance of the models. As a result, the LSTM-s2s with attention showed better performance than the LSTM-s2s in general as well as in predicting a peak value. Both models captured the inflow pattern during the peaks but detailed hourly variability is limitedly simulated. We conclude that the proposed LSTM-s2s with attention can improve inflow forecasting despite its limits in hourly prediction.
Keywords
LSTM; Sequence-to-Sequence; Attention; Dam Inflow prediction;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 Costa-jussa, M.R. (2018). "From feature to paradigm: Deep learning in machine translation." The Journal of Artificial Intelligence Research, Vol. 61, pp. 947-974.   DOI
2 Dawson, C.W., and Wilby, R.L. (2001). "Hydrological modelling using artificial neural networks." Progress in Physical Geography, Vol. 25, No. 1, pp. 80-108.   DOI
3 Han, H., Choi, C., Jung, J., and Kim, H.S. (2021). "Application of sequence to sequence learning based LSTM model (LSTM-s2s) for forecasting dam inflow." Journal of Korea Water Resources Association, Vol. 54, No. 3, pp. 157-166.   DOI
4 Hochreiter, S., and Schmidhuber, J. (1997). "Long short-term memory." Neural Computation, Vol. 9, No. 8, pp. 1735-1780.   DOI
5 Fu, M., Fan, T., Ding, Z., Salih, S.Q., Al-Ansari, N., and Yaseen, Z.M. (2020). "Deep learning data-intelligence model based on adjusted forecasting window scale: Application in daily streamflow simulation." IEEE Access, Vol. 8, pp. 32632-32651.   DOI
6 Li, Z., Liu, W., Zhang, X., and Zheng, F. (2009). "Impacts of land use change and climate variability on hydrology in an agricultural catchment on the loess plateau of China." Journal of Hydrology, Vol. 377, No. 1-2, pp. 35-42.   DOI
7 Qi, Y., Zhou, Z., Yang, L., Quan, Y., and Miao, Q. (2019). "A decomposition-ensemble learning model based on LSTM neural network for daily reservoir inflow forecasting." Water Resources Management, Vol. 33, No. 12, pp. 4123-4139.   DOI
8 Sutskever, I., Vinyals, O., and Le, Q.V. (2014). "Sequence to sequence learning with neural networks." Proceedings of the 27th International Conference on Neural Information Processing Systems, MIT Press, Cambridge, MA, U.S., Vol. 2, pp. 3104-3112.
9 Tiwari, M.K., and Chatterjee, C. (2010). "Development of an accurate and reliable hourly flood forecasting model using waveletbootstrap-ANN (WBANN) hybrid approach." Journal of Hydrology, Vol. 394, No. 3-4, pp. 458-470.   DOI
10 Yan, L., Chen, C., Hang, T., and Hu, Y. (2021). "A stream prediction model based on attention-LSTM." Earth Science Informatics, Vol. 14, No. 2, pp. 723-733.   DOI
11 Zhou, H., Zhang, Y., Yang, L., Liu, Q., Yan, K., and Du, Y. (2019). "Short-term photovoltaic power forecasting based on long short term memory neural network and attention mechanism." IEEE Access, Vol. 7, pp. 78063-78074.   DOI
12 Devia, G.K., Ganasri, B.P., and Dwarakish, G.S. (2015). "A review on hydrological models." Aquatic Procedia, Vol. 4, pp. 1001-1007.   DOI
13 Kwon, O.-I., and Shim, M.-P. (1997). "Determination scheme of variable restricted water level during flood period of multipurpose dam." Journal of Korea Water Resources Association, Vol. 30, No. 6, pp. 709-720.
14 Li, W., Guo, D., and Fang, X. (2018). "Multimodal architecture for video captioning with memory networks and an attention mechanism." Pattern Recognition Letters, Vol. 105, pp. 23-29.   DOI
15 Bicknell, B.R., Imhoff, J.C., Kittle Jr, J.L., Donigian Jr, A.S., and Johanson, R.C. (1996). Hydrological simulation program-FORTRAN. User's Manual for Release 11. U.S. Environmental Protection Agency, Washington, D.C., U.S.
16 Choi, H., Cho, K., and Bengio, Y. (2018). "Fine-grained attention mechanism for neural machine translation." Neurocomputing, Vol. 284, pp. 171-176.   DOI
17 Choi, J.-H., Kim, J.-S., Gwon, J.-H., and Moon, Y.-I. (2016). "Weighting assessment on evaluation indicators of dam rehabilitation using the AHP analysis." Journal of Korea Water Resources Association, Vol. 49, No. 5, pp. 381-389.   DOI
18 Marmanis, D., Schindler, K., Wegner, J. D., Galliani, S., Datcu, M., and Stilla, U. (2018). "Classification with an edge: Improving semantic image segmentation with boundary detection." ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 135, pp. 158-172.   DOI
19 He, X., Luo, J., Li, P., Zuo, G., and Xie, J. (2020). "A hybrid model based on variational mode decomposition and gradient boosting regression tree for monthly runoff forecasting." Water Resources Management, Vol. 34, No. 2, pp. 865-884.   DOI
20 Kao, I.-F., Zhou, Y., Chang, L.-C., and Chang, F.-J. (2020). "Exploring a long short-term memory based encoder-decoder framework for multi-step-ahead flood forecasting." Journal of Hydrology, Vol. 583, 124631.   DOI
21 Nourani, V. (2017). "An emotional ANN (EANN) approach to modeling rainfall-runoff process." Journal of Hydrology, Vol. 544, pp. 267-277.   DOI
22 Song, K., Yao, T., Ling, Q., and Mei, T. (2018). "Boosting image sentiment analysis with visual attention." Neurocomputing, Vol. 312, pp. 218-228.   DOI
23 Kwak, J.-W., Kim, H.-S., and Hong, I.-P. (2009). "A study of progressive parameter calibrations for rainfall-runoff models." Proceedings of the Korea Water Resources Association Conference, pp. 1499-1503.
24 Luong, M.-T., Pham, H., and Manning, C.D. (2015). "Effective approaches to attention-based neural machine translation." Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, ACL, Lisbon, Portugal, pp. 1412-1421.
25 Xiang, Z., Yan, J., and Demir, I. (2020). "A rainfall-runoff model with LSTM-based sequence-to-sequence learning." Water Resources Research, Vol. 56, No. 1, e2019WR025326.
26 Cho, K., and Kim, Y. (2022). "Improving streamflow prediction in the WRF-Hydro model with LSTM networks." Journal of Hydrology, Vol. 605, 127297.   DOI
27 Jung, S., Lee, D., and Lee, K. (2017). "Prediction of river water level using deep-learning open library." Korean Society of Hazard Mitigation, Vol. 18, No. 1, pp. 1-11.   DOI
28 Kim, B.K., Kim, S., Lee, E.T., and Kim, H.S. (2007). "Methodology for estimating ranges of SWAT model parameters: Application to Imha Lake inflow and suspended sediments." Journal of The Korean Society of Civil Engineers B, Vol. 27 No. 6B, pp. 661-668.
29 Le, X.-H., Ho, H. V., Lee, G., and Jung, S. (2019). "Application of Long Short-Term Memory (LSTM) neural network for flood forecasting." Water, Vol. 11, No. 7, 1387.   DOI
30 Legates, D.R., and McCabe Jr., G.J. (1999). "Evaluating the use of "goodness-of-fit" Measures in hydrologic and hydroclimatic model validation." Water Resources Research, Vol. 35, No. 1, pp. 233-241.   DOI
31 National Water Resources Management Infomation System (WAMIS) (2003). South Korea, accessed 10, January 2022, .
32 Noh, J.-K., and Lee, J.-N. (2011). "Comparison of streamflow runoff model in Korea for applying to reservoir operation." Korean Journal of Agricultural Science, Vol. 38, No. 3, pp. 513-524.   DOI
33 Nourani, V., Komasi, M., and Mano, A. (2009). "A multivariate ANN-wavelet approach for rainfall-runoff modeling." Water Resources Management, Vol. 23, No. 14, pp. 2877-2894.   DOI