Acknowledgement
이 논문은 2021년도 정부(교육부)의 재원으로 한국연구재단의 지원을 받아 수행된 기초연구사업임(No.2021R1I1A3050803).
References
- Adamowski, J., and Chan, H.F. (2011). "A wavelet neural network conjunction model for groundwater level forecasting." Journal of Hydrology, Vol. 407, pp. 28-40. https://doi.org/10.1016/j.jhydrol.2011.06.013
- Chen, P.-A., Chang, L.-C., and Chang, L.-C. (2013). "Reinforced recurrent neural networks for multi-step-ahead flood forecasts." Journal of Hydrology, Vol. 497, pp. 71-79. https://doi.org/10.1016/j.jhydrol.2013.05.038
- Cho, K., Van Merrienboer, B., Bahdanau, D., and Bengio, Y. (2014). "On the properties of neural machine translation: Encoder-decoder approaches." Proceedings of the SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation, Doha, Qatar, pp. 103-111.
- Guo, F., Yang, J., Li, H., Li, G., and Zhang, Z. (2021). "A convLSTM conjunction model for groundwater level forecasting in a karst aquifer considering connectivity characteristics." Water, Vol. 13, No. 19, 2759.
- Hipni, A., El-Shafie, A., Najah, A., Karim, O.A., Hussain, A., and Mukhlisin, M. (2013). "Daily forecasting of dam water levels: Comparing a support vector machine (SVM) model with adaptiveneuro fuzzy inference system (ANFIS)." Water Resources Management, Vol. 27, pp. 3803-3823. https://doi.org/10.1007/s11269-013-0382-4
- Hochreiter, S., and Schmidhuber, J. (1997). "Long short-term memory." Neural Computation, Vol. 9, pp. 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
- 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, pp. 1207-1216. https://doi.org/10.3741/JKWRA.2018.51.12.1207
- Kisi, O., Shiri, J., and Nikoofar, B. (2012). "Forecasting daily lake levels using artificial intelligence approaches." Computers & Geosciences, Vol. 41, pp. 169-180. https://doi.org/10.1016/j.cageo.2011.08.027
- Kumar, D.N., Raju, K.S., and Sathish, T. (2004). "River flow forecasting using recurrent neural networks." Water Resources Management, Vol. 18, pp. 143-161. https://doi.org/10.1023/B:WARM.0000024727.94701.12
- Ministry of Environment (ME) (2018). Korean hydrological survey yearbook.
- Mok, J.-Y., Choi, J.-H., and Moon, Y.-I. (2020). "Prediction of multipurpose dam inflow using deep learning." Journal of Korea Water Resources Association, Vol. 53, pp. 97-105. https://doi.org/10.3741/JKWRA.2020.53.2.97
- Park, K., Jung, Y., Kim, K., and Park, S.K. (2020). "Determination of deep learning model and optimum length of training data in the river with large fluctuations in flow rates." Water, Vol. 12, No. 12, 3537.
- Park, K., Jung, Y., Seong, Y., and Lee, S. (2022). "Development of deep learning models to improve the accuracy of water levels time series prediction through multivariate hydrological data." Water, Vol. 14, No. 3, 469.
- Sung, J.Y., Lee J., Chung, I.-M., and Heo, J.-H. (2017). "Hourly water level forecasting at tributary affected by main river condition." Water, Vol. 9, No. 9, 644.
- Tran, Q.-K., and Song, S.-K. (2017). "Water level forecasting based on deep learning: A use case of Trinity River-Texas-the United States." Journal of KIISE, Vol. 44, pp. 607-612. https://doi.org/10.5626/JOK.2017.44.6.607
- Zhang, D., Lin, J., Peng, Q., Wang, D., Yang, T., Sorooshian, S., Liu, X., and Zhuang, J. (2018). "Modeling and simulating of reservoir operation using the artificial neural network, support vector regression, deep learning algorithm." Journal of Hydrology, Vol. 565, pp. 720-736. https://doi.org/10.1016/j.jhydrol.2018.08.050
- Zhang, D., Peng, Q., Lin, J., Wang, D., Liu, X., and Zhuang, J. (2019). "Simulating reservoir operation using a recurrent neural network algorithm." Water, Vol. 11, No.4, 865.