Acknowledgement
본 연구는 1) 한국수력원자력(주) 「발전용댐 이·치수 능력검토 및 수문학적 안정성 평가 용역」의 지원과 2) 환경부 「기후변화특성화대학원사업」의 지원으로 수행되었습니다. 이에 감사드립니다.
References
- 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. https://doi.org/10.1016/j.advengsoft.2011.09.018
- Choi, J., Jeong, G., Kang, D., Ahn, J., and Kim, T. (2021). "Classification of hydropower dam in North-han River based on water storage characteristics." Journal of Korea Water Resources Association, Vol. 54, No. 8, pp. 567-576. https://doi.org/10.3741/JKWRA.2021.54.8.567
- Hochreiter, S., and Schmidhuber, J. (1997). "Long short-term memory." Neural Computation, Vol. 9, No. 8, pp. 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
- Ivanov, V.Y., Bras, R.L., and Curtis, D.C. (2007). "A weather generator for hydrological, ecological, and agricultural applications." Water Resources Research, Vol. 43, No. 10, W10406. https://doi.org/10.1029/2006WR005364
- Luo, B., Fang, Y., Wang, H., and Zang, D. (2020). "Reservoir inflow prediction using a hybrid model based on deep learning." IOP Conference Series: Materials Science and Engineering, IOP Publishing, Shanghai, China, Vol. 715, No. 1, 012044.
- Ministry of Construction and Traffic (MOCT) and K-water (1997). Reevaluation of existing dams (Han river basin).
- Ministry of Construction and Traffic (MOCT) and K-water (2010). Reevaluation of existing dams and optimum allocation of the capacity.
- Ministry of Construction and Traffic (MOCT) and K-water (2011). The national river basin investigation 2011.
- Ministry of Land, Infrastructure and Transport (MOLIT) and K-water (2016). Efficient utilization of existing water resources.
- 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 Korean Institute of Information Scientists and Engineers, Vol. 44, No. 6, pp. 607-612.
- Tran, T.D., Tran, V.N., and Kim, J. (2021). "Improving the accuracy of dam inflow predictions using a long short-term memory network coupled with wavelet transform and predictor selection." Mathematics, Vol. 9, No. 5, 551. https://doi.org/10.3390/math9050551
- US. Army Corps of Engineers (USACE) (2021). HEC-ResSim: Reservoir system simulation user's manual version 3.3. US Army Corps of Engineers Institute for Water Resources Hydrologic Engineering Center (HEC). CPD-82, Davis, CA, U.S.
- Yeo, W.K, Seo, Y.M, Lee, S.Y, and Jee, H.K (2010) "Study on water stage prediction using hybrid model of artificial neural network and genetic algorithm." Journal of Korea Water Resources Association, Vol. 43, No. 8, pp. 721-731. https://doi.org/10.3741/JKWRA.2010.43.8.721
- Zhang, Z., Zhu, Y., Zhang, X., Ye, M., and Yang, J. (2018). "Developing a Long Short-Term Memory (LSTM) based model for predicting water table depth in agricultural area." Journal of Hydrology, Vol. 561, pp. 918-929. https://doi.org/10.1016/j.jhydrol.2018.04.065