과제정보
본 결과물은 환경부의 재원으로 한국환경산업기술원의 물 관리연구사업의 지원을 받아 연구되었습니다(130747).
참고문헌
- Chang, F.J., Chiang, Y.M., and Chang, L.C. (2007). "Multi-step-ahead neural networks for flood forecasting." Hydrological Sciences Journal, Vol. 52, No. 1, pp. 114-130. https://doi.org/10.1623/hysj.52.1.114
- Chen, S.M., Wang, Y.M., and Tsou, I. (2013). "Using artificial neural network approach for modelling rainfall-runoff due to typhoon." Journal of Earth System Science, Vol. 122, No. 2, pp. 399-405. https://doi.org/10.1007/s12040-013-0289-8
- Chollet, F. (2017). Deep learning with Python. Manning, Shelter Island, NY, U.S.
- Cook, B.I., Mankin, J.S., Marvel, K., Williams, A.P., Smerdon, J.E., and Anchukaitis, K.J. (2020). "Twenty-first century drought projections in the CMIP6 forcing scenarios." Earth's Future, Vol. 8, No. 6, e2019EF001461.
- Cui, Z., Zhou, Y., Guo, S., Wang, J., Ba, H., and He, S. (2021). "A novel hybrid XAJ-LSTM model for multi-step-ahead flood forecasting." Hydrology Research. doi: 10.2166/nh.2021.016
- Dawson, C.W., and Wilby, R. (1998). "An artificial neural network approach to rainfall-runoff modelling." Hydrological Sciences Journal, Vol. 43, No. 1, pp.47-66. https://doi.org/10.1080/02626669809492102
- Devia, G.K., Ganasri, B.P., and Dwarakish, G.S. (2015). "A review on hydrological models." Aquatic Procedia, Vol. 4, pp. 1001-1007. https://doi.org/10.1016/j.aqpro.2015.02.126
- Eom, J.I., and Jung, K.S. (2019). "Estimation of Hourly Dam inflow using time series data." Journal of Korean Society of Hazard Mitiggation, Vol. 19, No. 2, pp. 163-168.
- Fuente, A., Meruane, V., and Meruane, C. (2019). "Hydrological early warning system based on a deep learning runoff model coupled with a meteorological forecast." Water, Vol. 11, No. 9, pp. 1808. https://doi.org/10.3390/w11091808
- Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep learning. MIT press, Cambridge, MA, U.S.
- Hsu, K.L., Gupta, H.V., and Sorooshian, S. (1995). "Artificial neural network modeling of the rainfall-runoff process." Water Resources Research, Vol. 31, No. 10, pp. 2517-2530. https://doi.org/10.1029/95WR01955
- Hu, C., Wu, Q., Li, H., Jian, S., Li, N., and Lou, Z. (2018). "Deep learning with a long short-term memory networks approach for rainfall-runoff simulation." Water, Vol. 10, No. 11, 1543. doi: 10.3390/w10111543
- Imrie, C.E., Durucan, S., and Korre, A. (2000). "River flow prediction using artificial neural networks: Generalisation beyond the calibration range." Journal of HYdrology, Vol. 233, No. 1-4, pp. 138-153. https://doi.org/10.1016/S0022-1694(00)00228-6
- Jain, A., and Kumar, A.M. (2007). "Hybrid neural network models for hydrologic time series forecasting." Applied Soft Computing, Vol. 7, No. 2, pp. 585-592. https://doi.org/10.1016/j.asoc.2006.03.002
- Jeong, D.M., and Bae, D.H, (2004), "Monthly Dam inflow forecasts by using weather forecasting information." Journal of Korea Water Resources Association, Vol. 37, No. 6, pp. 449-460. https://doi.org/10.3741/JKWRA.2004.37.6.449
- Kang, M.S., and Park, S.W. (2003). "Short-term flood forecasting using artificial neural networks." Journal of the Korean Society of Agricultural Engineers, Vol. 45, No. 2, pp. 45-57.
- Kao, I.F., Zhou, Y.L., 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. https://doi.org/10.1016/j.jhydrol.2020.124631
- Kim, J.H. (1993). A study on hydrologic forecasting of streamflows based on artificial neural network, Ph.D, dissertation. Inha University.
- Kim, S.W. (2000). "A study on the forecasting of daily streamflow using the multilayer neural networks model." Journal of Korea Water Resources Association, Vol. 33, No. 5, pp. 537-550.
- Kim, J.H., Jun, S.M., Hwang, S.H., Kim, H.K., Heo, J.M., and Kang M.S. (2021). "Impact of activation functions on flood forecasting model based on artificial neural networks" Journal of the Korean Society of Agricultural Engineers, Vol. 63, No. 1, pp. 11-25. https://doi.org/10.5389/KSAE.2021.63.1.011
- Kingma, D.P., and Ba, J. (2015). "Adam : A method for stochastic optimization." 3rd International Conference on Learning Representations 2015, San Diego, CA, U.S., arXiv:1412.6980.
- Kratzert, F., Klotz, D., Brenner, C., Schulz, K., and Herrnegger, M. (2018). "Rainfall-runoff modelling using long short-term memory (LSTM) networks." Hydrology and Earth System Sciences, Vol. 22, No. 11, pp. 6005-6022. https://doi.org/10.5194/hess-22-6005-2018
- Kumar, S., Tiwari, M.K., Chatterjee, C., and Mishra, A. (2015). "Reservoir inflow forecasting using ensemble models based on neural networks, wavelet analysis and bootstrap method." Water Resources Management, Vol. 29, No. 13, pp. 4863-4883. https://doi.org/10.1007/s11269-015-1095-7
- Lee, J.Y., Kim, H.I., and Han, K.Y. (2020). "Linkage of hydrological model and machine learning for real-time prediction of river flood." Journal of Korean Society of Civil Engineers, Vol. 40, No. 3, pp. 303-314. https://doi.org/10.12652/KSCE.2020.40.3.0303
- 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, No. 2, pp. 97-105. https://doi.org/10.3741/JKWRA.2020.53.2.97
- Mosavi, A., Ozturk, P., and Chau, K.W. (2018) "Flood prediction using machine learning models: Literature review." Water, Vol. 10, No. 11, 1536. https://doi.org/10.3390/w10111536
- Noori, N., and Kalin, L. (2016) "Coupling SWAT and ANN models for enhanced daily streamflow prediction." Journal of Hydrology, Vol. 533, pp. 141-151. https://doi.org/10.1016/j.jhydrol.2015.11.050
- Park, M.K., Yoon, Y.S., Lee, H.H., and Kim, J.H. (2018). "Application of recurrent neural network for inflow prediction into multi-purpose dam basin." Journal of Korea Water Resources Association, Vol. 51, No. 12, pp. 1217-1227.
- Sim, S.B., and Kim, M.S. (1998). "Flood inflow forecasting on multipurpose reservoir by neural network." Journal of Korea Water Resources Association, Vol. 31, No. 1, pp. 45-57.
- Sit, M., Demiray, B.Z., Xiang, Z., Ewing, G.J., Sermet, Y., and Demir, I. (2020). "A comprehensive review of deep learning applications in hydrology and water resources." Water Science and Technology, Vol. 82, No. 12, pp. 2635-2670. https://doi.org/10.2166/wst.2020.369
- Tabari, H. (2020). "Climate change impact on flood and extreme precipitation increases with water availability." Scientific Reports, Vol. 10, No. 1, pp. 1-10. https://doi.org/10.1038/s41598-019-56847-4
- Taieb, S.B., Sorjamaa, A., and Bontempi, G. (2010). "Multiple-output modeling for multi-step-ahead time series forecasting." Neurocomputing, Vol. 73, No. 10-12, pp. 1950-1957. https://doi.org/10.1016/j.neucom.2009.11.030
- Thomas, V., and Lopez, R. (2015) Global increase in climate-related disasters. ADB Economics Working Paper Series, No. 466, Asian Development Bank, Mandaluyong, Philippines.
- Toth, E., and Brath, A. (2007). "Multistep ahead streamflow forecasting: Role of calibration data in conceptual and neural network modeling." Water Resources Research, Vol. 43, No. 11.
- 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.
- Yaseen, Z.M., El-Shafie, A., Jaafar, O., Afan, H.A., and Sayl, K.N. (2015). "Artificial intelligence based models for stream-flow forecasting: 2000-2015." Journal of Hydrology, Vol. 530, pp. 829-844. https://doi.org/10.1016/j.jhydrol.2015.10.038
- Yoo, C.S., Hwang, J.H., and Kim, J.H. (2012). "Use of the extended Kalman Filter for the real-time quality improvement of runoff data: 1. Algorithm construction and application to one station." Journal of Korea Water Resources Association, Vol. 45, No. 7, pp. 697-711. https://doi.org/10.3741/JKWRA.2012.45.7.697