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2021 Vol.54, Issue 3 Preview Page

Research Article

March 2021. pp. 157-166
Abrahart, R., Kneale, P.E., and See, L.M. (2004). Neural networks for hydrological modeling. CRC Press, London, UK. 10.1201/9780203024119
Adeyemi, O., Grove, I., Peets, S., Domun, Y., and Norton, T. (2018). "Dynamic neural network modelling of soil moisture content for predictive irrigation scheduling." Sensors, Vol. 18, No. 10, pp. 3408-3429. 10.3390/s1810340830314346PMC6210977
Assem, H., Ghariba, S., Makrai, G., Johnston, P., Gill, L., and Pilla, F. (2017). "Urban water flow and water level prediction based on deep learning." Joint European conference on machine learning and knowledge discovery in databases. Springer, Skopje, Macedonia, Vol. 3, pp. 317-329. 10.1007/978-3-319-71273-4_26
Bae, Y., Kim, J., Wang, W., Yoo, Y., Jung, J., and Kim, H.S. (2019). "Monthly inflow forecasting of Soyang River dam using VARMA and machine learning models." Journal of Climate Research, Vol. 14, No. 3, pp. 183-198. (in Korean) 10.14383/cri.2019.14.3.183
Bang, Y., and Kim, S. (2018). "Development of initial design-width formulas for small streams: Case study in Western Gangwon province." Journal of the Korean Society of Hazard Mitigation, Vol. 18, No. 6, pp. 357-367. (in Korean) 10.9798/KOSHAM.2018.18.6.357
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. EPA, U.S.
Blöschl, G., Hall, J., Viglione, A., Perdigão. R.A., Parajka, J., Merz, B., Lun, D., Arheimer, B., Aronica, G.T., Bilibashi, A., Boháč, M., Bonacci, O., Borga, M., Čanjevac, I., Castellarin, A., Chirico, G.B., Claps, P., Frolova, N., Ganora, D., Gorbachova, L., Gül, A., Hannaford, J., Harrigan, S., Kireeva, M., Kiss, A., Kjeldsen, T.R., Kohnová, S., Koskela, J.J., Ledvinka, O., Macdonald, N., Mavrova-Guirguinova, M., Mediero, L., Merz, R., Molnar, P., Montanari, A., Murphy, C., Osuch, M., Ovcharuk, V., Radevski, I., Salinas, J.L., Sauquet, E., Šraj, M., Szolgay, J., Volpi, E., Wilson, D., Zaimi, K., and Živković, N. (2019). "Changing climate both increases and decreases European river floods." Nature, Vol. 573, No. 7772, pp. 108-111. 10.1038/s41586-019-1495-631462777
Chen, L., Singh, V.P., Lu, W., Zhang, J., Zhou, J., and Guo, S. (2016). "Streamflow forecast uncertainty evolution and its effect on real-time reservoir operation." Journal of Hydrology, Vol. 540, pp. 712-726. 10.1016/j.jhydrol.2016.06.015
Chen, P.A., Chang, L.C., and Chang, F.J. (2013). "Reinforced recurrent neural networks for multi-step-ahead flood forecasts." Journal of Hydrology, Vol. 497, pp. 71-79. 10.1016/j.jhydrol.2013.05.038
Cho, K., Van Merriënboer, B., Bahdanau, D., and Bengio, Y. (2014a). "On the properties of neural machine translation: Encoder-decoder approaches." arXiv preprint, arXiv:1409.1259v2 [cs.CL]. 10.3115/v1/W14-4012
Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014b). "Learning phrase representations using RNN encoder-decoder for statistical machine translation." arXiv preprint, arXiv:1406.1078v3 [cs.CL]. 10.3115/v1/D14-1179
Choi, C., Kim, J., Han, H., Han, D., and Kim, H.S. (2020). "Development of water level prediction models using machine learning in wetlands: A case study of Upo Wetland in South Korea." Water, Vol. 12, No. 1, pp. 93-110. 10.3390/w12010093
Devia, G.K., Ganasri, B.P., and Dwarakish, G.S. (2015). "A review on hydrological models." Aquatic Procedia, Vol. 4, pp. 1001-1007. 10.1016/j.aqpro.2015.02.126
Fan, H., Jiang, M., Xu, L., Zhu, H., Cheng, J., and Jiang, J. (2020). "Comparison of long short term memory networks and the hydrological model in runoff simulation." Water, Vol. 12, No. 1, pp. 175-189. 10.3390/w12010175
Feng, D., Fang, K., and Shen, C. (2020). "Enhancing streamflow forecast and extracting insights using long‐short term memory networks with data integration at continental scales." Water Resources Research, Vol. 56, No. 9, e2019WR026793. 10.1029/2019WR026793
Gholami, V.C.K.W., Chau, K.W., Fadaee, F., Torkaman, J., and Ghaffari, A. (2015). "Modeling of groundwater level fluctuations using dendrochronology in alluvial aquifers." Journal of Hydrology, Vol. 529, pp. 1060-1069. 10.1016/j.jhydrol.2015.09.028
Ghumman, A.R., Ghazaw, Y.M., Sohail, A.R., and Watanabe, K. (2011). "Runoff forecasting by artificial neural network and conventional model." Alexandria Engineering Journal, Vol. 50, No. 4, pp. 345-350. 10.1016/j.aej.2012.01.005
Granata, F., Gargano, R., and De Marinis, G. (2016). "Support vector regression for rainfall-runoff modeling in urban drainage: A comparison with the EPA's storm water management model." Water, Vol. 8, No. 3, pp. 69-81. 10.3390/w8030069
Han, J., Lee, D., Kang, B., Chung, S., Jang, W., Lim, K., and Kim, J. (2017). "Potential impacts of future extreme storm events on streamflow and sediment in Soyang-dam watershed." Journal of Korean Society on Water Environment, Vol. 33, No. 2, pp. 160-169. (in Korean)
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, pp. 1543-1558. 10.3390/w10111543
Jeong, E., Cho, H., and Koo, B. (2018). "Analysis of the impact of droughts on river flows in an agricultural watershed using a semi-distributed watershed model STREAM." Journal of the Korean Society for Marine Environment & Energy, Vol. 21, No. 4, pp. 398-410. (in Korean) 10.7846/JKOSMEE.2018.21.4.398
Jung, S.H., Lee, D.E., and Lee, K.S. (2018). "Prediction of river water level using deep-learning open library." Journal of the Korean Society of Hazard Mitigation, Vol. 18, No. 1, pp. 1-11. (in Korean) 10.9798/KOSHAM.2018.18.1.1
Kang, N.R., Noh, H.S., Lee, J.S., Lim, S.H., and Kim, H.S. (2013). "Runoff simulation of an urban drainage system using radar rainfall data." Journal of Wetlands Research, Vol. 15, No. 3, pp. 413-422. (in Korean) 10.17663/JWR.2013.15.3.413
Kim, B.K., Kim, S.D., 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, Vol. 27, No. 6B, pp. 661-668. (in Korean)
Kim, D., Kim, J., Kwak, J., Necesito, I.V., Kim, J., and Kim, H.S. (2020). "Development of water level prediction models using deep neural network in mountain wetlands." Journal of Wetlands Research, Vol. 22, No. 2, pp. 106-112. (in Korean)
Kim, K.S. (2010). A study on the real time forecasting for monthly inflow Daecheong dam using hydrologic time series analyses. Master Thesis, Seokyeong University, pp. 32-54. (in Korean)
Kim, Y., Seo, S., and Kim, Y. (2018). "Development of a hybrid regionalization model for estimation of hydrological model parameters for ungauged watersheds." Journal of Korea Water Resources Association, Vol. 51, No. 8, pp. 677-686. (in Korean)
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. 10.5194/hess-22-6005-2018
Lee, G., Jung, S., and Lee, D. (2018). "Comparison of physics-based and data-driven models for streamflow simulation of the Mekong River." Journal of Korea Water Resources Association, Vol. 51, No. 6, pp. 503-514.
Lee, K., Choi, C., Shin, D.H., and Kim, H.S. (2020). "Prediction of heavy rain damage using deep learning." Water, Vol. 12, No. 7, pp. 1942-1959. 10.3390/w12071942
Legates, D.R., and McCabe, 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. 10.1029/1998WR900018
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. (in Korean)
Montanari, A., Rosso, R., and Taqqu, M.S. (1997). "Fractionally differenced ARIMA models applied to hydrologic time series: Identification, estimation, and simulation." Water Resources Research, Vol. 33, No. 5, pp. 1035-1044. 10.1029/97WR00043
Moriasi, D.N., Arnold, J.G., Van Liew, M.W., Bingner, R.L., Harmel, R. D., and Veith, T.L. (2007). "Model evaluation guidelines for systematic quantification of accuracy in watershed simulations." Transactions of the ASABE, Vol. 50, No. 3, pp. 885-900. 10.13031/2013.23153
Mosavi, A., Ozturk, P., and Chau, K.W. (2018). "Flood prediction using machine learning models: Literature review." Water, Vol. 10, No. 11, p. 1536. 10.3390/w10111536
Neitsch, S.L., Arnold, J.G., Kiniry, J.R., and Williams, J.R. (2011). Soil and water assessment tool theoretical documentation version 2009. Texas Water Resources Institute, Temple, TX.
Noh, H., Lee, J., Kang, N., Lee, D., Kim, H. S., and Kim, S. (2016). "Long-term simulation of daily streamflow using radar rainfall and the SWAT model: A case study of the Gamcheon basin of the Nakdong River, Korea." Advances in Meteorology, Vol. 2016, pp. 431-442. 10.1155/2016/2485251
Palmer, M., and Ruhi, A. (2019). "Linkages between flow regime, biota, and ecosystem processes: Implications for river restoration." Science, Vol. 365, No. 6459, eaaw2087. 10.1126/science.aaw208731604208
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. (in Korean)
Riad, S., Mania, J., Bouchaou, L., and Najjar, Y. (2004). "Predicting catchment flow in a semi‐arid region via an artificial neural network technique." Hydrological Processes, Vol. 18, No. 13, pp. 2387-2393. 10.1002/hyp.1469
Shoaib, M., Shamseldin, A.Y., Melville, B.W., and Khan, M.M. (2016). "A comparison between wavelet based static and dynamic neural network approaches for runoff prediction." Journal of Hydrology, Vol. 535, pp. 211-225. 10.1016/j.jhydrol.2016.01.076
Sutskever, I., Vinyals, O., and Le, Q.V. (2014). "Sequence to sequence learning with neural networks." arXiv preprint, arXiv:1409. 3215v3 [cs.CL].
Tian, Y., Xu, Y.P., Yang, Z., Wang, G., and Zhu, Q. (2018). "Integration of a parsimonious hydrological model with recurrent neural networks for improved streamflow forecasting." Water, Vol. 10, No. 11, pp. 1655. 10.3390/w10111655
Wilcox, B.P., Rawls, W.J., Brakensiek, D.L., and Ross Wight, J. (1990). "Predicting runoff from rangeland catchments: A comparison of two models." Water Resources Research, Vol. 26, No. 10, pp. 2401-2410. 10.1029/WR026i010p02401
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. 10.1029/2019WR025326
Xu, J., Luo, W., and Huang, Y. (2019). "Dadu River runoff forecasting via Seq2Seq." Proceedings of the 2019 International Conference on Artificial Intelligence and Computer Science, Wuhan, China, pp. 494-498. 10.1145/3349341.3349457
Yan, J., Jin, J., Chen, F., Yu, G., Yin, H., and Wang, W. (2018). "Urban flash flood forecast using support vector machine and numerical simulation." Journal of Hydroinformatics, Vol. 20, No. 1, pp. 221-231. 10.2166/hydro.2017.175
Zhang, J., 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 areas." Journal of Hydrology, Vol. 561, pp. 918-929. 10.1016/j.jhydrol.2018.04.065
Zhang, Q., Xiao, M., Singh, V.P., and Li, J. (2012). "Regionalization and spatial changing properties of droughts across the Pearl River basin, China." Journal of Hydrology, Vol. 472, pp. 355-366. 10.1016/j.jhydrol.2012.09.054
  • Publisher(Ko) :한국수자원학회
  • Journal Title :Journal of Korea Water Resources Association
  • Journal Title(Ko) :한국수자원학회 논문집
  • Volume : 54
  • No :3
  • Pages :157-166
  • Received Date :2020. 12. 07
  • Revised Date :2021. 01. 19
  • Accepted Date : 2021. 01. 19