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http://dx.doi.org/10.3741/JKWRA.2021.54.S-1.1183

Estimation of regional flow duration curve applicable to ungauged areas using machine learning technique  

Jeung, Se Jin (Kangwon Institute of Inclusive Technology, Kangwon National University)
Lee, Seung Pil (H.L.Construction co., Ltd)
Kim, Byung Sik (Department of Urban Environment & Disaster Management, Kangwon National University)
Publication Information
Journal of Korea Water Resources Association / v.54, no.spc1, 2021 , pp. 1183-1193 More about this Journal
Abstract
Low flow affects various fields such as river water supply management and planning, and irrigation water. A sufficient period of flow data is required to calculate the Flow Duration Curve. However, in order to calculate the Flow Duration Curve, it is essential to secure flow data for more than 30 years. However, in the case of rivers below the national river unit, there is no long-term flow data or there are observed data missing for a certain period in the middle, so there is a limit to calculating the Flow Duration Curve for each river. In the past, statistical-based methods such as Multiple Regression Analysis and ARIMA models were used to predict sulfur in the unmeasured watershed, but recently, the demand for machine learning and deep learning models is increasing. Therefore, in this study, we present the DNN technique, which is a machine learning technique that fits the latest paradigm. The DNN technique is a method that compensates for the shortcomings of the ANN technique, such as difficult to find optimal parameter values in the learning process and slow learning time. Therefore, in this study, the Flow Duration Curve applicable to the unmeasured watershed is calculated using the DNN model. First, the factors affecting the Flow Duration Curve were collected and statistically significant variables were selected through multicollinearity analysis between the factors, and input data were built into the machine learning model. The effectiveness of machine learning techniques was reviewed through statistical verification.
Keywords
DNN; Machine learning; Flow duration curve (FDC); Ungauged; Watershed characteristic factor;
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1 Kim, D.H., Kim, J.W., Kwak, J., and 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.   DOI
2 Byeon, S.J., Lee, S.H., Choi, G.W., and Jung, J.G. (2014). "Use ofgauged water level and precipitation data to predict short termwater level changes." Korean Review of Crisis and Emergency Management, Vol.10, pp. 247-264.
3 Castillo, J.M.M., Cspedes, J.M.S., and Cuchango, H.E.E. (2018). "Water level prediction using artificial neural network model." International Journal of Applied Engineering Research, Vol. 13, pp. 14378-14381.
4 Choi, S.Y., Han, K.Y., and Kim, B.H. (2012). "Comparison ofdifferent multiple linear regression models for real-time floodstage forecasting." Journal of the Korean Society of Civil Engineers B, Vol. 32, No. 1, pp. 9-20.
5 Jeung, S.J. (2019). Impact assessments of climate and hydrologicalcycle changes in North Korea based on RCP climate changescenarios. Ph.D dissertation, Kangwon National University.
6 Jun, H.D., and Lee, J.H. (2013). "A methodology for floodforecasting and warning based on the characteristic of observedwater levels between up stream and downstream." Journal of the Korean Society of Hazard Mitigation, Vol. 13, pp. 367-374.   DOI
7 Lee, T.H. (2016). Development of regional regression model forestimating mean low flow in ungauged basins. Ph.D dissertation, Ajou University.
8 Lim, G.G. (2020). A study on estimation of lowflow indices inungauged basin using multiple regression. Master's thesis, Kangwon National University.
9 Bengio, Y., Courville, A., and Vincent, P. (2013). "Representation learning: A review and new perspectives." Cornell University, special issue Learning Deep Architectures, Accessed 24 June, .
10 Matsumoto, W., Hagiwara, M., Boufounos, P., Fukushima K., Mariyama, T., and Xiongxin, Z. (2016). "A deep neural network architecture usingdimensionality reduction with sparse matrices." ICONIP 2016: Neural Information Processing, International Conference on Neural Information Prcessing, Kyoto, Japan, pp 397-404.
11 Park, S.D., (2003) "Dimensionless flow duration curve in natural river." Journal of Korea Water Resources Association, Vol. 36, No. 1, pp. 33-44.   DOI
12 Rezaeianzadeh, M., Kalin, L., and Anderson, C. (2015). "Wetland-water-level prediction using ANN in conjunction with baseflowrecession analysis." Journal of Hydrologic Engineering, Vol. 22, pp. 1-11.
13 Rezaeianzadeh, M., Kalin, L., and Hantush, M. (2018). "An integrated approach for modeling wetland water level: Applicationto a headwater wetland in Coastal Alabama, USA." Water, Vol. 10, pp. 1-17.   DOI
14 Ryoo, K.S., and Chong, K.Y. (2008). "Development of multiple regression models for the prediction of reservoir inflow in the floodseason." 2008 Conference of Korean Society of Civil Engineers, pp. 3500-3503.
15 Tiwari, M.K., and Chatterjee, C. (2010). "Development of an accurateand reliable hourly flood forecasting model using Wavelet-Bootstrap-ANN (WBANN) hybrid approach." Journal of Hydrology, Vol. 394, pp. 458-470.   DOI
16 Yoon, Y.N. (2007). Hydrology, Cheongmungak.