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Estimation of Water-Use Rates Based on Hydro-Meteorological Variables Using Deep Belief Network

Han River Flood Control Office, Ministry of Environment, 328 Dongjak-daero, Seocho-gu, Seoul 06501, Korea
Earth System Research Division, National Institute of Meteorological Research, 33 Seohobuk-ro, Seogwipo-si, Jeju 63568, Korea
Department of Civil Engineering, Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul 01811, Korea
Authors to whom correspondence should be addressed.
Water 2020, 12(10), 2700;
Received: 30 August 2020 / Revised: 20 September 2020 / Accepted: 24 September 2020 / Published: 27 September 2020
(This article belongs to the Section Hydrology and Hydrogeology)
This study proposed a deep learning-based model to estimate stream water-use rate (WUR) using precipitation (P) and potential evapotranspiration (PET). Correlations were explored to identify relationships among accumulated meteorological variables for various time durations (three-, four-, five-, and six-month cumulative) and WUR, which revealed that three-month cumulative meteorological variables and WUR were highly correlated. A deep belief network (DBN) based on iterating parameter tuning was developed to estimate WUR using P, PET, and antecedent stream water-use rate (DWUR). The training and validation periods were 2011–2016, and 2017–2019, respectively. The results showed that the PET-DWUR based model provided better performances in Nash–Sutcliff efficiency (NSE), root mean square error (RMSE), and determination coefficient (R2) than the P-PET-DWUR and P-DWUR models. The framework in this study can provide a forecast model for deficiencies of stream water use coupled with a weather forecast model. View Full-Text
Keywords: stream water-use rate; precipitation; PET; deep belief network stream water-use rate; precipitation; PET; deep belief network
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Sung, J.H.; Ryu, Y.; Chung, E.-S. Estimation of Water-Use Rates Based on Hydro-Meteorological Variables Using Deep Belief Network. Water 2020, 12, 2700.

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