Next Article in Journal
Correction: Hervás-Gámez, Carmen and Delgado-Ramos, Fernando. Are the Modern Drought Management Plans Modern Enough? The Guadalquivir River Basin Case in Spain. Water 2020, 12, 49
Previous Article in Journal
Influence of the Aggregate-Pouring Sequence on the Efficiency of Plugging Inundated Tunnels through Drilling Ground Boreholes
Open AccessArticle

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

1
Han River Flood Control Office, Ministry of Environment, 328 Dongjak-daero, Seocho-gu, Seoul 06501, Korea
2
Earth System Research Division, National Institute of Meteorological Research, 33 Seohobuk-ro, Seogwipo-si, Jeju 63568, Korea
3
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; https://doi.org/10.3390/w12102700
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
Show Figures

Figure 1

MDPI and ACS Style

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.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop