Next Article in Journal
Statistical Analysis of the Spatiotemporal Distribution of Ozone Induced by Cut-Off Lows in the Upper Troposphere and Lower Stratosphere over Northeast Asia
Previous Article in Journal
Evaluating Forecast Skills of Moisture from Convective-Permitting WRF-ARW Model during 2017 North American Monsoon Season
Previous Article in Special Issue
Extreme Drought Events over Brazil from 2011 to 2019
Open AccessArticle

Estimation of Low-Flow in South Korean River Basins Using a Canonical Correlation Analysis and Neural Network (CCA-NN) Based Regional Frequency Analysis

1
Department of Civil and Environmental Engineering, Konkuk University, Seoul 05028, Korea
2
Department of Civil and Environmental Engineering, Dankook University, Gyeonggi-do 16890, Korea
*
Author to whom correspondence should be addressed.
Atmosphere 2019, 10(11), 695; https://doi.org/10.3390/atmos10110695
Received: 25 October 2019 / Revised: 5 November 2019 / Accepted: 7 November 2019 / Published: 11 November 2019
(This article belongs to the Special Issue Meteorological and Hydrological Droughts)
Low-flow quantiles at ungauged locations are generally estimated based on hydrological methods, such as the drainage area ratio and frequency analysis methods. In practice, the drainage area ratio approach is a popular but simple linear model. When hydrologically nonlinear characteristics govern the runoff process, the linear approach leads to significant bias. This study was conducted to develop an improved nonlinear approach using a canonical correlation analysis and neural network (CCA-NN)-based regional frequency analysis (RFA) for low-flow estimation. The jackknife technique was utilized to validate the two methods. The approaches were applied to 33 river basins in South Korea. In this work, we focused on two-year and five-year return periods. For the two-year return period, the BIAS, RMSE, and R2 were 0.013, 0.511, and 0.408 with the RFA, respectively, and −0.042, 1.042, and 0.114 with the drainage area ratio method, respectively; whereas for the five-year return period, the respective indices were −0.018, 0.316, and 0.573 with RFA, respectively, and 0.166, 0.536, and 0.044 with the drainage area ratio method, respectively. RFA outperformed the drainage area ratio method based on its high prediction accuracy and ability to avoid the bias problem. This study indicates that machine learning-based nonlinear techniques have the potential for use in estimating reliable low-flows at ungauged sites. View Full-Text
Keywords: low-flow quantiles; regional frequency analysis; drainage area ratio; canonical correlation analysis; neural networks low-flow quantiles; regional frequency analysis; drainage area ratio; canonical correlation analysis; neural networks
Show Figures

Figure 1

MDPI and ACS Style

Jung, K.; Kim, E.; Kang, B. Estimation of Low-Flow in South Korean River Basins Using a Canonical Correlation Analysis and Neural Network (CCA-NN) Based Regional Frequency Analysis. Atmosphere 2019, 10, 695.

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
Back to TopTop