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Water 2017, 9(9), 644; doi:10.3390/w9090644

Hourly Water Level Forecasting at Tributary Affected by Main River Condition

Han River Flood Control Office, Ministry of Land, Infrastructure and Transport (MOLIT), 328 Dongjak-daero, Seocho-Gu, Seoul 06501, Korea
School of Civil & Environmental Engineering, College of Engineering, Yonsei University, 50 Yonsei-ro Seodaemun-Gu, Seoul 03722, Korea
Hydro Science and Engineering Research Institute, Korea Institute of Civil Engineering and Building Technology (KICT), 283 Goyang-daero, Ilsanseo-Gu, Goyang-Si 10223, Gyeonggi-Do, Korea
Author to whom correspondence should be addressed.
Received: 18 August 2017 / Revised: 18 August 2017 / Accepted: 21 August 2017 / Published: 28 August 2017
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This study develops hourly water level forecasting models with lead-times of 1 to 3 h using an artificial neural network (ANN) for Anyangcheon stream, one of the major tributaries of the Han River, South Korea. To consider the backwater effect from this river, an enhanced tributary water level forecasting model is proposed by adding multiple water level data on the main river as input variables into the conventional ANN structure which often uses rainfall and upstream water level data. Four types of ANN models per each lead-time are built with increasing complexity of the input vector, and their performances are compared. The results indicate that the inclusion of multiple water level data on the main river to the network provides water level forecasts with greater accuracy at the Ogeumgyo gauging station of interest. The final best ANN models for water level forecasts with lead-times of 1 to 2 h show good performance with root mean square errors (RMSE) below 0.06 m and 0.12 m, respectively. However, the final best ANN model for forecasting 3 h ahead was unsatisfactory, showing underestimation at many rising parts of the hydrograph. View Full-Text
Keywords: tributary; hourly water level forecasting; artificial neural network tributary; hourly water level forecasting; artificial neural network

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Sung, J.Y.; Lee, J.; Chung, I.-M.; Heo, J.-H. Hourly Water Level Forecasting at Tributary Affected by Main River Condition. Water 2017, 9, 644.

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