COMPLEMENT METHOD OF MISSING OBSERVATION FLOW DATA BY MEANS OF DEEP LEARNING METHOD

Streamflow data are important for river maintenance, water resources planning, and flood forecasting. However, the flow data may be missing due to various reasons. Therefore, this study proposed a novel method using deep learning to complement missing data of flow discharge time series. This study u...

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Veröffentlicht in:	Doboku Gakkai Ronbunshu. B1, Suikogaku = Journal of Japan Society of Civil Engineers. Ser. B1, Hydraulic Engineering Ser. B1 (Hydraulic Engineering), 2021, Vol.77(2), pp.I_1243-I_1248
Hauptverfasser:	NAGASATO, Takeyoshi, ISHIDA, Kei, YOKOO, Kazuki, SAKAGUCHI, Daiju, KIYAMA, Masato, AMAGASAKI, Motoki
Format:	Artikel
Sprache:	eng ; jpn
Schlagworte:	Convolutional neural network Deep learning Discharge Flood forecasting Flow rates Flow velocity Hydrologic data Machine learning Missing data Missing data complementation Multilayer perceptron Precipitation River discharge Stream discharge Stream flow Teaching methods Water resources
Online-Zugang:	Volltext
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