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Daily Reservoir Inflow Forecasting Combining Qpf Into Anns Model : Volume 6, Issue 1 (06/01/2009)

By Jun Zhang

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Book Id: WPLBN0003974262
Format Type: PDF Article :
File Size: Pages 30
Reproduction Date: 2015

Title: Daily Reservoir Inflow Forecasting Combining Qpf Into Anns Model : Volume 6, Issue 1 (06/01/2009)  
Author: Jun Zhang
Volume: Vol. 6, Issue 1
Language: English
Subject: Science, Hydrology, Earth
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Liao, S., Wu, X., Cheng, C., Zhang, J., & Shen, J. (2009). Daily Reservoir Inflow Forecasting Combining Qpf Into Anns Model : Volume 6, Issue 1 (06/01/2009). Retrieved from

Description: Dept. of Civil & Hydraulic Engineering, Dalian Univ. of Technology, Dalian 116024, China. Daily reservoir inflow predictions with lead-times of several days are essential to the operational planning and scheduling of hydroelectric power system. The demand for quantitative precipitation forecasting (QPF) is increasing in hydropower operation with the dramatic advances in the numerical weather prediction (NWP) models. This paper presents a simple and an effective algorithm for daily reservoir inflow predictions which solicits the observed precipitation, forecasted precipitation from QPF as predictors and discharges in following 1 to 6 days as predicted targets for multilayer perceptron artificial neural networks (MLP-ANNs) modeling. An improved error back-propagation algorithm with self-adaptive learning rate and self-adaptive momentum coefficient is used to make the supervised training procedure more efficient in both time saving and search optimization. Several commonly used error measures are employed to evaluate the performance of the proposed model and the results, compared with that of ARIMA model, show that the proposed model is capable of obtaining satisfactory forecasting not only in goodness of fit but also in generalization. Furthermore, the presented algorithm is integrated into a practical software system which has been severed for daily inflow predictions with lead-times varying from 1 to 6 days of more than twenty reservoirs operated by the Fujian Province Grid Company, China.

Daily reservoir inflow forecasting combining QPF into ANNs model

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