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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
Historic
Publication Date:
2009
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 http://hawaiilibrary.net/


Description
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.

Summary
Daily reservoir inflow forecasting combining QPF into ANNs model

Excerpt
ASCE Task Committee: Artificial neural networks in hydrology. II: Hydrologic applications, J. Hydrol. Eng., 5, 124–137, 2000.; Aqil,~M., Kita,~I., Yano,~A., and Nishiyama,~S.: A~comparative study of artificial neural networks and neuro-fuzzy in continuous modeling of the daily and hourly behaviour of runoff, J. Hydrol., 337, 22–34, 2007.; Atiya,~A F., El-Shoura,~S M., Shaheen,~S I., and El-Sherif,~M S.: A~comparison between neural-network forecasting techniques – case study: river flow forecasting, IEEE T. Neural Networ., 10, 402–409, 1999.; Birikundavyi,~S., Labib,~R., Trung,~H T., and Rousselle,~J.: Performance of neural networks in daily streamflow forecasting, J. Hydrol. Eng., 7, 392–398, 2002.; Brath, A., Montanari, A., and Toth, E.: Neural networks and non-parametric methods for improving real-time flood forecasting through conceptual hydrological models, Hydrol. Earth Syst. Sci., 6, 627–639, 2002.; Campolo,~M., Soldati,~A., and Andreussi,~P.: Artificial neural network approach to flood forecasting in the river Arno, Hydrolog. Sci. J., 48, 381–398, 2003.; Chau,~K W.: Particle swarm optimization training algorithm for ANNs in stage prediction of Shing Mun river, J. Hydrol., 329, 363–367, 2006.; Chau,~K W.: A~split-step particle swarm optimization algorithm in river stage forecasting, J. Hydrol., 346, 131–135, 2007.; Cheng,~C T. and Chau,~K W.: Flood control management system for reservoirs, Environ. Modell. Softw., 19, 1141–1150, 2004.; Cheng,~C T., Chau,~K W., Li,~X Y., and Li,~G.: Developing a~Web-based flood forecasting system for reservoirs with J2EE, Hydrolog. Sci. J., 49, 973–986, 2004.; Collier,~C G. and Kzyzysztofowicz,~R.: Quantitative precipitation forecasting, J. Hydrol., 239, 1–2, 2000.; Collischonn,~W., Haas,~R., Andreolli,~I., and Tucci,~C E M.: Forecasting river Uruguay flow using rainfall forecasts from a~regional weather-prediction model, J. Hydrol., 305, 87–98, 2005.; Collischonn,~W., Morelli Tucci,~C E., Clarke,~R T., Chou,~S C., Guilhon, L G., Cataldi,~M., and Allasia,~D.: Medium-range reservoir inflow predictions based on quantitative precipitation forecasts, J. Hydrol., 344, 112–122, 2007.; Coulibaly,~P., Anctil,~F., and Bobée,~B.: Daily reservoir inflow forecasting using artificial neural networks with stopped training approach, J. Hydrol., 230, 244–257, 2000.; Dawson,~C W. and Wilby,~R L.: Hydrological modelling using artificial neural networks, Prog. Phys. Geog., 25, 80–108, 2001.; Dawson,~C W., Abrahart,~R J., Shamseldin,~A Y., and Wilby,~R L.: Flood estimation at ungauged sites using artificial neural networks, J. Hydrol., 319, 391–409, 2006.; Dawson,~C W., Abrahart,~R J., and See,~L M.: HydroTest: a~web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts, Environ. Modell. Softw., 22, 1034–1052, 2007.; Dibike,~Y B. and Solomatine,~D P.: River flow forecasting using artificial neural networks, Phys. Chem. Earth. Pt. B, 26, 1–7, 2001.; El-Din,~A G. and Smith,~D W.: A~neural network model to predict the wastewater inflow incorporating rainfall events, Water Res., 36, 1115–1126, 2002.; De Roo,~A P J., Bartholmes,~J., Bates,~P D., Beven,~K., Bongioannini-Cerlini,~B., Gouweleeuw,~B., Heise,~E., Hils,~M., Hollingsworth,~M., Holst,~B., Horritt,~M., Hunter,~N., Kwadijk,~J., Pappenburger,~F., Reggiani,~P., Rivin,~G., Sattler,~K., Sprokkereef,~E., Thielen,~J., Todini,~E., and Van Dijk,~M.: Development of a~European flood forecasting system, Int. J. River Basin Manag., 1, 49–59, 2003.; Firat, M.: Comparison of Artificial Intelligence Techniques for river flow forecasting, Hydrol. Earth Syst. Sci., 12, 123–139, 2008.; Habets,~F., LeMoigne,~P., and Noilhan,~J.: On the utility of operational precipitation forecasts to served as input for streamflow forecasting, J. Hydrol., 293, 270–288, 2004.; Hu,~T S., Lam,~K C., and Ng,~S T.: A~modified neural network for improving river flow prediction, Hydrolog. Sci. J., 50, 299–318, 2005.; Irvine,~K N. and Eberhardt,~A J.: Multip

 

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