The Research of the Large Reservoirs Combined Forecasting and Scheduling System of Liaoning Province

Article Preview

Abstract:

The existing problems of large reservoir forecasting model In Liaoning Province are cumbersome and time-consuming, false report and not standard. This paper in view of the weak links, established a set of large reservoir flood forecasting and scheduling system of province administrated. Determining the research target, the function structure and business flow of the system were summarized. The key technology research was focused on the system platform construction, GIS, station control, model management, daily forecast, flood forecast and flood control etc. Research shows that: The system can provide fast, accurate forecasting and dispatching reference for 9 large reservoirs and realize the change from "distributed" forecast model to the "distributed and lumped coexist" forecast model.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

362-367

Citation:

Online since:

January 2015

Authors:

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2015 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] N. Miller J.Y. Zhang. Coupled Precipitation streamflow simulations at the GAME/HUBEX Site: XiXian Basin[J]. Journal of the Meteorological Society of Japan, 24(5): 985-998. ( 2001).

DOI: 10.2151/jmsj.79.985

Google Scholar

[2] Rajurkar M P, Kothyafi U C, Chaude U C. Modeling of the daily rainfall-runoff relationship with artificial neural network[J].J. Hydrol, 285: 96—1l3. (2004).

DOI: 10.1016/j.jhydrol.2003.08.011

Google Scholar

[3] Senarath US. On the calibration and verification of two dimensional, distributed, Hortonian, continuous watershed models[J]. Water Resources Research, 36(6): 1495-1510. ( 2000).

DOI: 10.1029/2000wr900039

Google Scholar

[4] Gautam M R, Watanabe K, SaegusaH. Runoi analysis in humid forest Catchment with artificial neural network[J]. Journal of Hydrology, (2): 33-35. ( 2000).

Google Scholar

[5] Imrie C E, Durucan S, Korre A. River flow prediction using artificial neural networks: generalization beyond the calibration range[J]. Journal of Hydrology, 233: 138-153. ( 2000).

DOI: 10.1016/s0022-1694(00)00228-6

Google Scholar

[6] Linda. See, Robert J Abrahant. Multi-model data fusion for hydrological forecasting[J]. Computer & Geosciences , 27(6): 987-994. ( 2001).

DOI: 10.1016/s0098-3004(00)00136-9

Google Scholar

[7] Tucci, Carlos E.M. a; Villanueva, Adolfo O.N. a . Flood control measures in União da Vitoria and Porto União: structural vs. non-structural measures. Urban Water, Vol. 1(6)177~182. ( 1999).

DOI: 10.1016/s1462-0758(00)00012-1

Google Scholar

[8] Zsuffa. Impact of Austrian hydropower plants on the flood control safety of the Hungarian Danube reach. Hydrological Sciences Journal . 44 (3 ). ( 1999).

DOI: 10.1080/02626669909492232

Google Scholar