Abstract
A real-time forecasting method coupled with the 1-D unsteady flow model with the recursive least-square method was developed. The 1-D unsteady flow model was modified by using the time-variant parameter and revising it dynamically through introducing a variable weighted forgetting factor, such that the output of the model could be adjusted for the real time forecasting of floods. The application of the new real time forecasting model in the reach from Yichang to Luoshan of the Yangtze River was demonstrated. Computational result shows that the forecasting accuracy of the new model is much higher than that of the original 1-D unsteady flow model. The method developed is effective for flood forecasting, and can be used for practical operation in the flood forecasting.
Similar content being viewed by others
Refferences
LIN San-yi. Hydrologic forecast[M]. Beijing: China Press of Water Coservancy and Electric Power, 2001 (in Chinese).
ZHANG Jian-yun, ZHANG Rui-fang. Development of the real-time hydrological forecasting and information display system [J]. Advances in Water Science, 1996, 7(3): 214–220. (in Chinese).
SONG Xing-yuan. Research on real-time rainfall-runoff forecasting by improved time variant gain mode[J]. Journal of Wuhan University (Natural Science), 2002, 35(2):1–4 (in Chinese).
KANG Ling, WANG Cheng, JIANG Tie-bing. A new genetic simulated annealing algorithm for flood routing model[J]. Journal of Hydrodynamics, Ser. B, 2004, 16(2): 233–239.
JIANG Tie-bing, SHU Chang. River flow routing using Armax model and multilayer Bp neural networks[J]. Journal of Hydrodynamics, Ser. A, 1999, 14(2):247–252 (in Chinese).
ZHU Q. M. and WARWICK K. A neural network enhanced generalized minimum variance self-turning proportional, integral and derivative control algorithm for complex dynamic systems[J]. Proc. Instn. Mech. Engrs. Part I: Systems and Control Engineering, 2002, 215 (3): 262–273.
ZHANG Xiao-feng, YUAN Jing. Real-time forecasting method of Ann model based on forgetting factor[J]. Advances in Water Science, 2004, 15(6): 787–792. (in Chinese).
QU Si-min. Comprehensive correction of real-time flood forecasting[J]. Advances in Water Science, 2003, 14 (2): 167–171. (in Chinese).
WANG Jing-quan. Study on real-time water level forecasting based on Kalman half adaptive filtering[J]. Yangtze River, 2000, 31(1): 15–18. (in Chinese).
LI Zhi-jia. Real-time flood forecasting modeling of 1D unsteady channel flow and Kalman filter[J]. Journal of Hydrodynamics, Ser. B, 2001, 13(1): 64–69.
XU Zu-xin, YIN Hai-long. 2D real-time modeling of tidal flow in Huangpu river’s mainstream[J]. Journal of Hydrodynamics, Ser. A, 2003, 18(3): 372–378. (in Chinese).
ZHANG Xiao-feng, NAKAGA WA Ha-jime, XU Quan-xi. On accuracy of first order upwind scheme[J]. Journal of Wuhan University (Natural Science), 2001, 33 (1): 6–7. (in Chinese).
LIU Bao, WANG Zhen-gou. System identification[M]. Beijin: China Machine Press, 1993 (in Chinese).
FORTESCUE T. R. et al. Implementation of self-turning regulalors with variable forgetting factors[J]. Automatica, 1981, 17(6): 831–835.
Author information
Authors and Affiliations
Corresponding author
Additional information
Biography: MU Jin-bin (1980-),Male, Ph. D. Student
Rights and permissions
About this article
Cite this article
Mu, Jb., Zhang, Xf. Real-Time Flood Forecasting Method With 1-D Unsteady Flow Model. J Hydrodyn 19, 150–154 (2007). https://doi.org/10.1016/S1001-6058(07)60041-9
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1016/S1001-6058(07)60041-9