Comparison of Several Flood Forecasting Models in Yangtze River
Publication: Journal of Hydrologic Engineering
Volume 10, Issue 6
Abstract
In a flood-prone region, quick and accurate flood forecasting is imperative. It can extend the lead time for issuing disaster warnings and allow sufficient time for habitants in hazardous areas to take appropriate action, such as evacuation. In this paper, two hybrid models based on recent artificial intelligence technology, namely, the genetic algorithm-based artificial neural network (ANN-GA) and the adaptive-network-based fuzzy inference system (ANFIS), are employed for flood forecasting in a channel reach of the Yangtze River in China. An empirical linear regression model is used as the benchmark for comparison of their performances. Water levels at a downstream station, Han-Kou, are forecasted by using known water levels at the upstream station, Luo-Shan. When cautious treatment is made to avoid overfitting, both hybrid algorithms produce better accuracy in performance than the linear regression model. The ANFIS model is found to be optimal, but it entails a large number of parameters. The performance of the ANN-GA model is also good, yet it requires longer computation time and additional modeling parameters.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgment
This research was supported by an Internal Competitive Research Grant of the Hong Kong Polytechnic University (A-PE26).
References
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. (2000). “Artificial neural networks in hydrology. II: Hydrologic application.” J. Hydrologic Eng., 5(2), 124–137.
Chau, K. W. (2002). “Calibration of flow and water quality modeling using genetic algorithm.” Lecture Notes in Artificial Intelligence, 2557, 720–720.
Chau, K. W. (2004a). “River stage forecasting with particle swarm optimization.” Lect. Notes Comput. Sci., 3029, 1166–1173.
Chau, K. W. (2004b). “Rainfall-runoff correlation with particle swarm optimization algorithm.” Lect. Notes Comput. Sci., 3174, 970–975.
Chau, K. W., and Cheng, C. T. (2002). “Real-time prediction of water stage with artificial neural network approach.” Lecture Notes in Artificial Intelligence, 2557, 715–715.
Chau, K. W., and Lee, J. H. W. (1991a). “Mathematical modelling of Shing Mun River network.” Adv. Water Resour., 14(3), 106–112.
Chau, K. W., and Lee, J. H. W. (1991b). “A microcomputer model for flood prediction with applications.” Microcomput. Civ. Eng., 6(2), 109–121.
Cheng, C. T., and Chau, K. W. (2001). “Fuzzy iteration methodology for reservoir flood control operation.” J. Am. Water Resour. Assoc., 37(5), 1381–1388.
Cheng, C. T., Ou, C. P., and Chau, K. W. (2002). “Combining a fuzzy optimal model with a genetic algorithm to solve multi-objective rainfall-runoff model calibration.” J. Hydrol., 268(3), 72–86.
Dubrovin, T., Jolma, A., and Turunen, E. (2002). “Fuzzy model for real-time reservoir operation.” J. Water Resour. Plan. Manage., 128(1), 66–73.
Forntane, D. G., Gates, T. K., and Moncada, M. (1997). “Planning reservoir operations with imprecise objectives.” J. Water Resour. Plan. Manage., 123(3), 154–162.
Goldberg, D. E., and Kuo, C. H. (1987). “Genetic algorithms in pipeline optimization.” J. Comput. Civ. Eng., 1(2), 128–141.
Jang, J. S. R. (1993). “ANFIS: Adaptive-network-based fuzzy inference system.” IEEE Trans. Syst. Man Cybern., 23(3), 665–685.
Liong, S. Y., Lim, W. H., and Paudyal, G. N. (2000). “River stage forecasting in Bangladesh: Neural network approach.” J. Comput. Civ. Eng., 14(1), 1–8.
Olivera, R., and Loucks, D. P. (1997). “Operating rules for multireservoir systems.” Water Resour. Res., 33(4), 839–852.
Ponnambalam, K., Karray, F., and Mousavi, S. J. (2002). “Minimizing variance of reservoir systems operations benefits using soft computing tools.” Fuzzy Sets Syst., 139(2), 451–461.
Rumelhart, D. E., Widrow, B., and Lehr, M. A. (1994). “The basic ideas in neural networks.” Commun. ACM, 37(3), 87–92.
Russell, S. O., and Campbell, P. F. (1996). “Reservoir operating rules with fuzzy programming.” J. Water Resour. Plan. Manage., 122(3), 165–170.
Shahin, M. A., Maier, H. R., and Jaksa, M. B. (2002). “Predicting settlement of shallow foundations using neural networks.” J. Geotech. Geoenviron. Eng., 128(9), 785–793.
Smith, J., and Eli, R. N. (1995). “Neural-network models of rainfall-runoff process.” J. Water Resour. Plan. Manage. 121(6), 499–508.
Smith, M. (1993). Neural networks for statistical modeling, Van Nostrand Reinhold, New York.
Takagi, T., and Sugeno, M. (1985). “Fuzzy identification of systems and its applications to modeling and control.” IEEE Trans. Syst. Man Cybern., 15(1), 116–132.
Tilmant, A., Vanclooster, M., Duckstein, L., and Persoons, E. (2002). “Comparison of fuzzy and nonfuzzy optimal reservoir operating policies.” J. Water Resour. Plan. Manage., 128(6), 390–398.
Tokar, A. S., and Johnson, P. A. (1999). “Rainfall-runoff modeling using artificial neural networks.” J. Hydrologic Eng., 4(3), 232–239.
Wardlaw, R., and Sharif, M. (1999). “Evaluation of genetic algorithms for optimal reservoir system operation.” J. Water Resour. Plan. Manage., 125(1), 25–33.
Yu, P. S., Chen, C. J., and Chen, S. J. (2000). “Application of gray and fuzzy methods for rainfall forecasting.” J. Hydrologic Eng., 5(4), 339–345.
Zedeh, L., and Kacprzyk, J., eds. (1992). Fuzzy logic for the management of uncertainty, Wiley, New York.
Information & Authors
Information
Published In
Journal of Hydrologic Engineering
Volume 10 • Issue 6 • November 2005
Pages: 485 - 491
Copyright
© 2005 ASCE.
History
Received: Dec 11, 2003
Accepted: Mar 1, 2005
Published online: Nov 1, 2005
Published in print: Nov 2005
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.