Comparison of Several Flood Forecasting Models in Yangtze River

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 performanc...

[1]  L. Zadeh,et al.  Fuzzy Logic for the Management of Uncertainty , 1992 .

[2]  R. P. Oliveira,et al.  Operating rules for multireservoir systems , 1997 .

[3]  Kwok-Wing Chau.  Calibration of Flow and Water Quality Modeling Using Genetic Algorithm , 2002, Australian Joint Conference on Artificial Intelligence.

[4]  Chuntian Cheng,et al.  Combining a fuzzy optimal model with a genetic algorithm to solve multi-objective rainfall–runoff model calibration , 2002 .

[5]  Chuntian Cheng,et al.  Real-Time Prediction of Water Stage with Artificial Neural Network Approach , 2002, Australian Joint Conference on Artificial Intelligence.

[6]  Murray Smith,et al.  Neural Networks for Statistical Modeling , 1993 .

[7]  Holger R. Maier,et al.  PREDICTING SETTLEMENT OF SHALLOW FOUNDATIONS USING NEURAL NETWORKS , 2002 .

[8]  Chun-tian Cheng,et al.  FUZZY ITERATION METHODOLOGY FOR RESERVOIR FLOOD CONTROL OPERATION 1 , 2001 .

[9]  Marnik Vanclooster,et al.  Comparison of Fuzzy and Nonfuzzy Optimal Reservoir Operating Policies , 2002 .

[10]  R Govindaraju,et al.  ARTIFICIAL NEURAL NETWORKS IN HYDROLOGY: II, HYDROLOGIC APPLICATIONS , 2000 .

[11]  R. Wardlaw,et al.  EVALUATION OF GENETIC ALGORITHMS FOR OPTIMAL RESERVOIR SYSTEM OPERATION , 1999 .

[12]  Samuel O. Russell,et al.  Reservoir Operating Rules with Fuzzy Programming , 1996 .

[13]  David E. Goldberg,et al.  Genetic Algorithms in Pipeline Optimization , 1987 .

[14]  Ari Jolma,et al.  Fuzzy Model for Real-Time Reservoir Operation , 2002 .

[15]  Kwok-Wing Chau.  Rainfall-Runoff Correlation with Particle Swarm Optimization Algorithm , 2004, ISNN.

[16]  Shie-Yui Liong,et al.  River Stage Forecasting in Bangladesh: Neural Network Approach , 2000 .

[17]  Kwok-Wing Chau.  River Stage Forecasting with Particle Swarm Optimization , 2004, IEA/AIE.

[18]  Jason Smith,et al.  Neural-Network Models of Rainfall-Runoff Process , 1995 .

[19]  Fakhri Karray,et al.  Minimizing variance of reservoir systems operations benefits using soft computing tools , 2003, Fuzzy Sets Syst..

[20]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[21]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[22]  Joseph H.W. Lee,et al.  Mathematical modelling of Shing Mun River network , 1991 .

[23]  Bernard Widrow,et al.  The basic ideas in neural networks , 1994, CACM.

[24]  Kwok-wing Chau.  River stage forecasting with particle swarm optimization , 2004 .

[25]  Timothy K. Gates,et al.  Planning Reservoir Operations with Imprecise Objectives , 1997 .

[26]  Pao-Shan Yu,et al.  Application of Gray and Fuzzy Methods for Rainfall Forecasting , 2000 .

[27]  A. Tokar,et al.  Rainfall-Runoff Modeling Using Artificial Neural Networks , 1999 .