River basin flood prediction using support vector machines

This paper presents a river flood prediction technique using support vector machine (SVM). We investigated the 2-year data covering 2005 and 2006 and 7 crucial river floods that occurred in the downtown of Chiang Mai, Thailand. Past and current river levels of the 3 gauging stations are utilized as the input data of the SVM models to predict the river levels at the downtown station in 1 hour and 7 hours in advance. The performances of the SVM models are compared with that of the multilayer perceptrons (MLP) models. The experimental results show that the SVM models can perform better than the MLP models. Moreover, the results from the blind test sets demonstrate that the SVM models are appropriate for warning people before flood events. The proposed SVM prediction models are also implemented in a real-world flood warning system. The predicted river levels are accessible to public via a Web site, electronic billboards, and warning stations all over the city.

[1]  Giorgio Guariso,et al.  Coupling fuzzy modeling and neural networks for river flood prediction , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[2]  Vladimir Vapnik,et al.  An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.

[3]  Theodore B. Trafalis,et al.  Support vector machine for regression and applications to financial forecasting , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[4]  Alan F. Murray,et al.  International Joint Conference on Neural Networks , 1993 .

[5]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[6]  Wenjian Wang,et al.  Air pollutant parameter forecasting using support vector machines , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[7]  Yen-Chang Chen,et al.  Flood forecasting using radial basis function neural networks , 2001, IEEE Trans. Syst. Man Cybern. Part C.

[8]  Nipon Theera-Umpon,et al.  Support vector regression based adaptive power system stabilizer , 2003, Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS '03..

[9]  S. Gunn.  Support Vector Machines for Classification and Regression , 1998 .

[10]  Pao-Shan Yu,et al.  Real-time probabilistic forecasting of flood stages , 2007 .

[11]  Tienfuan Kerh,et al.  Neural networks forecasting of flood discharge at an unmeasured station using river upstream information , 2006, Adv. Eng. Softw..

[12]  Stefano Manetti,et al.  A real time hydrological forecasting system using a fuzzy clustering approach , 2003 .

[13]  I. Nancovska,et al.  Support vector regression for voltage reference elements monitoring , 2001, VIMS 2001. 2001 IEEE International Workshop on Virtual and Intelligent Measurement Systems (IEEE Cat. No.01EX447).

[14]  Nipon Theera-Umpon,et al.  Leeway Prediction of Oceanic Disastrous Target via Support Vector Regression , 2004, Journal of Advanced Computational Intelligence and Intelligent Informatics.

[15]  F. Girosi,et al.  Nonlinear prediction of chaotic time series using support vector machines , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.

[16]  Gang Wu,et al.  Applications of nonlinear prediction methods to the Internet traffic , 2001, ISCAS 2001. The 2001 IEEE International Symposium on Circuits and Systems (Cat. No.01CH37196).