Precipitation forecasting by using wavelet-support vector machine conjunction model

A new wavelet-support vector machine conjunction model for daily precipitation forecast is proposed in this study. The conjunction method combining two methods, discrete wavelet transform and support vector machine, is compared with the single support vector machine for one-day-ahead precipitation forecasting. Daily precipitation data from Izmir and Afyon stations in Turkey are used in the study. The root mean square errors (RMSE), mean absolute errors (MAE), and correlation coefficient (R) statistics are used for the comparing criteria. The comparison results indicate that the conjunction method could increase the forecast accuracy and perform better than the single support vector machine. For the Izmir and Afyon stations, it is found that the conjunction models with RMSE=46.5mm, MAE=13.6mm, R=0.782 and RMSE=21.4mm, MAE=9.0mm, R=0.815 in test period is superior in forecasting daily precipitations than the best accurate support vector regression models with RMSE=71.6mm, MAE=19.6mm, R=0.276 and RMSE=38.7mm, MAE=14.2mm, R=0.103, respectively. The ANN method was also employed for the same data set and found that there is a slight difference between ANN and SVR methods.

[1]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[2]  Mohammad H. Aminfar,et al.  A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation , 2009, Eng. Appl. Artif. Intell..

[3]  N. J. Ferreira,et al.  Artificial neural network technique for rainfall forecasting applied to the São Paulo region , 2005 .

[4]  Witold F. Krajewski,et al.  Rainfall forecasting in space and time using a neural network , 1992 .

[5]  Paulin Coulibaly,et al.  Wavelet analysis of variability in annual Canadian streamflows , 2004 .

[6]  Supawadee Ingsriswang,et al.  Machine Learning Techniques for Short-Term Rain Forecasting System in the Northeastern Part of Thailand , 2008 .

[7]  Nachimuthu Karunanithi,et al.  Neural Networks for River Flow Prediction , 1994 .

[8]  R.K. Jurgen,et al.  Sarnoff Labs: 'still crazy' but coping , 1988, IEEE Spectrum.

[9]  O. Kisi Neural Networks and Wavelet Conjunction Model for Intermittent Streamflow Forecasting , 2009 .

[10]  Norman W. Junker,et al.  Evaluation of 33 Years of Quantitative Precipitation Forecasting at the NMC , 1995 .

[11]  Mohammad Taghi Dastorani,et al.  Application of ANN and ANFIS models on dryland precipitation prediction (case study: Yazd in Central Iran). , 2010 .

[12]  Chen Yunping,et al.  An ANN and wavelet transformation based method for short term load forecast , 1998, Proceedings of EMPD '98. 1998 International Conference on Energy Management and Power Delivery (Cat. No.98EX137).

[13]  Murat Küçük,et al.  Wavelet Regression Technique for Streamflow Prediction , 2006 .

[14]  R. Hecht-Nielsen,et al.  Neurocomputing: picking the human brain , 1988, IEEE Spectrum.

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

[16]  Celso Augusto Guimarães Santos,et al.  Drought forecast using an artificial neural network for three hydrological zones in San Francisco River basin, Brazil. , 2009 .

[17]  Chie-Ming Chou,et al.  On-line estimation of unit hydrographs using the wavelet-based LMS algorithm / Estimation en ligne des hydrogrammes unitaires grâce à l'algorithme des moindres carrés moyens à base d'ondelettes , 2002 .

[18]  Liu Mingzhe,et al.  Least Square Support Vector Machine Ensemble for Daily Rainfall Forecasting Based on Linear and Nonlinear Regression , 2010 .

[19]  Turgay Partal,et al.  Long-term trend analysis using discrete wavelet components of annual precipitations measurements in Marmara region (Turkey) , 2006 .

[20]  Ping Wang,et al.  Multiscale characteristics of the rainy season rainfall and interdecadal decaying of summer monsoon in North China , 2003 .

[21]  Yong Peng,et al.  The Research of Monthly Discharge Predictor-corrector Model Based on Wavelet Decomposition , 2008 .

[22]  H A Ceccatto,et al.  Predicting Indian monsoon rainfall: a neural network approach , 1994 .

[23]  Frank S. Marzano,et al.  Neural-network approach to ground-based passive microwave estimation of precipitation intensity and extinction , 2006 .

[24]  Ozgur Kisi,et al.  Stream flow forecasting using neuro‐wavelet technique , 2008 .

[25]  Shie-Yui Liong,et al.  Rainfall and runoff forecasting with SSA-SVM approach , 2001 .

[26]  Wei-Chiang Hong,et al.  Rainfall forecasting by technological machine learning models , 2008, Appl. Math. Comput..

[27]  Konstantine P. Georgakakos,et al.  A hydrologically useful station precipitation model: 1. Formulation , 1984 .

[28]  Nitin K. Tripathi,et al.  An artificial neural network model for rainfall forecasting in Bangkok, Thailand , 2008 .

[29]  M. Çimen,et al.  Estimation of daily suspended sediments using support vector machines , 2008 .

[30]  Akira Kawamura,et al.  Neural Networks for Rainfall Forecasting by Atmospheric Downscaling , 2004 .

[31]  K. P. Moustris,et al.  Precipitation Forecast Using Artificial Neural Networks in Specific Regions of Greece , 2011 .

[32]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[33]  K. Chau,et al.  Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques , 2010 .

[34]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[35]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[36]  Laurence C. Smith,et al.  Stream flow characterization and feature detection using a discrete wavelet transform , 1998 .

[37]  Ahmed El-Shafie,et al.  Adaptive neuro-fuzzy inference system based model for rainfall forecasting in Klang River, Malaysia , 2011 .

[38]  Ozgur Kisi,et al.  A wavelet-support vector machine conjunction model for monthly streamflow forecasting , 2011 .