A hybrid model for stream flow forecasting using wavelet and least squares support vector machines

This paper proposed a hybrid wavelet-least square support vector machines (WLSSVM) model that combine both wavelet method and LSSVM model for monthly stream flow forecasting. The original stream flow series was decomposed into a number of sub-series of time series using wavelet theory and these time series were imposed as input data to the LSSVM for stream flow forecasting. The monthly stream flow data from Klang and Langat stations in Peninsular Malaysia are used for this case study. Time series prediction capability performance of the WLSSVM model is compared with single LSSVM and Autoregressive Integrated Moving Average (ARIMA) models using various statistical measures. Empirical results showed that the WLSSVM model yield a more accurate outcome compared to individual LSSVM, ANN and ARIMA models for monthly stream flow forecasting.

[1]  Ruhaidah Samsudin,et al.  A hybrid model of self-organizing maps (SOM) and least square support vector machine (LSSVM) for time-series forecasting , 2011, Expert Syst. Appl..

[2]  M. Firat,et al.  Comparison of Artificial Intelligence Techniques for river flow forecasting , 2008 .

[3]  Lixia Liu,et al.  Exchange Rates Forecasting with Least Squares Support Vector Machine , 2008, 2008 International Conference on Computer Science and Software Engineering.

[4]  Chuntian Cheng,et al.  A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series , 2009 .

[5]  R. Gopakumar,et al.  Hydrologic Data Exploration and River Flow Forecasting of a Humid Tropical River Basin Using Artificial Neural Networks , 2007 .

[6]  Linda See,et al.  Data preprocessing for river flow forecasting using neural networks: Wavelet transforms and data partitioning , 2006 .

[7]  Jun Guo,et al.  Monthly streamflow forecasting based on improved support vector machine model , 2011, Expert Syst. Appl..

[8]  Asaad Y. Shamseldin,et al.  Comparison of different forms of the Multi-layer Feed-Forward Neural Network method used for river flow forecasting , 2002 .

[9]  Jun Li,et al.  Dynamic temperature modeling of an SOFC using least squares support vector machines , 2008 .

[10]  Holger R. Maier,et al.  Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications , 2000, Environ. Model. Softw..

[11]  R. Modarres.  Streamflow drought time series forecasting , 2007 .

[12]  O. Kisi,et al.  Wavelet and neuro-fuzzy conjunction model for precipitation forecasting , 2007 .

[13]  Juan B. Valdés,et al.  NONLINEAR MODEL FOR DROUGHT FORECASTING BASED ON A CONJUNCTION OF WAVELET TRANSFORMS AND NEURAL NETWORKS , 2003 .

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

[15]  Jan Adamowski,et al.  Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. , 2010 .

[16]  Vahid Nourani,et al.  Prediction of daily suspended sediment load using wavelet and neurofuzzy combined model , 2010 .

[17]  Umut Okkan,et al.  Rainfall–runoff modeling using least squares support vector machines , 2012 .

[18]  Radhey Shyam.  IT for water resource management , 2015, 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom).

[19]  O. Kisi.  Wavelet regression model for short-term streamflow forecasting. , 2010 .

[20]  Kadri Yürekli,et al.  Prediction of Daily Maximum Streamflow Based on Stochastic Approaches , 2012 .

[21]  H. K. Cigizoglu,et al.  Incorporation of ARMA models into flow forecasting by artificial neural networks , 2003 .

[22]  Ani Shabri,et al.  Streamflow forecasting using least-squares support vector machines , 2012 .

[23]  Vahid Nourani,et al.  A Multivariate ANN-Wavelet Approach for Rainfall–Runoff Modeling , 2009 .

[24]  Wenrui Huang,et al.  Forecasting flows in Apalachicola River using neural networks , 2004 .

[25]  Turgay Partal,et al.  Estimation and forecasting of daily suspended sediment data using wavelet–neural networks , 2008 .

[26]  Ozgur Kisi.  Neural network and wavelet conjunction model for modelling monthly level fluctuations in Turkey. , 2009 .

[27]  Wensheng Wang,et al.  Wavelet Network Model and Its Application to the Prediction of Hydrology , 2003 .

[28]  M. Erol Keskin,et al.  Artificial Models for Interbasin Flow Prediction in Southern Turkey , 2009 .

[29]  Kwok-Wing Chau,et al.  Data-driven models for monthly streamflow time series prediction , 2010, Eng. Appl. Artif. Intell..

[30]  C. Fernández,et al.  Streamflow drought time series forecasting: a case study in a small watershed in North West Spain , 2009 .

[31]  A. Shabri,et al.  River Flow Forecasting: a Hybrid Model of Self Organizing Maps and Least Square Support Vector Machine , 2010 .

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

[33]  Adarsh Singh,et al.  Daily river flow forecasting using wavelet ANN hybrid models , 2010 .

[34]  O. Kisi.  River flow forecasting and estimation using different artificial neural network techniques , 2008 .

[35]  D. Labat,et al.  Rainfall-runoff relations for karstic springs. Part II: Continuous wavelet and discrete orthogonal multiresolution analyses. , 2000 .

[36]  R. Shah,et al.  Least Squares Support Vector Machines , 2022 .

[37]  Muhsin Tunay Gençoglu,et al.  Prediction of flashover voltage of insulators using least squares support vector machines , 2009, Expert Syst. Appl..

[38]  Davut Hanbay.  An expert system based on least square support vector machines for diagnosis of the valvular heart disease , 2009, Expert Syst. Appl..

[39]  Jiang Liangzhong,et al.  Water Quality Prediction Using LS-SVM and Particle Swarm Optimization , 2009, WKDD.