Skip to main content
SpringerLink
Log in

Improving Forecasting Accuracy of Streamflow Time Series Using Least Squares Support Vector Machine Coupled with Data-Preprocessing Techniques

  • Published:
Water Resources Management Aims and scope Submit manuscript

Abstract

Highly reliable forecasting of streamflow is essential in many water resources planning and management activities. Recently, least squares support vector machine (LSSVM) method has gained much attention in streamflow forecasting due to its ability to model complex non-linear relationships. However, LSSVM method belongs to black-box models, that is, this method is primarily based on measured data. In this paper, we attempt to improve the performance of LSSVM method from the aspect of data preprocessing by singular spectrum analysis (SSA) and discrete wavelet analysis (DWA). Kharjeguil and Ponel stations from Northern Iran are investigated with monthly streamflow data. The root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (R) and coefficient of efficiency (CE) statistics are used as comparing criteria. The results indicate that both SSA and DWA can significantly improve the performance of forecasting model. However, DWA seems to be superior to SSA and able to estimate peak streamflow values more accurately. Thus, it can be recommended that LSSVM method coupled with DWA is more promising.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Adamowski J, Chan HF (2011) A wavelet neural network conjunction model for groundwater level forecasting. J Hydrol 407:28–40

    Article  Google Scholar 

  • Adamowski J, Sun K (2010) Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. J Hydrol 390:85–91

    Article  Google Scholar 

  • Al-geelani NA, Piah MAM, Shaddad RQ (2012) Characterization of acoustic signals due to surface discharges on H.V. glass insulators using wavelet radial basis function neural networks. Appl Soft Comput 12:1239–11246

    Article  Google Scholar 

  • Bhagwat PP, Maity R (2012) Multistep-ahead river flow prediction using LS-SVR at daily scale. J Water Resour Prot 4:528–539

    Article  Google Scholar 

  • Cimen M (2008) Estimation of daily suspended sediments using support vector machines. Hydrolog Sci J 53(3):656–666

    Article  Google Scholar 

  • Cimen M, Kisi O (2009) Comparison of two different data-driven techniques in modeling lake level fluctuations in Turkey. J Hydrol 378:253–262

    Article  Google Scholar 

  • Daliakopoulos IN, Coulibaly P, Tsanis IK (2005) Groundwater level forecasting using artificial neural networks. J Hydrol 309:229–240

    Article  Google Scholar 

  • Demirel MC, Venancio A, Kahya E (2009) Flow forecast by SWAT model and ANN in Pracana basin, Portugal. Adv Eng Softw 40:467–473

    Article  Google Scholar 

  • Golyandina N, Korobeynikov A (2014) Basic singular spectrum analysis and forecasting with R. Comput Stat Data An 71:934–954

    Article  Google Scholar 

  • Golyandina N, Nekrutkin V, Zhigljavsky A (2001) Analysis of time series structure: SSA and related techniques. Chapman & Hall/CRC

  • Hsu KL, Gupta HV, Sorooshian S (1995) Artificial neural network modelling of the rainfall-runoff process. Water Resour Res 31(10):2517–2530

    Article  Google Scholar 

  • Jaipuria S, Mahapatra SS (2014) An improved demand forecasting method to reduce bullwhip effect in supply chains. Expert Syst Appl 41(5):2395–2408

    Article  Google Scholar 

  • Ji H, Yucheng B, Huiyuan W (2011) Electromechanical equipment state forecasting based on genetic algorithm-support vector regression. Expert Syst Appl 38:8399–8402

    Article  Google Scholar 

  • Kalteh AM (2013) Monthly river flow forecasting using artificial neural network and support vector regression models coupled with wavelet transform. Comput Geosci 54:1–8

    Article  Google Scholar 

  • Kalteh AM (2015) Wavelet genetic algorithm-support vector regression (wavelet GA-SVR) for monthly flow forecasting. Water Resour Manage 29(4):1283–1293

    Article  Google Scholar 

  • Kalteh AM, Berndtsson R (2007) Interpolating monthly precipitation by self-organizing map (SOM) and multilayer perceptron (MLP). Hydrolog Sci J 52(2):305–317

    Article  Google Scholar 

  • Kisi O (2012) Modeling discharge-suspended sediment relationship using least square support vector machine. J Hydrol 456–457:110–120

    Article  Google Scholar 

  • Kisi O (2015) Streamflow forecasting and estimation using least square support vector regression and adaptive neuro-fuzzy embedded fuzzy c-means clustering. Water Resour Manage 29(14):5109–5127

    Article  Google Scholar 

  • Kisi O, Cimen M (2009) Evapotranspiration modelling using support vector machines. Hydrolog Sci J 54(5):918–928

    Article  Google Scholar 

  • Kisi O, Cimen M (2011) A wavelet-support vector machine conjunction model for monthly streamflow forecasting. J Hydrol 399:132–140

    Article  Google Scholar 

  • Kisi O, Dailr AH, Cimen M, Shiri J (2012) Suspended sediment modeling using genetic programming and soft computing techniques. J Hydrol 450–451:48–58

    Article  Google Scholar 

  • Lin YJ, Cheng CT, Chau KW (2006) Using support vector machines for long-term discharge prediction. Hydrolog Sci J 51(4):599–612

    Article  Google Scholar 

  • Mallat SG (1989) A theory for multi resolution signal decomposition: the wavelet representation. IEEE T Pattern Anal 11(7):674–693

    Article  Google Scholar 

  • Mellit A, Pavan AM, Benghanem M (2012) Least squares support vector machine for short-term prediction of meteorological time series. Theor Appl Climatol 111(1–2):297–307

    Google Scholar 

  • Pai PF (2006) System reliability forecasting by support vector machines with genetic algorithms. Math Comput Model 43(3–4):262–274

    Article  Google Scholar 

  • Partal T, Kisi O (2007) Wavelet and neuro-fuzzy conjunction model for precipitation forecasting. J Hydrol 342(2):199–212

    Article  Google Scholar 

  • Rocco SCM (2013) Singular spectrum analysis and forecasting of failure time series. Reliab Eng Syst Safe 114(1):126–136

    Article  Google Scholar 

  • Samsudin R, Saad P, Shabri A (2011) River flow time series using least squares support vector machines. Hydrol Earth Syst Sc 15:1835–1852

    Article  Google Scholar 

  • Shabri A, Suhartono (2012) Streamflow forecasting using least-squares support vector machines. Hydrolog Sci J 57(7):1–19

    Article  Google Scholar 

  • Suykens JAK (2001) Nonlinear modelling and support vector machines. In: Proceeding of IEEE Instrumentation and Measurement Technology Conference 1: 287–294

  • Suykens JAK, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293–300

    Article  Google Scholar 

  • Wang WC, Chau KW, Cheng CT, Qiu L (2009) A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. J Hydrol 374:294–306

    Article  Google Scholar 

  • Wu CL, Chau KW (2011) Rainfall-runoff modeling using artificial neural network coupled with singular spectrum analysis. J Hydrol 399:394–409

    Article  Google Scholar 

  • Wu CL, Chau KW, Li YS (2008) River stage prediction based on a distributed support vector regression. J Hydrol 358(1–2):96–111

    Article  Google Scholar 

  • Wu CL, Chau KW, Fan C (2010) Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques. J Hydrol 389:146–167

    Article  Google Scholar 

  • Yoon H, Jun SC, Hyun Y, Bae GO, Lee KK (2011) A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer. J Hydrol 396(1–2):128–138

    Article  Google Scholar 

  • Yu PS, Chen ST, Chang IF (2006) Support vector regression for real-time flood stage forecasting. J Hydrol 328:704–716

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Range and Watershed Management, Faculty of Natural Resources, University of Guilan, P.O. Box 1144, Sowmehe Sara, Guilan Province, Iran

    Aman Mohammad Kalteh

Authors
  1. Aman Mohammad Kalteh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aman Mohammad Kalteh.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kalteh, A.M. Improving Forecasting Accuracy of Streamflow Time Series Using Least Squares Support Vector Machine Coupled with Data-Preprocessing Techniques. Water Resour Manage 30, 747–766 (2016). https://doi.org/10.1007/s11269-015-1188-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-015-1188-3

Keywords

Search

Navigation