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

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• 作者：Aman Mohammad Kalteh
• 关键词：Monthly streamflow forecasting ; Least squares support vector machine ; Singular spectrum analysis ; Discrete wavelet analysis
• 刊名：Water Resources Management
• 出版年：2016
• 出版时间：January 2016
• 年：2016
• 卷：30
• 期：2
• 页码：747-766
• 全文大小：1,257 KB
• 参考文献：Adamowski J, Chan HF (2011) A wavelet neural network conjunction model for groundwater level forecasting. J Hydrol 407:28–40CrossRef
  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 390:85–91CrossRef
  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 12:1239–11246CrossRef
  Bhagwat PP, Maity R (2012) Multistep-ahead river flow prediction using LS-SVR at daily scale. J Water Resour Prot 4:528–539CrossRef
  Cimen M (2008) Estimation of daily suspended sediments using support vector machines. Hydrolog Sci J 53(3):656–666CrossRef
  Cimen M, Kisi O (2009) Comparison of two different data-driven techniques in modeling lake level fluctuations in Turkey. J Hydrol 378:253–262CrossRef
  Daliakopoulos IN, Coulibaly P, Tsanis IK (2005) Groundwater level forecasting using artificial neural networks. J Hydrol 309:229–240CrossRef
  Demirel MC, Venancio A, Kahya E (2009) Flow forecast by SWAT model and ANN in Pracana basin, Portugal. Adv Eng Softw 40:467–473CrossRef
  Golyandina N, Korobeynikov A (2014) Basic singular spectrum analysis and forecasting with R. Comput Stat Data An 71:934–954CrossRef
  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):2517–2530CrossRef
  Jaipuria S, Mahapatra SS (2014) An improved demand forecasting method to reduce bullwhip effect in supply chains. Expert Syst Appl 41(5):2395–2408CrossRef
  Ji H, Yucheng B, Huiyuan W (2011) Electromechanical equipment state forecasting based on genetic algorithm-support vector regression. Expert Syst Appl 38:8399–8402CrossRef
  Kalteh AM (2013) Monthly river flow forecasting using artificial neural network and support vector regression models coupled with wavelet transform. Comput Geosci 54:1–8CrossRef
  Kalteh AM (2015) Wavelet genetic algorithm-support vector regression (wavelet GA-SVR) for monthly flow forecasting. Water Resour Manage 29(4):1283–1293CrossRef
  Kalteh AM, Berndtsson R (2007) Interpolating monthly precipitation by self-organizing map (SOM) and multilayer perceptron (MLP). Hydrolog Sci J 52(2):305–317CrossRef
  Kisi O (2012) Modeling discharge-suspended sediment relationship using least square support vector machine. J Hydrol 456–457:110–120CrossRef
  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):5109–5127CrossRef
  Kisi O, Cimen M (2009) Evapotranspiration modelling using support vector machines. Hydrolog Sci J 54(5):918–928CrossRef
  Kisi O, Cimen M (2011) A wavelet-support vector machine conjunction model for monthly streamflow forecasting. J Hydrol 399:132–140CrossRef
  Kisi O, Dailr AH, Cimen M, Shiri J (2012) Suspended sediment modeling using genetic programming and soft computing techniques. J Hydrol 450–451:48–58CrossRef
  Lin YJ, Cheng CT, Chau KW (2006) Using support vector machines for long-term discharge prediction. Hydrolog Sci J 51(4):599–612CrossRef
  Mallat SG (1989) A theory for multi resolution signal decomposition: the wavelet representation. IEEE T Pattern Anal 11(7):674–693CrossRef
  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
  Pai PF (2006) System reliability forecasting by support vector machines with genetic algorithms. Math Comput Model 43(3–4):262–274CrossRef
  Partal T, Kisi O (2007) Wavelet and neuro-fuzzy conjunction model for precipitation forecasting. J Hydrol 342(2):199–212CrossRef
  Rocco SCM (2013) Singular spectrum analysis and forecasting of failure time series. Reliab Eng Syst Safe 114(1):126–136CrossRef
  Samsudin R, Saad P, Shabri A (2011) River flow time series using least squares support vector machines. Hydrol Earth Syst Sc 15:1835–1852CrossRef
  Shabri A, Suhartono (2012) Streamflow forecasting using least-squares support vector machines. Hydrolog Sci J 57(7):1–19CrossRef
  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):293–300CrossRef
  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 374:294–306CrossRef
  Wu CL, Chau KW (2011) Rainfall-runoff modeling using artificial neural network coupled with singular spectrum analysis. J Hydrol 399:394–409CrossRef
  Wu CL, Chau KW, Li YS (2008) River stage prediction based on a distributed support vector regression. J Hydrol 358(1–2):96–111CrossRef
  Wu CL, Chau KW, Fan C (2010) Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques. J Hydrol 389:146–167CrossRef
  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):128–138CrossRef
  Yu PS, Chen ST, Chang IF (2006) Support vector regression for real-time flood stage forecasting. J Hydrol 328:704–716CrossRef
  
• 作者单位：Aman Mohammad Kalteh (1)
  
  1. Department of Range and Watershed Management, Faculty of Natural Resources, University of Guilan, P.O. Box 1144, Sowmehe Sara, Guilan Province, Iran
  
• 刊物类别：Earth and Environmental Science
• 刊物主题：Earth sciences
  Hydrogeology
  Geotechnical Engineering
  Meteorology and Climatology
  Civil Engineering
  Environment
  
• 出版者：Springer Netherlands
• ISSN：1573-1650

文摘

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. Keywords Monthly streamflow forecasting Least squares support vector machine Singular spectrum analysis Discrete wavelet analysis