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

Aman Mohammad Kalteh ()
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Aman Mohammad Kalteh: University of Guilan

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2016, vol. 30, issue 2, No 18, 747-766

Abstract: 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.

Keywords: Monthly streamflow forecasting; Least squares support vector machine; Singular spectrum analysis; Discrete wavelet analysis (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s11269-015-1188-3

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