The incorrect usage of singular spectral analysis and discrete wavelet transform in hybrid models to predict hydrological time series

Journal of Hydrology

Volumn 552, Issue , 2017, Pages 44-51

  Du, Kongchang ^a,b   Zhao, Ying ^a   Lei, Jiaqiang ^a  

^a XINJIANG INSTITUTE OF ECOLOGY AND GEOGRAPHY   (China)

^b UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

Author keywords

Artificial network; Discrete wavelet transform; Hydrological time series; Prediction; Singular spectrum analysis

Indexed keywords

DISCRETE WAVELET TRANSFORMS; FORECASTING; SIGNAL RECONSTRUCTION; SPECTRUM ANALYSIS; SUPPORT VECTOR MACHINES; TIME SERIES;

ARTIFICIAL NETWORKS; COMPARATIVE EXPERIMENTS; ENSEMBLE MODELS; HYDROLOGICAL TIME-SERIES; PREDICTION ERRORS; PREDICTION PERFORMANCE; PREPROCESSING TECHNIQUES; SINGULAR SPECTRUM ANALYSIS;

TIME SERIES ANALYSIS;

ARTIFICIAL NEURAL NETWORK; DECOMPOSITION ANALYSIS; HYDROLOGICAL REGIME; NUMERICAL METHOD; PREDICTION; SPECTRAL ANALYSIS; SUPPORT VECTOR MACHINE; TIME SERIES ANALYSIS; WAVELET ANALYSIS;

EID: 85021407587     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2017.06.019     Document Type: Article

References (21)

1. Abdollahzade, M., Miranian, A., Hassani, H., Iranmanesh, H., A new hybrid enhanced local linear neuro-fuzzy model based on the optimized singular spectrum analysis and its application for nonlinear and chaotic time series forecasting. Inf. Sci. (Ny) 295 (2015), 107–125, 10.1016/j.ins.2014.09.002.
2. Baratta, D., Cicioni, G., Masulli, F., Studer, L., Application of an ensemble technique based on singular spectrum analysis to daily rainfall forecasting. Neural Netw. 16 (2003), 375–387, 10.1016/S0893-6080(03)00022-4.
3. Broomhead, D.S., King, G.P., Extracting qualitative dynamics from experimental data. Phys. D Nonlinear Phenom. 20 (1986), 217–236, 10.1016/0167-2789(86)90031-X.
4. Golyandina, N., Nekrutkin, V., Zhigljavsky, A., 2001. Analysis of Time Series Structure: SSA and Related Techniques. Chapman Hall/CRC Monogr. Stat. Appl. Probab. 320. http://dx.doi.org/10.1198/jasa.2002.s239.
5. Hagan, M., Neural network design. Network, 4120, 2007, 45308.
6. He, X., Guan, H., Qin, J., A hybrid wavelet neural network model with mutual information and particle swarm optimization for forecasting monthly rainfall. J. Hydrol. 527 (2015), 88–100, 10.1016/j.jhydrol.2015.04.047.
7. Keller, A., von Steiger, B., van der Zee, S.E.A.T.M., Schulin, R., A stochastic empirical model for regional heavy-metal balances in agroecosystems. J. Environ. Qual. 30 (2001), 1976–1989.
8. Kisi, O., Cimen, M., Evapotranspiration modelling using support vector machines. Hydrol. Sci. J. 54 (2009), 918–928.
9. Kisi, O., Cimen, M., Precipitation forecasting by using wavelet-support vector machine conjunction model. Eng. Appl. Artif. Intell. 25 (2012), 783–792, 10.1016/j.engappai.2011.11.003.
10. Nash, J.E., Sutcliffe, J.V., River flow forecasting through conceptual models part I – a discussion of principles. J. Hydrol. 10 (1970), 282–290, 10.1016/0022-1694(70)90255-6.
11. Nourani, V., Alami, M.T., Aminfar, M.H., A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation. Eng. Appl. Artif. Intell. 22 (2009), 466–472, 10.1016/j.engappai.2008.09.003.
12. Nourani, V., Hosseini Baghanam, A., Adamowski, J., Kisi, O., Applications of hybrid wavelet-artificial Intelligence models in hydrology: a review. J. Hydrol. 514 (2014), 358–377, 10.1016/j.jhydrol.2014.03.057.
13. Partal, T., Modelling evapotranspiration using discrete wavelet transform and neural networks. Hydrol. Process. 23 (2009), 3545–3555.
14. Sang, Y.F., Improved wavelet modeling framework for hydrologic time series forecasting. Water Resour. Manage. 27 (2013), 2807–2821, 10.1007/s11269-013-0316-1.
15. Sivapragasam, C., Liong, S., Pasha, M., Rainfall and runoff forecasting with SSA-SVM approach. J. Hydroinf., 141–152, 2001, 10.1016/j.jhydrol.2010.02.030.
16. Valipour, M., Banihabib, M.E., Behbahani, S.M.R., Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir. J. Hydrol. 476 (2013), 433–441, 10.1016/j.jhydrol.2012.11.017.
17. Venkata Ramana, R., Krishna, B., Kumar, S.R., Pandey, N.G., Monthly rainfall prediction using wavelet neural network analysis. Water Resour. Manage. 27 (2013), 3697–3711, 10.1007/s11269-013-0374-4.
18. Wu, C.L., Chau, K.W., Prediction of rainfall time series using modular soft computing methods. Eng. Appl. Artif. Intell. 26 (2013), 997–1007, 10.1016/j.engappai.2012.05.023.
19. Wu, C.L., Chau, K.W., Li, Y.S., Predicting monthly streamflow using data-driven models coupled with data-preprocessing techniques. Water Resour. Res. 45 (2009), 1–23, 10.1029/2007WR006737.
20. Wu, C.L., Chau, K.W., Li, Y.S., Methods to improve neural network performance in daily flows prediction. J. Hydrol. 372 (2009), 80–93, 10.1016/j.jhydrol.2009.03.038.
21. Wu, C.L., Chau, K.W., Fan, C., Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques. J. Hydrol. 389 (2010), 146–167, 10.1016/j.jhydrol.2010.05.040.