Skip to main content

Comparative Study of Conventional and Computerized Reconstruction Techniques for Flow Time Series Data of Hydrometric Station


One of the undeniable requirements in hydrological forecasting and water resources studies is the availability of reliable information. Due to the various reasons, time series data are not usually complete in those surveys, therefore; reconstruction techniques are highly required to complete the missing data. This research was undertaken to evaluate the efficiency of the computer-based methods namely artificial neural network, support vector machine, ARIMA, and ARMAX along with conventional reconstruction strategies of ratio analysis, Fragment, and Thomas-Fiering. As a case study, the monthly flow data of seven hydrometric stations in the Urmia Lake Basin were employed during a 15-year period. The results were then compared using the evaluation criteria of the correlation coefficient (R2), root mean square error (RMSE), standard deviation ratio (SDR), Nash-Sutcliffe efficiency (NSE), and standard error (SE). Based on key results, computerized methods had higher accuracy than conventional ones in data reconstruction. In terms of efficiency, among the computer-based methods, the support vector machine, ARMAX, artificial neural network, and ARIMA model were ranked from the first to fourth in missing data regeneration.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8


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

    Article  Google Scholar 

  • Adnan RM, Yuan X, Kisi O, Curtef V (2017) Application of time series models for streamflow forecasting. Civil Environ Res 9(3):56–63

    Google Scholar 

  • Chao CF, Horng MH (2015) The construction of support vector machine classifier using the firefly algorithm. Comput Intell Neurosci 1:8

    Google Scholar 

  • Coulibaly P, Evora ND (2007) Comparison of neural network methods for infilling missing daily weather records. J Hydrol 341:27–41

    Article  Google Scholar 

  • Elganiny MA, Eldwer AE (2013) Comparison of stochastic models in forecasting monthly stream flow in Rivers: a case study of River Nile and its tributaries. J Water Resour Protect 8:143–153

    Article  Google Scholar 

  • Hamel L (2009) Knowledge discovery with support vector machines. John Wiley, Hoboken, N.J

    Book  Google Scholar 

  • Hamidi O, Poorolajal J, Sadeghifar M, Abbasi H, Maryanaji Z, Faridi HR, Tapak L (2014) A comparative study of support vector machines and artificial neural networks for predicting precipitation in Iran. Theor Appl Climatol 119:723–731

    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 

  • Manzour D, Yadi Pour M (2016) Studying the Iranian electricity market price with an ARMAX-GARCH Mode. Quart J Quant Econ 13(1):97–117

    Google Scholar 

  • Marzi H; Turnbull M; Marzi E (2008) Use of neural networks in forecasting financial market. Soft Computing in Industrial Applications, SMCia '08. IEEE Conference on: 240–245

  • Memarian H, Balasundram SK (2012) Comparison between multi-layer perceptron and radial basis function networks for sediment load estimation in a tropical watershed. J Water Resour Protect 4:870–876

    Article  Google Scholar 

  • Rafidah A, Suhaila Y (2013) Modeling river stream flow using support vector machine. Trans Tech Publication 315:602–605

    Google Scholar 

  • Rojas-Domínguez A, Padierna LC, Carpo Valadez JM, Puga-Soberanes H, Fraire H (2018) Optimal hyper-parameter tuning of SVM classifiers with application to medical diagnosis. IEEE Access 6:7164–7176

    Article  Google Scholar 

  • Shen L, Chen H, Yu Z, Kang W, Zhang B, Li H (2016) Evolving support vector machines using fruit fly optimization for medical data classification. Knowl-Based Syst 96(15):61–75

    Article  Google Scholar 

  • Shenify M, Danesh AS, Gocić M, Surya Taher R, Abdul Wahab AW, Gani A, Shamshirband S, Petković D (2016) Precipitation estimation using support vector machine with discrete wavelet transform. Water Resour Manag 30(2):641–652

    Article  Google Scholar 

  • Silva AT, Portela MM (2012) Disaggregation modelling of monthly stream flows using a new approach of the method of fragments. Hydrol Sci J 57(5):942–955

    Article  Google Scholar 

  • Solgi A, Nourani V, Pourhaghi A (2014) Forecasting daily precipitation using hybrid model of wavelet-artificial neural network and comparison with adaptive neuro-fuzzy inference system (case study: Verayneh Station, Nahavand). Adv Civil Eng 2014:1–12.

  • Tarekul IGM, Yoshihisa K (2009) Stochastic modeling and prediction of the Ganges flow. Advances in water resources and hydraulic engineering. Springer, Berlin, Heidelberg.

    Google Scholar 

  • Waseem M, Mani N, Andiego G, Usman M (2017) A review of criteria of fit for hydrological models. Int Res J Eng Technol (IRJET) 4(11):1765–1772

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Hamed Nozari.

Ethics declarations

Conflict of Interest


Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Nozari, H., Tavakoli, F. & Mohamadi, M. Comparative Study of Conventional and Computerized Reconstruction Techniques for Flow Time Series Data of Hydrometric Station. Water Resour Manage 33, 1913–1926 (2019).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


  • Reconstruction
  • Missing data
  • Monthly flow
  • Support vector machine