Stochastic Environmental Research and Risk Assessment

, Volume 32, Issue 9, pp 2667–2682 | Cite as

River flow modelling: comparison of performance and evaluation of uncertainty using data-driven models and conceptual hydrological model

  • Zhenghao Zhang
  • Qiang Zhang
  • Vijay P. Singh
  • Peijun Shi
Original Paper


Hydrological and statistical models are playing an increasing role in hydrological forecasting, particularly for river basins with data of different temporal scales. In this study, statistical models, e.g. artificial neural networks, adaptive network-based fuzzy inference system, genetic programming, least squares support vector machine, multiple linear regression, were developed, based on parametric optimization methods such as particle swarm optimization (PSO), genetic algorithm (GA), and data-preprocessing techniques such as wavelet decomposition (WD) for river flow modelling using daily streamflow data from four hydrological stations for a period of 1954–2009. These models were used for 1-, 3- and 5-day streamflow forecasting and the better model was used for uncertainty evaluation using bootstrap resampling method. Meanwhile, a simple conceptual hydrological model GR4J was used to evaluate parametric uncertainty based on generalized likelihood uncertainty estimation method. Results indicated that: (1) GA and PSO did not help improve the forecast performance of the model. However, the hybrid model with WD significantly improved the forecast performance; (2) the hybrid model with WD as a data preprocessing procedure can clarify hydrological effects of water reservoirs and can capture peak high/low flow changes; (3) Forecast accuracy of data-driven models is significantly influenced by the availability of streamflow data. More human interferences from the upper to the lower East River basin can help to introduce greater uncertainty in streamflow forecasts; (4) The structure of GR4J may introduce larger parametric uncertainty at the Longchuan station than at the Boluo station in the East river basin. This study provides a theoretical background for data-driven model-based streamflow forecasting and a comprehensive view about data and parametric uncertainty in data-scarce river basins.


Wavelet decomposition Artificial neural network GR4J model Bootstrap method Generalized likelihood uncertainty estimation method Uncertainty 



This work is financially supported by the National Science Foundation for Distinguished Young Scholars of China (Grant No.: 51425903), the Fund for Creative Research Groups of National Natural Science Foundation of China (Grant No.: 41621061), the Key Project of National Natural Science Foundation of China (Grant No.: 51190091) and by National Natural Science Foundation of China (No.: 41401052). Our cordial gratitude should be extended to the editor-in-chief, George Christakos, Ph.D., ScD, PE, RSM, and two anonymous reviewers for their professional and pertinent comments and suggestions which are greatly helpful for further improvement of the quality of this manuscript.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Zhenghao Zhang
    • 1
  • Qiang Zhang
    • 2
    • 3
    • 4
  • Vijay P. Singh
    • 5
  • Peijun Shi
    • 2
    • 3
    • 4
  1. 1.Department of Water Resources and EnvironmentSun Yat-sen UniversityGuangzhouChina
  2. 2.Key Laboratory of Environmental Changes and Natural HazardsMinistry of Education, Academy of Disaster Reduction and Emergency Management, Beijing Normal UniversityBeijingChina
  3. 3.State Key Laboratory of Earth Surface Processes and Resource EcologyBeijing Normal UniversityBeijingChina
  4. 4.Faculty of Geographical ScienceAcademy of Disaster Reduction and Emergency Management, Beijing Normal UniversityBeijingChina
  5. 5.Department of Biological and Agricultural Engineering and Zachry Department of Civil EngineeringTexas A&M UniversityCollege StationUSA

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