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Performance assessment of artificial neural networks and support vector regression models for stream flow predictions

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Abstract

Water resources planning, development, and management need reliable forecasts of river flows. In past few decades, an important dimension has been introduced in the prediction of the hydrologic phenomenon through artificial intelligence-based modeling. In this paper, the performance of three artificial neural network (ANN) and four support vector regression (SVR) models was investigated to predict streamflows in the Upper Indus River. Results from ANN models using three different optimization techniques, namely Broyden-Fletcher-Goldfarb-Shannon, Conjugate Gradient, and Back Propagation algorithms, were compared with one another. A further comparison was made between these ANNs and four types of SVR models which were based on linear, polynomial, radial basis function, and sigmoid kernels. Past 30 years’ monthly data for precipitation, temperature, and streamflow obtained from Pakistan Surface Water Hydrology Department Lahore were used for this purpose. Three types of input combinations with respect to the main input variables (temperature, precipitation, and stream flow) and several types of input combinations with respect to time lag were tested. The best input for ANN and SVR models was identified using correlation coefficient analysis and genetic algorithm. The performance of the ANN and SVR models was evaluated by mean bias error, Nash–Sutcliffe efficiency, root mean square error, and correlation coefficient. The efficiency of the Broyden-Fletcher-Goldfarb-Shannon-ANN model was found to be much better than that of other models, while the SVR model based on radial basis function kernel predicted stream flows with comparatively higher accuracy than the other kernels. Finally, long-term predictions of streamflow have been made by the best ANN model. It was found that stream flow of Upper Indus River has a decreasing trend.

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Acknowledgments

The authors would like to thank Almighty Allah, the source of all knowledge and wisdom within and beyond our comprehension. We wish to express our sincere thanks to all the contributing authors and review editors. Without their expertise, commitment, integrity, and vast investments of time, a paper of this quality would never have been completed. We are much obliged to Dr. Asim Rauf, Director and Engr. Mian Waqar Ali Shah, a research associate at Pakistan Glacier Management and Research Centre, whose expertise and guidance were pivotal to carry out this study. We would also like to thank the staff of the Pakistan Surface Water Hydrology Department, especially Director Abdul Majid and Data keeper Mr. Tariq Khan for their assistance and providing the necessary data. We are deeply indebted to them.

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  1. Ateeq-ur-Rauf

    Present address: Faculty of Civil & Environmental Engineering, University of Engineering & Technology, Taxila, Pakistan

  2. Abdul Razzaq Ghumman

    Present address: College of Engineering, Civil Engineering Department, Qassim University, Buraydah, Saudi Arabia

Authors and Affiliations

  1. Faculty of Civil & Environmental Engineering, University of Engineering & Technology, Taxila, Pakistan

    Abdul Razzaq Ghumman & Hashim Nisar Hashmi

  2. Department of Civil Engineering, Bannu Campus, University of Engg & Tech, Peshawar, Pakistan

    Ateeq-ur-Rauf

  3. Department of Civil and Environmental Engineering, University of Nevada, Las Vegas, NV, USA

    Sajjad Ahmad

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Correspondence to Abdul Razzaq Ghumman.

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Ateeq-ur-Rauf, Ghumman, A.R., Ahmad, S. et al. Performance assessment of artificial neural networks and support vector regression models for stream flow predictions. Environ Monit Assess 190, 704 (2018). https://doi.org/10.1007/s10661-018-7012-9

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