Comparison of Intelligent Models Efficiency for Routing of River Daily Flow (Case Study: Baleqlu-Chay River, Ardabil Province)

Nikpour, , Mohamadreza; Mahmodibabolan, Sajad

Comparison of Intelligent Models Efficiency for Routing of River Daily Flow (Case Study: Baleqlu-Chay River, Ardabil Province)

Document Type : Original Article

Authors

¹ Assisstant Professor. Department of Water Engineering. University of Mohaghegh Ardabili.

² Former Student, Department of Water Engineering, University of Mohaghegh Ardabili, Ardabil, Iran

Abstract

Abstract
Routing of river flow is one of the most important of water resources management topics to adopt appropriate decision in occurrence time of flood or droughts. In this study, artificial neural networks (ANN), gene expression programming (GEP), wavelet- neural network (WNN) and least square support vector machine (LS-SVM) models were used for routing of Baleqlu-Chay river daily flow, located at Dareh-Roud watershed. Daily river discharge data of two consecutive hydrometric stations located at the mentioned river for the period of 1997-2013. The statistics indices including, root mean square error (RMSE), correlation coefficient (R), Nash–Sutcliffe efficiency coefficient (NS) and Bias were used to evaluate the precision of the models. Comparison of results demonstrate that the LS-SVM with RMSE=1.540 m³/s, R=0.894, NS=0.713 and Bias=0.013 had the best performance in the test period. But in estimating of peak discharge values, the WNN model with average relative error equal to 34.21% had the least error. It should be mentioned that all of the models tended to underestimate the discharge values.

Keywords

References

منابع

رضایی، الف.، ع. خاشعیسیوکی و ع. شهیدی. 1393. طراحی شبکة پایش سطح آب زیرزمینی با استفاده از مدل حداقل مربعات ماشین بردار پشتیبان (LS-SVM). نشریه تحقیقات آب وخاک ایران، سال چهارم، شماره 45، ص 396-389.

شفائی، م.، ا. فاخریفرد، ص. دربندی و م.ع. قربانی. 1392. پیشبینی جریان روزانه رودخانه با استفاده از مدل هیبرید موجک و شبکه عصبی، مطالعه موردی: ایستگاه هیدرومتری ونیار در حوضه آبریز آجیچای. نشریه مهندسی آبیاری و آب ایران، سال چهارم، شماره 14، ص 128-113.

غفاری، غ. و م. وفاخواه. 1392. شبیهسازی فرآیند بارش- رواناب با استفاده از شبکه عصبی مصنوعی و سیستم فازی- عصبی تطبیقی (مطالعه موردی: حوزه آبخیز حاجیقوشان). پژوهشنامه مدیریت حوزه آبخیز ، سال چهارم، شماره 8، ص 136-120.

قربانی، م.ع. و ر. دهقانی. 1395. کاربرد شبکه‌های عصبی بیزین، ماشین بردار پشتیبان و برنامه‌ریزی بیان ژن در تحلیل بارش–رواناب ماهانه (مطالعه موردی: رودخانه کاکارضا). نشریه علوم و مهندسی آبیاری, جلد 39، شماره 2، ص 138-125.

معروفی، ص.، ک. امیرمرادی و ن. پارسافر. 1392. پیشبینی جریان روزانه با استفاده از شبکه های عصبی مصنوعی و عصبی-موجکی (مطالعه موردی: رودخانه باراندوزچای). نشریه دانش آب و خاک، جلد 23، شماره 3، ص 103-93.

نوری، ر.، ا. فرخنیا، س. مرید و ح. ریحانی مدوار. 1388. تاثیر پیش پردازش های متغیر ورودی به شبکه عصبی برای پیش بینی جریان ماهانه با آنالیز مولفه های اصلی و موجک. نشریه آب و فاضلاب، شماره 1، ص 22-13.

Ahani, A., M. Shourian, and P. Rahimi Rad. 2017. Performance assessment of the linear, nonlinear and nonparametric data driven models in river flow forecasting. Water Resources Management, DOI 10.1007/s11269-017-1792-5

Behzad, M., K. Asghari, M. Eazi and M. Palhang. 2009.Generalization performance of support vector machines and neural networks in runoff modeling. Expert Systems with applications, 36(4): 7624-7629.

Ferreira, C. 2001. Algorithm for solving gene expression programming: a new adaptive problems. Complex Systems, 13(2): 87-129.

Ferreira, C. 2006. Automatically defined functions in gene expression programming. In Genetic Systems Programming. Springer Berlin Heidelberg, 21-56.

Ghorbani, M. A., O. Kisi, M. Aalinezhad. 2010. A probe into the chaotic nature of daily streamflow time series by correlation dimension and largest Lyapunov methods. Applied Mathematical Modelling, 34(12): 4050-4057.

Ghorbani, M. A., R. Khatibi, A. Goel, M.H. FazeliFard and A. Azani. 2016. Modeling river discharge time series using support vector machine and artificial neural networks. Environmental Earth Sciences, 75(8): 1-13.

He, Z., X. Wen, H. Liu and J. Du. 2014. A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region. Journal of Hydrology, 509: 379-386.

Kisi, O. 2008. Stream flow forecasting using neuro‐wavelet technique. Hydrological Processes, 22(20): 4142-4152.

Nourani, V., A. Hosseini Baghanam, J. Adamowski and O. Kisi. 2014. Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review. Journal of Hydrology, 514(1): 358-377.

Shafaei, M. and O. Kisi. 2016. Predicting river daily flow using wavelet-artificial neural networks based on regression analyses in comparison with artificial neural networks and support vector machine models. Neural Computing and Applications, 1-14.

Sivapragasam, C., R. Maheswaran and V. Venkatesh. 2008. Genetic programming approach for flood routing in natural channels. Hydrological processes, 22(5): 623-628.

Suykens, J.A., J. De Brabanter. L. Lukas and J. Vandewalle. 2002. Weighted least squares support vector machines: robustness and sparse approximation. Neurocomputing, 48(1): 85-105.

Taheri, H. and M. Ghafouri. 2012. Comparison between active learning method and support vector machine for runoff modeling. Journal of Hydrology and Hydromechanics, 60(1): 16-32.

Tiwari, M.K. 2015. River flow forecasting using neural networks coupled with wavelet analysis. International Journal of Engineering Research and Technology, 4(3): 174-179.

Vapnic, VN. 1998. Statistical Learning Theory. Wiley, NEW YORK: USA

Yu, P.S., S.T. Chen and I.F. Chang. 2006. Support vector regression for real-time flood stage forecasting. Journal of Hydrology, 328(3): 704-716.