STREAMFLOW AND SEDIMENT LOAD PREDICTION USING LINEAR GENETIC PROGRAMMING

Daily flow and suspended sediment discharge are two major hydrological variables that affect rivers’ morphology and ecosystem, particularly during flood events. Artificial neural networks (ANNs) have been successfully used to model and predict these variables in recent studies. However, these are implicit and cannot be simply used in practice. In this paper, linear genetic programming (LGP) approach has been suggested to develop explicit models to predict these variables in two rivers in Iran. The explicit relationships (prediction rules) evolved by LGP take the form of equations or program codes, which can be checked for its physical consistency. The results showed that the LGP outperforms ANNs to get global maximum and minimum discharges providing lowest root mean squared error and higher coefficient of efficiency both for training and validation periods.

Akım ve Sediment Yük Öngörümü İçin Doğrusal Genetik Programlamanın Uygulanması

Nehirlerin morfolojisini, ekosistemi ve özellikle taşkın olaylarını etkileyen iki ana değişken askıdaki sediment ve günlük akımlardır. Yapay sinir ağları (YSA), bu değişkenleri modellemek ve tahmin etmek için yakın zamanda yapılmış çalışmalarda başarıyla kullanılmıştır. Bununla birlikte, bunlar kapalı yöntemlerdir ve pratik uygulamalarda kolaylıkla kullanılamazlar. Bu makalede, İran'daki iki nehirde bu değişkenleri tahmin etmek üzere açık modeller geliştirmek için doğrusal genetik programlama (DGP) yaklaşımı önerilmiştir. DGP tarafından geliştirilen açık ilişkiler (tahmin kuralları), fiziksel tutarlılığı açısından kontrol edilebilen denklemler veya program kodları şeklindedir. Sonuçlar, global maksimum ve minimum akımları elde etme noktasında, DGP’nin YSA’ya göre daha başarılı olduğunu gerek kalibrasyon gerekse doğrulama aşamalarında hataların karelerinin ortalamasının karekökünün en düşük, verimlilik katsayısının ise daha yüksek olmasını sağlayarak göstermiştir.

PDF

___

Abrahart, R.J., Anctil, F., Coulibaly, P., et al., (2012) Two decades of anarchy? Emerging themes and outstanding challenges for neural network modelling of surface hydrology. Progresses in Physical Geography 36(4), 480-513. doi: 10.1177/0309133312444943

Aytek, A., and Kisi, O. (2008) A genetic programming approach to suspended sediment modeling, Journal of Hydrology, 351, 288-298. doi: 10.1016/j.jhydrol.2007.12.005

Babovic, V., Keijzer, M. (2002) Declarative and preferential bias in GP-based scientific discovery. Genetic Programming and Evolvable Machines, 3(1), 41-79. Retrieved from https://link.springer.com/article/10.1023/A:1014596120381

Danandeh Mehr, A., Kahya, E. (2017) A Pareto-optimal moving average multigene genetic programming model for daily streamflow prediction, Journal of Hydrology,549, 603-615. doi: 10.1016/j.jhydrol.2017.04.045

Danandeh Mehr, A., Nourani, V. (2017) A Pareto-optimal moving average-multigene genetic programming model for rainfall-runoff modelling. Environmental Modelling & Software, 92, 239-251. doi: 10.1016/j.envsoft.2017.03.004

Danandeh Mehr, A., Demirel, M.C. (2016) On the calibration of multi-gene genetic programming to simulate low flows in the Moselle River. Uludağ University Journal of the Faculty of Engineering, 21 (2), 365-376. doi: 10.17482/uumfd.278107

Danandeh Mehr, A., Kahya E., Şahin, A. and Nazemosadat M.J. (2015) Successive-station monthly streamflow prediction using different ANN algorithms. International Journal of Environmental Science and Technology, 12 (7): 2191-2200. doi: 10.1007/s13762-014-0613- 0

Danandeh Mehr, A., Kahya, E. and Yerdelen, C. (2014) Linear genetic programming application for successive-station monthly streamflow prediction. Computers & Geosciences, 70, 63-72.16(6), 1318-1330. doi: 10.1016/j.cageo.2014.04.015

Danandeh Mehr, A., Kahya E. and Olyaie E. (2013) Streamflow prediction using linear genetic programming in comparison with a neuro-wavelet technique. Journal of Hydrology, 505:240–249. doi: 10.1016/j.jhydrol.2013.10.003

Francone, D.F. (2001) DiscipulusTM Software Owner’s Manual, Version 3.0 Register Machine Learning Technologies, Inc., Littleton, Colorado. Retrieved from https://www.cs.bham.ac.uk/~wbl/biblio/gp-html/francone_manual.html

Giustolisi, O. (2004) Using genetic programming to determine chezy resistance coefficient in corrugated channels, Journal of Hydroinformatics, 157-173. doi: 10.2166/hydro.2004.0013

Guven A, Aytek A, Yuce M. I . and Aksoy H. (2008) Genetic programming-based empirical model for daily reference evapotranspiration estimation. Clean-Soil AirWater, 36(10-11) 905-912. doi: 10.1002/clen.200800009

Guven, A. (2009). Linear genetic programming for time-series modeling of daily flow rate, Journal of Earth System and Science. 118, No. 2, 157-173. doi: 10.1007/s12040-009-0022-9

Hrnjica, B. and Danandeh Mehr, A. (2019) Optimized Genetic Programming Applications: Emerging Research and Opportunities, (pp. 1-310). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-6005-0

Kisi, O. and Cigizoglu H. K. (2007) Comparision of different ANN techniques in river flow prediction, Civil engineering and environmental system. vol 24(3), 211-231. doi: 10.1080/10286600600888565

Koza, J.R., 1992. Genetic Programming: on the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, USA.

Olyaie, E. Zare Abyaneh, H. and Danandeh Mehr, A. (2017). A comparative analysis among computational intelligence techniques for dissolved oxygen prediction in Delaware River. doi: 10.1016/j.gsf.2016.04.007

Ravansalar, M., Rajaee, T., & Kisi, O. (2017). Wavelet-linear genetic programming: A new approach for modeling monthly streamflow. Journal of Hydrology, 549, 461-475. doi: 10.1016/j.jhydrol.2017.04.018

Roushangar, K., & Homayounfar, F. (2015). Prediction of Flow Friction Coefficient using GEP and ANN Methods. International Journal of Artificial Intelligence and Mechatronics, 4(2), 65-68. Retrieved from http://www.ijaim.org/volissues.html?view=publication&task=show&id=140

Sajikumar, N., & Thandaveswara, B. S. (1999). A non-linear rainfall–runoff model using an artificial neural network. Journal of hydrology, 216(1-2), 32-55. doi: 10.1016/S0022- 1694(98)00273-X

Tofiq F.A., Guven, .A (2014) Prediction of design flood discharge by statistical downscaling and General Circulation Models. Journal of Hydrology, 517, 1145-1153. doi: 10.1016/j.jhydrol.2014.06.028

Uyumaz, A., Danandeh Mehr A., Kahya E. and Erdem H. (2014) Rectangular side weirs discharge coefficient estimation in circular channels using linear genetic programming approach, Journal of Hydroinformatics, 16(6), 1318-1330. doi: 10.2166/hydro.2014.112