Streamflow and Sediment Load Prediction Using Linear Genetic Programming

Year 2018, Volume: 23 Issue: 2, 323 - 332, 31.08.2018

Ali Danandeh Mehr , Ali Unal Şorman

https://doi.org/10.17482/uumfd.352833

Cited By: 1

Abstract

Daily flow and suspended sediment
discharge are two major hydrologıcal 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 in terms of root mean squared
error and coefficient of efficiency.

Keywords

Daily discharge, sediment, prediction, linear genetic programming, artificial neural networks

AKIM VE SEDIMENT YÜK ÖNGÖRÜMÜ İÇIN DOĞRUSAL GENETIK PROGRAMLAMANIN UYGULANMASI

Abstract

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. 

Keywords

Günlük akım, Sediment, Öngörüm, Doğrusal Genetik Pogramlama, Yapay sinir ağları

References

• 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/vol-issues.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