Modeling of river water temperature using Gene Expression Programming (Case Study: MohammadAbad River in Golestan province)

Document Type : Complete scientific research article

Authors

1 Department of Water Engineering, graduate student, University of Agricultural Sciences and Natural Resources,Gorgan, Iran

2 Department of Water Engineering, Faculty Member, University of Agricultural Sciences and Natural Resources, Gorgan, Iran

3 -

4 Faculty Member

5 Department of Water Engineering, Facuity Member, University of Agricultural Sciences and Natural Resources, Gorgan, Iran

Abstract

Background and Objectives: River water temperature has both economic and ecological significance when considering issues such as water quality and biotic conditions in rivers. This parameter affects directly other water quality parameters and plays a major role in the quality of aquatic life and habitats. Consequently, with wide application of water temperature for conducting environmental impact assessments as well as for effective fisheries management, it is important to understand the thermal behavior of rivers and related heat exchange processes. Numerous deterministic and statistical models have been used for prediction of river water temperature by researchers. These modeling were generally based on the air temperature, yet. However, the river hydraulics and metrological parameters may have their special effects on river water temperature. Furthermore, there are limited researches undertaken by novel and intelligent algorithms. Hence, in this study, gene expression programming has been used for estimation of the water temperature of the MohammadAbad river located in Golestan province. In addition to the air temperature, the river hydraulics and metrological parameters were also accounted for modeling river temperature.
Material and Methods: Gene Expression Programming (GEP) is an evolutionary algorithm that uses a population of individuals and selects individuals according to fitness, and introduces genetic variation using one or more genetic operators. For the water temperature modeling, the river hydraulics and meteorological parameters including river flow discharge, flow velocity, air temperature, humidity, wind speed and cloud cover during 7-year statistical period (2006-2012) were considered as input parameters and river water temperature was selected as output parameter.
Results: Based on the comparison of the results of different GEP models with 1 to 6 input variables, it was concluded that the GEP model with 6-parameters has the highest accuracy in terms of the coefficient of determination and the root-mean-square error. These values were obtained 0.92 and 1.8˚C for training data and 0.90 and 2.3˚C for the testing data. The mean absolute errors of this model were obtained as 14.67% and 12.80% for training and testing phases, respectively, while the error of linear regression model was obtained greater than 38%. Results showed that in comparison with the multiple-linear regression model, the GEP model has better performance for river water temperature estimation.
Conclusion: According to the results obtained in this paper, one can use the GEP model for prediction of river water temperature with acceptable accuracy. It is concluded that in addition to the air temperature which has the highest impact on the river temperature, the river flow discharge also has considerable impact.

Keywords

References

1.Aytek, A., and Alp, M. 2008. An application of artificial intelligence for rainfall-runoff modeling. J. Earth Syst. Sci. 117: 2. 145-155.
2.Aytek, A., and Kisi, O. 2008. A genetic approach programming to suspended sediment modeling. J. Hydrol. 351: 288-298.
3.Brown, G.W., and Krygier, J.T. 1970. Effect of clear cutting on stream temperature. Water Resources Research, 6: 1133-1139.
4.Ebrahimian, M., and Moradi, P. 2012. The destructive effects of thermal pollution on the environment. The 3rd National Con. on Environmental Planning and Management. University of Tehran. (In Persian)
5.Eby, L., Helmy, O., Holsinger, L.M., and Young, M.K. 2014. Evidence of climate-induced range contractions in bull trout salvelinus confluentus in a rocky mountain watershed, USA. PLOS ONE, 9: E98812.
6.Elliott, J.M., and Elliott, J.A. 2010. Temperature requirements of Atlantic salmon Salmo salar, brown trout Salmo trutta and Arctic charr Salvelinus alpinus: Predicting the effects of climate change. J. Fish Biol. 44: 1-25.
7.Fernando, K.A., Shamseldin, A.Y., and Abrahart, R.J. 2012. River flow forecasting using Gene Expression Programming models. 10th Int. Conf. on Hydro Informatics, HIC 2012, Hamburg, Germany.
8.Ferreira, C. 2001. Gene Expression Programming: A new adaptive algorithm for solving problems. Complex Systems, 13: 2. 87-129.
9.Ferreira, C. 2004. Gene Expression programming and the evolution of computer programs. Recent Developments in Biologically Inspired Computing, Pp: 82-103.
10.Guven, A., and Gunal, M. 2008. Genetic programming approach for prediction of local scour downstream of hydraulic structures. J. Irrig. Drain. Engin. 134: 2. 241-249.
11.Imamgholizadeh, S., Karimi Demnah, R., and Azhdari, Kh. 2016.Comparison of conventional methods for estimation of suspended sediment load of Karkheh river by Gene Expression Programming approach. Geograph. Dev. Iran. J. 45: 121-140. (In Persian)
12.Johnson, S.L., and Jones, J.A. 2000. Stream temperature response to forest harvest and debris flows in western Cascades, Oregon. Can. J. Fish. Aqua. Sci. 57: 30-39.
13.Larnier, K., Roux, H., Dartus, D., and Croze, O. 2010. Water temperature modeling in the Garonne River (France). Knowledge and Management of Aquatic Ecosystems, 398: 04.
14.Letcher, B.H., Schueller, P., Bassar, R.D., Nislow, K.H., Coombs, J.A., Sakrejda, K., Morrissey, M., Sigourney, D.B., Whiteley, A.R., OʼDonnell, M.J., and Dubreuil, T.L. 2015. Robust estimates of environmental effects on population vita rates: an integrated capture-recapture model of seasonal brook troute growth, survival and movement in a stream network. J. Anim. Ecol. 84: 337-352.
15.Pilgrim, J.M., Fang, X., and Stefan, H.G. 1998. Stream temperature correlations with air temperature in Minnesota: implications for climatic warming. J. Amer. Water Resour. Assoc. 34: 1109-1121.
16.Prats, J., Val, R., Dolz, J., and Armengol, J. 2012. Water temperature modeling in the lower Ebro river (Spain: heat fluxes, equilibrium temperature, and magnitude of alteration caused by reservoirs and thermal effluent). Water Resour. Res. 48: W05523.
17.Somers, K.A., Bernhardt, E.S., Grace, J.B., Hassett, B.A., Sudduth, E.B., Wang, S., and Urban, D.L. 2013. Streams in the urban heat island: spatial and temporal variability in temperature, Fresh Water Science, 32: 1. 309-326.
18.Van Vliet, M.T.H., Yearsley, J.R., Franssen, W.H.P., Ludwig, F., Haddeland, I., Lettenmaier, D.P., and Kabat, P. 2012. Coupled daily stream flow and water temperature modeling in large river basins. Hydrol. Earth Syst. Sci. 16: 4303-4321.
19.Zahiri, A., Dehghani, A.A., and Azamathulla, H.Md. 2015. Chapter 4. Application of gene-expression programming in hydraulics engineering. Handbook of Genetic Programming Applications, A.H. Gandomi, A.H. Alavi and C. Ryan (eds). Springer.Pp: 71-98.
Journal of Water and Soil Conservation
Volume 27, Issue 2
May and June 2020
Pages 237-244

Files

Share

How to cite

Statistics