Application of Gene Expression Programming to water dissolved oxygen concentration prediction

Mehdipour, V.; Memarianfard, M.; Homayounfar, F.

doi:10.22034/ijhcum.2017.02.01.004

Document Type : ORIGINAL RESEARCH ARTICLE

Authors

¹ Civil Engineering Department of Khajeh Nasir Toosi University of Technology, Tehran, Iran

² Civil Engineering Department of AmirKabir university of technology, Tehran, Iran

10.22034/ijhcum.2017.02.01.004

Abstract

This research based on record and collected data from four stations at Eymir Lake, Turkey, which are monitored daily in seven months. Water quality monitoring using former methods are time-needed and expensive, while the application of gene expression programming is more understandable, rapid, and reliable which is used in this article to provide a prediction for dissolved oxygen. The concentration of oxygen is one of the most important factors of water quality identification, which shows if water has proper ability for aquatic life, agriculture, sanitary and drink, or not. Therefore, the concentration of oxygen is one of the most important parameters, which cannot be calculated by mathematical analyses directly. Phosphor, nitrate, phosphate, dissolved nitrogen, water alkalinity, water temperature, dissolved chlorophyll, electrical conductivity, precipitation rate, wind velocity and environment temperature are parameters which used as correlated factors to better prediction of dissolved oxygen in this paper. In the best model determination coefficient and root mean square error values respectively, were found to be 0.8031 and 0.0937. Finally, the assessment of forecasted data showed that the proposed approach produces satisfactory results.

Keywords

Main Subjects

Urban ecology and related environmental concerns

References

Akilandeswari, S.; Adline Mahiba, H., (2013). Prediction of BOD values in engineering work industrial efﬂuent by Anﬁs modeling. Int. J. Res. Pure Appl. Phys., 3(2): 7–9 (3 pages).

Alte, P. D.; Sadgir, P. A., (2015). Water quality prediction by using ANN. Int. J. Adv. Found. Res. In Sci. Eng., 1: 278-285 (8 pages).

Altinbilek, D..; Usul, N.; Yazicioglu, H.; Kutoglu, Y.; Merzi, N.; Gögüs, M.; Doyuran, V. ; Günyakti, A., (1995). Water resources and environment management plan project for Gölvesi-Mogan-Ayrir Lake. In Mogan and Eymir lakes first environment Conference, 13–21 (9 pages). (In Turkish)

Azamathulla, H. M.; Ghani, A. A.; Leow, C. S.; Chang, C. K.; Zakaria, N. A., (2011). Gene-expression programming for the development of a stage-discharge curve of the Pahang River. Water Resour. Manage., 25(11): 2901-2916 (16 pages).

Beklioglu, M.; Ince, O.; Tuzun, I., (2003). Restoration of the eutrophic Lake Eymir, Turkey, by biomanipulation after a major external nutrient control I. Hydrobiologia, 490(1): 93-105 (13 pages).

Chu, H.B.; Lu, W.X.; Zhang, L., (2013). Application of artiﬁcial neural network in environmental water quality assessment. J. Agric. Sci. Technol. 15(2): 343–356 (14 pages)

Ding, Y.R.; Cai, Y.J.; Sun, P.D.; Chen, B., (2014). The use of combined neural network and genetic algorithms for prediction of water quality. J. Appl. Res. Technol., 12(3) : 493–499 (7 pages)

Ferreira, C., (2006). Gene expression programming: mathematical modeling by an artificial intelligence, Vol. 21. Berlin-Heidelberg, Springer-Verlag.

Ferreira, C., (2002). Gene expression programming in problem solving. Part VI, In Soft computing and industry, Springer, London, 635-653 (19 pages).

Ferreira, C., (2001). Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst., 13(2): 87–129 (43 pages).

Jain, A.; Kumar, A. M., (2007). Hybrid neural network models for hydrologic time series forecasting. Appl. Soft Comput., 7(2): 585-592 (8 pages).

Karimi, S.; Shiri, J.; Kisi, O.; Shiri, A.A., (2016). Short-term and long-term streamflow prediction by using'wavelet–gene expression's programming approach. J. Hydraul. Eng., 22(2): 148-162 (15 pages).

Kisi, O.; Akbari, N.; Sanatipour, M.; Hashemi, A.; Teimourzadeh, K.; Shiri, J., (2013). Modeling of Dissolved Oxygen in River Water Using Artificial Intelligence Techniques. J. Environ. Inf., 22 (2): 92-101 (10 pages).

Kişi, Ö., (2008). River flow forecasting and estimation using different artificial neural network techniques. Hydrol. Res., 39(1): 27-40 (14 pages).

Koza, J.R., (1992). Genetic programming: on the programming of computers by means of natural selection, Vol. 1, MIT Press, Cambridge.

Lacombe, G.; Douangsavanh, S.; Vogel, R. M.; McCartney, M.; Chemin, Y.; Rebelo, L. M.; Sotoukee, T., (2014). Multivariate power-law models for streamflow prediction in the Mekong Basin. J. Hydrol.: Reg. Stud., 2: 35-48 (14 pages).

Lihua, C.; Shengquan, M.; Li, L., (2008). A model to evaluate do of river based on artificial neural network and stylebook. J. Hainan Normal Univ. (Nat. Sci.), 21(4) : 372–376 (5 pages).

Liu, S.; Tai, H.; Ding, Q.; Li, D.; Xu, L.; Wei, Y., (2013). A hybrid approach of support vector regression with genetic algorithm optimization for aquaculture water quality prediction. Math. Comput. Modell., 58(3): 458-465 (8 pages).

Martí, P.; Shiri, J.; Duran-Ros, M.; Arbat, G.; De Cartagena, F.R.; Puig-Bargués, J., (2013). Artificial neural networks vs. gene expression programming for estimating outlet dissolved oxygen in micro-irrigation sand filters fed with effluents. Comput. Electron. Agric., 99: 176-185 (10 pages).

McKnight, U. S.; Funder, S. G.; Rasmussen, J. J.; Finkel, M.; Binning, P. J.; Bjerg, P. L., (2010). An integrated model for assessing the risk of TCE groundwater contamination to human receptors and surface water ecosystems. Ecol. Eng., 36(9): 1126-1137 (12 pages).

Mitchell, M., (1996). An Introduction to Genetic Algorithms, MIT Press Cambridge, MA, USA.

Palani, S.; Liong, S.Y.; Tkalich, P.; Palanichamy, J., (2009). Development of a neural network model for dissolved oxygen in seawater. Indian J. Mar. Sci., 38(2): 151-159 (9 pages).

Sarkar, A.; Pandey, P., (2015). River water quality modeling using artificial neural network technique. Aquatic Procedia, 4: 1070-1077 (8 pages).

Pour, S. H.; Harun, S. B.; Shahid, S., (2014). Genetic programming for the downscaling of extreme rainfall events on the East Coast of Peninsular Malaysia. Atmos., 5(4): 914-936 (23 pages).

Rounds, S. A., (2002). Development of a neural network model for dissolved oxygen in the Tualatin river, Oregon, In Second Federal Interagency hydrologic modeling conference, Las Vegas, Nevada, 1–13 (13 pages).

Salami, E. S.; Ehteshami, M., (2015). Simulation, evaluation and prediction modeling of river water quality properties (case study: Ireland Rivers). Int. J. Environ. Sci. Technol., 12(10): 3235–3242 (8 Pages).

Razaq, S. A.; Shahid, S.; Ismail, T.; Chung, E. S.; Mohsenipour, M.; Wang, X. J., (2016). Prediction of flow duration curve in ungauged catchments using genetic expression programming. Procedia Eng., 154: 1431-1438 (8 pages).

Scholten, H.; Kassahun, A.; Refsgaard, J. C.; Kargas, T.; Gavardinas, C.; Beulens, A. J. M., (2007). A methodology to support multidisciplinary model-based water management. Environ. Modell. Software. 22(5) : 743–759 (17 pages).

Simeonov, V.; Einax, J.; Stanimirova, I.; Kraft, J., (2002). Environmetric modeling and interpretation of river water monitoring data. Anal. Bioanal.Chem, 374(5): 898-905 (8 pages).

Singh, K. P.; Basant, N.; Gupta, S., (2011). Support vector machines in water quality management. Anal. Chim. Acta, 703(2): 152-162 (11 pages).

Tayfur, G., (2002). Artificial neural networks for sheet sediment transport. Hydrol. Sci. J., 47(6): 879-892 (14 pages).

Yagbasan, O.; Yazicigil, H., (2009). Sustainable management of Mogan and Eymir Lakes in central Turkey. Environ. Geol., 56(6): 1029-1040 (12 pages).

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International Journal of Human Capital in Urban Management

Application of Gene Expression Programming to water dissolved oxygen concentration prediction

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Volume 2, Issue 1
Winter 2017
Pages 39-48

Application of Gene Expression Programming to water dissolved oxygen concentration prediction

References

References

Send comment about this article

Volume 2, Issue 1Winter 2017Pages 39-48

Volume 2, Issue 1
Winter 2017
Pages 39-48