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

Home Articles List Article Information

|
|
|
Export Citation
RIS EndNote BibTeX APA MLA Harvard Vancouver
|
Share

Abstracting and Indexing

International Journal of Human Capital in Urban Management

pISSN 2476-4698
eISSN 2476-4701

Articles in Press

Current Issue

Journal Archive

Volume 2 (2017)

Issue 2

Issue 1

Volume 1 (2016)

Facts & Figures
Publication starting date	2016
h-index	2
Citations	14
Issue per year	4
No. of volumes	1
No. of issues	4
No. of articles	32
No. of review papers	1
No. of research papers	19
No. of case studies	10
No. of short communications	2
No. of pages	314
No. of references	1036
No. of contributors	86
No. of databases	25

Application of Gene Expression Programming to water dissolved oxygen concentration prediction

Article 4, Volume 2, Issue 1, Winter 2017, Page 39-48 PDF (220 K)

Document Type: ORIGINAL RESEARCH PAPER

DOI: 10.22034/ijhcum.2017.02.01.004

Authors

V. Mehdipour

¹; M. Memarianfard¹; F. Homayounfar²

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

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

Receive Date: 15 October 2016, Revise Date: 08 November 2016, Accept Date: 28 December 2016

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

Dissolved oxygen (DO); Electrical Conductivity (EC); Eymir Lake; Gene expression programming (GEP); Water quality

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.