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 PAPER

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

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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