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Journal of Environmental Accounting and Management
António Mendes Lopes (editor), Jiazhong Zhang(editor)
António Mendes Lopes (editor)

University of Porto, Portugal


Jiazhong Zhang (editor)

School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, China

Fax: +86 29 82668723 Email:

Assessment of the Water Quality of Karun River Catchment Using Artificial Neural Networks-self-Organizing Maps and K-Means Algorithm

Journal of Environmental Accounting and Management 9(1) (2021) 43--58 | DOI:10.5890/JEAM.2021.03.005

Mehdi Ahmadmoazzam$^{1,2}$, Yaser Tahmasebi Birgani$^{1,3}$ , Mohsen Molla-Norouzi$^{4,5}$, Maryam Dastoorpour$^6$

$^1$ Department of Environmental Health Engineering, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

$^2$ Student Research Committee, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, IR Iran

$^3$ Environmental Technologies Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

$^4$ MSc in environmental health engineering, Shahid Beheshti University of Medical Sciences, School of public health, department of environmental health engineering

$^5$ Ministry of Energy, Tehran Province Water and Wastewater Company

$^6$ Assistant Professor of Epidemiology, Air Pollution and Respiratory Diseases Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

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Analyzing the water quality status is one of the most important issues to control the pollution discharged to the river. This helps to obtain the effective environmental management. However, the conventional water quality indices cannot accurately indicate the water quality status, particularly in the presence of the various water quality parameters. In this study 15, water quality parameters of Karun River were categorized based on the similar variation pattern by Self-Organizing Map (SOM) for a 6-year period between 2013 and 2018. Data were then clustered using the k-means nonhierarchical algorithm for determination of the spatio-seasonal pattern of Karun River's water quality and discrimination of the sources of water pollution all along Karun River catchment. The results indicated the dissimilar variation patterns of some water quality parameters which were might due to the different sources of pollutions around the Karun River catchment. Moreover, data clustering by k-means specified 5 different groups collected data. Spatio-seasonal assessment identified the critical stations in the Karun River during a 6-year period. The result of the applied methodology showed an integration of SOM and k-means algorithms gives an insight into the similarities of water quality parameters and analysis of the water quality conditions in critical locations. Therefore, it can be used as a tool for efficient decision making in environmental management.


The present study was financially supported by Ahvaz Jundishapur University of Medical Sciences (Grant no: ETRC-9601). The authors are sincerely grateful to the Khuzestan Water and Power Authority for providing the required data of Karun River. The authors have declared no conflicts of interest.


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