Comparative evaluation and comparison of quality monitoring network of Iranʼs rivers with selected countries

Document Type : Research Paper


1 Department of Water Engineering and Management ,Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran

2 Department of Water Engineering and Management, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran


Evaluating the monitoring network and determining the main criteria in the stations is an effective step in improving the efficiency of the monitoring and measurement network. Consecutive assessment, pathology and optimization in long-term monitoring; Leads to proper management of output data, system efficiency and reduces costs. Of course, limiting factors such as: growing needs, limited resources, the consequences of unsustainable development and occasional human interference in the water cycle, etc., cause concern in meeting the water needs of human society. In this study, the evaluation of the quality monitoring network of the country's rivers was evaluated using a multi-criteria decision-making method and by selecting four main criteria. In management and technical criteria in water monitoring stations, data quality control and assurance sub-criteria, in social criteria sub-criteria of water quality parameters, in economic criteria sub-criteria of pollution detection and in environmental criteria sub-criteria of network optimization at a specific time and place have the most weight. Also, a comparative comparison of Iran's monitoring network with the monitoring network of selected countries was performed using Spearman correlation coefficient method. The Iranian monitoring network is most consistent with the Thai monitoring network with a coefficient of 0.9. One of the most important priorities of the US Monitoring Network is to optimize monitoring at specific times and places. Japan's monitoring network focuses more on regional monitoring than grade 1 rivers.


Main Subjects

Comparative evaluation and comparison of quality monitoring network of Iranʼs rivers with selected countries




The extraction of reliable data is one of the important issues in the monitoring of river quality stations. Also, one of the environmental concerns is surface water pollution and underground water discharges. In order to achieve the objectives of the study, it is possible to examine the challenges of Iran's quality monitoring network using the multi-criteria decision-making method. In general, the purpose of this article is to review several issues, including: evaluating the performance of the quality monitoring stations of the country's rivers, comparative comparison of the quality monitoring stations of the country's rivers with selected countries, evaluation of the quality monitoring stations of the country's rivers using the multi-criteria decision-making method and finally the statistics of points The weaknesses and challenges of the quality monitoring network of Iranian rivers using the multi-criteria decision-making method and providing the necessary suggestions to solve the problems and solve the challenges. After examining and prioritizing the sub-criteria for Iran's monitoring network, the status of Iran's monitoring network compared to the monitoring network of other countries was investigated.

Materials and Methods:

At the beginning of this section, Iran was examined as the study area, and using the multi-criteria decision-making method, the criteria and sub-criteria of the Iranian network were prioritized using a questionnaire and Super Decision software. Then, in order to make a comparative comparison, criteria were determined according to the global conditions and standards, and to raise the scientific level of the paper, countries were selected for comparative comparison in order to select the closest global standard for the monitoring network. This prioritization was done using Superman's correlation coefficient method.


By using four criteria and 41 sub-criteria, the highest weight has been obtained for two managerial and technical and environmental criteria, the value of which is 0.390. Then the economic criterion with a weight of 0.152 and finally the social criterion with the lowest weight of 0.068 have been obtained in the model. In general, among the four specified criteria, the greatest effect is obtained for the sub-criterion of control and assurance of data quality in the managerial and technical criteria, and the least importance is for the sub-criterion of social growth and culture building in the social criterion.


 This article has been studied with the aim of evaluating and comparing the quality monitoring network of Iranian rivers with selected countries. The greatest importance has been obtained for managerial and technical criteria. Then the environmental criteria and finally the economic and social criteria are stated. Finally, in order to apply the results of the research, the following prioritization from the most important to the least important was presented from the ANP method for Iran's monitoring network:

The sub-criterion of data quality control and assurance

Detection of pollution

Optimizing the monitoring network at a specific time and place consecutively

Strengthening the quantity of data or time (frequency) in order to increase the effectiveness of data in scientific progress and necessary exploitation at the level of scientific and research centers of the country

Regarding the results of the comparative comparison, the following can be mentioned:

The highest degree of correlation between Iran's monitoring network and Thailand's monitoring network has been obtained.

In the US monitoring network, water quality parameters and monitoring optimization plan are very important. But in Iran, this sub-criterion is the fifth priority.

The German monitoring network considers the effect of sharing statistics and data to be important.

In its guidelines, the Japan Monitoring Network is based on the belief that monitoring should be done regionally with respect to Grade 1 rivers.

Antonie, S., and Durate, S. (1997). “Stochastic judgment in the AHP: the measurements of rank reversal, Decision Science” Journal of the decision sciences institute.(3) 28, 0011-7315.
Aragones, P., Aznar, J., Ferries, J., and Garica, M. (2006). “Valuation of urban industrial land: an analytical network process approach” European journal of operation research, Elsevier, vol. 185(1), pages 322-339, February.
       Ahuja, S. (2013). Monitoring water quality , pollution assessment, and remediation to assure sustainability: Monitoring Water Quality Amsterdam. Elsevier 2013:14-18.
Adu-Manu, K. S., Tapparello, G., Heinzelman, W., Katsriku, F. A., Abdulai, J-D. (2017). Water quality monitoring using wireless sensor network: Current trends and future research direction. ACM Trans sens Netw (TOSN) 2017; 13(1)-41
Boyacioglu, H., Boyacioglu, H and Gunduz, O. (2005). Application of factory analysis in the assessment of surface water quality in Buyuk Menderes river basin. Journal of European water. (EWRA). 10: 43-49.
Cude, C. (2001). Oregon water quality index: A tool for evaluating water quality management effectiveness. J Am water resource as 37:125–137.
Evalution report unda project (2012). “Water quality in central asia” united nations economic commision for erope in cooperation with the regional environmental centre centre for central asia (carec). almaty, 2018
Evangelos, T. (2000). “ Ph.D. multi- criteriadecision making” : Theory and applications. MDPI.
Gangopadhyay, S., Gupta, A.D., Nachabe , M.H., (2001). “Evaluation of ground water monitoring network by principal component analysis”. Ground Water. 39,181-191.
Gibbons. J. D.(1971). Non parametric statistical in fernce- Mc Graw- Hill, New York.
Guidance manual for optimizing water quality monitoring program design (2015). Canadian Council of Ministers of the Environment, PN 1543 ISBN 978-1-77202-020 PDF
Jiang, J., Tang, S., Han, D., Fu, G., Solomatine, D., Zheng, Y. (2020). A comprehensive review on the design and optimization of surface water quality monitoring network, Envirom Model Softw 2020: 104792.
Khan, S., and Faisal, M. N. (2007). An analytical network process model for municipal solid waste disposal option, Waste management, xx: pp. 6-15.
Khodamoradi Vatan, N., Mazaheri, M., Mohammadoli Samani, J. (2021). Evaluating the performance of the quality monitoring network of the country's rivers. Water and Irrigation Management, 11 (3), 541-559. doi: 10.22059 / jwim.2021.327850.906
Kolpin, D. W., Furlong, E. T., Meyer, M. T., Thrman, E. M., Zaugg, S. D., Barber, L. B., (2002). Pharmaceuticals, Hormanes and other organic waste water contaminants in U. S. streams, 1999-2000: a National Reconnaissance, Environ Sci Technol, Vol. 36, No. 6, PP. 1202-1211.
Noori, R., Kerachian, R., Darban, A.K., Shakibaienia, A., (2007). Assessment of importance of water quality monitoring stations using principal components analysis and factor analysis: a case study of the Karoon river. Water and Wastewater 18 (3), 60–69(In Persian). 
Noori, R.,  Karbassi, A., Khakpour, A., Shahbazbegian, M., Mohammadi Khalf Badam, H., Vesali-Naseh, M. (2012).  Chemometric Analysis of Surface Water Quality Data: Case Study of the Gorganrud River Basin, Iran.
Ning, S. K., Chang, N. B., (2004). Multi-objective, decision based assessment of a water quality monitoring in a river system. Journal of Environmental, Vol 4. No. 1, PP. 121-126.
Outlook of Water Environmental Management Strategies in Asia Copyright © 2009 Ministry of the Environment, Japan, All rights reserved. ISBN: 978-4-88788-052-8
Pasika, S., Gandla, S. T. (2020). Smart water quality monitoring system with cost- effective using IoT. Heliyon 2020; 6(7): e04096.
Razavi Toosi, S. L., Mohammavali Samani, J., and Koorehpazan Dezfuli, A. (2010). Ranking water transfer projects using fuzzy methods. Proceeding of the Institotion of civil Engineers(ice) water management 163 Appri; 2010 Issue WM4, 189-197.
Razavi Tusi, S. L., Mohammavali Samani J. (2013). Management Prioritization of a number of catchments in the country using a new hybrid algorithm based on (ANP) TOPSIS-ANP fuzzy network analysis process methods. (In Persian)
Saaty, T. L. (1996). Decision making with dependence and feedback: the analytic network process , RWS publications Pittsburgh.
Saaty, T.L. (1999). Fundamentals of the analytic network process, ISAHP 1999, Kobe, Japan, August, pp. 12-14.
Saaty, T.L.  and Luis G. Vargas, (2006). Decision Making With The Analytic Network Process, Springer Science, New York, USA.
Saaty.T. L. and Vargas, L . G.  (2006). The analytic hierarchy process: wash criteria should not be ignored. International Journal of Management and Decision Making. 188-180,7.
Stream gages, USGS-US. (2017). Gedogical Survey Federal priority. USGS Federal Priorty Stream gages (FPS). Retrieved April 30.
Sanders, T.G., Ward, R. C., Loftis, J. C., Steel, T. D., Adrian, D. D., Yevjevich, V. (1987). Decision of network in a large river system using the Genetic Algorithm. Ecological modeling. Vol.199, 289-297.
Strobl, R. O., Robillard, P. D., Shannon, R. D., Day, R. L., Mc-Donnell, A. J. A. (2006). Water quality monitoring network design methodology for the selection of critical sampling point: Part I: Environmental monitoring and assessment, Vol. 112. No. 1-3, PP. 137-158.
Sojka, M., Siepak, M., Ziola, A., Frankowski, M., Murat- Blazejewska, S. and Siepak, J. (2007). Application of multivariate statistical techniques to evaluation of water quality in the Mala Welna river, (Western Poland). Journal of Environmental monitoring Assessment 147: 159- 170.
Tanhai,V and., And Rostami Kashki, N. (2019). Mahabad river water quality monitoring in terms of microbiological parameters based on protocol 1.11 of the National Standards Organization of Iran. Journal of Cellular, Molecular Biological News, Volume 8, Number 31 - Summer 2019.
Tians, S., Wang, Z. and. Shang. H. (2011). Study on the self- purification of Juma River. Procedia Environmental Sciences. 11.PP: 1328- 1333.
Unified Interior Regional Boundaries. (2020). Retried July 30.
USGS National Water-quality Assessment. (2017). (NAWQI) Program. Retrieved April 30.
Veerabhadram, K. (2009)."Mapping of water quality index (WQI) using Geographical Information System (GIS) as a decision supporting system tool. Department of Environmental Studies, College of Engineering GITAM, Visakhapatnam, 530 045, India."
Yolthantham, T. (2007). Water Quality and Water Quality Situation in Thailand. Oral Presentation Proceedings: The 3rd WEPA International Symposium on Water Environmental Governance, Ministry of the Environment Japan.