Extending a Consensus-based Fuzzy Ordered Weighting Average (FOWA) Model in New Water Quality Indices

Mohammad Ali Baghapour, Mohammad Reza Shooshtarian

Abstract


In developing a specific WQI (Water Quality Index), many quality parameters are involved with different levels of importance. The impact of experts’ different opinions and viewpoints, current risks affecting their opinions, and plurality of the involved parameters double the significance of the issue. Hence, the current study tries to apply a consensus-based FOWA (Fuzzy Ordered Weighting Average) model as one of the most powerful and well-known Multi-Criteria Decision- Making (MCDM) techniques to determine the importance of the used parameters in the development of such WQIs which is shown with an example. This operator has provided the capability of modeling the risks in decision-making through applying the optimistic degree of stakeholders and their power coupled with the use of fuzzy numbers. Totally, 22 water quality parameters for drinking purposes were considered in this study. To determine the weight of each parameter, the viewpoints of 4 decision-making groups of experts were taken into account. After determining the final weights, to validate the use of each parameter in a potential WQI, consensus degrees of both the decision makers and the parameters are calculated. The highest and the lowest weight values, 0.999 and 0.073 respectively, were related to Hg and temperature. Regarding the type of consumption that was drinking, the parameters’ weights and ranks were consistent with their health impacts. Moreover, the decision makers’ highest and lowest consensus degrees were 0.9905 and 0.9669, respectively. Among the water quality parameters, temperature (with consensus degree of 0.9972) and Pb (with consensus degree of 0.9665), received the highest and lowest agreement with the decision-making group. This study indicated that the weight of parameters in determining water quality largely depends on the experts’ opinions and approaches. Moreover, using the FOWA model provides results accurate and closer- to-reality on the significance of each of the water quality parameters. Thus, using this operator can be a precise and appropriate method to determine the parameters’ weights and importance in order to develop specific WQIs for drinking, industrial, and agricultural purposes.

Keywords


MCDM, FOWA Model, Consensus, Fuzzy Number, Water Quality Index

Full Text:

PDF XML

References


Effendi, H., River water quality preliminary rapid assessment using pollution index. Procedia Environmental Sciences, 2016. 33: p. 562-567.

Said, A., D.K. Stevens, and G. Sehlke, An innovative index for evaluating water quality in streams. Environmental management, 2004. 34(3): p. 406-414.

Al-Mashagbah, A.F., Assessment of surface water quality of king abdullah canal, using physico-chemical characteristics and water quality index, Jordan. Journal of Water Resource and Protection, 2015. 7(04): p. 339.

Nasseri, M., et al., Recognition and Spatial Mapping of Multivariate Groundwater Quality Index using Combined Fuzzy Method. Iran. J. water and waste 2011. 1: p. 82-93.

Nikoonahad, A., H. Moazed, and F. Kazembeigi, Comparing Inices for Selecting the Best Index for Karkheh Dam. Iran. J. water res, 2010. 4: p. 69-73.In persian.

Mohebbi, M.R., et al., Assessment of water quality in groundwater resources of Iran using a modified drinking water quality index (DWQI). Ecological Indicators, 2013. 30: p. 28-34.

Prakirake, C., P. Chaiprasert, and S. Tripetchkul, Development of specific water quality index for water supply in Thailand. Songklanakarin J. Sci. Technol, 2009. 31(1): p. 91-104.

Boyacioglu, H., Development of a water quality index based on a European classification scheme. Water Sa, 2007. 33(1).

.H, M. and A. .H, A New Consensus-based Fuzzy Group Decision-Making Algorithm Case Study: Groundwater Resource Management. Iran-Water Resources Research, 2008. 4(2): p. 1- 13.In persian.

Karbassi, A. and F.M.M. Hosseini, Development of Water Quality Index (WQI) for River Water Quality Assessment (Case study: Gorganrood River ), in National Conference on Water Flow and Pollution2012, Water Institiution: University of Tehran, Iran. p. 1-10.In persian.

Kohanestani, Z., R. Ghorbani, and A. Fazel, Evaluation of water quality using TOPSIS method in the Zaringol Stream (Golestan Province, Iran). International Journal of Aquatic Biology, 2013. 1(5): p. 202-208.

Gharibi, H., et al., Development of a dairy cattle drinking water quality index (DCWQI) based on fuzzy inference systems. Ecological Indicators, 2012. 20: p. 228-237.

Ocampo-Duque, W., et al., Assessing water quality in rivers with fuzzy inference systems: A case study. Environment International, 2006. 32(6): p. 733-742.

Semiromi, F.B., et al., Water quality index development using fuzzy logic: A case study of the Karoon River of Iran. African Journal of Biotechnology, 2011. 10(50): p. 10125-10133.

Ardakanian, R. and M. Zarghami, Managing Water Resources Development Projects. first ed. Vol. 1. 2010, Tehran: Jihad Daneshgahi. 103.In persian.

Yetkin, M.E., et al., A fuzzy approach to selecting roof supports in longwall mining. South African Journal of Industrial Engineering, 2016. 27(1): p. 162-177.

Zarghami, M. and F. Szidarovszky. Group decision support system for ranking of water resources projects. in The 3rd International Conference on Water Resources and Arid Environments and the 1st Arab Water Forum, The King Fahd Cultural Center, Riyadh, Saudi Arabia. 2008.

Zarghami, M. and I. Ehsani, Evaluation of different Group Multi-Criteria Decision Making Methods in Selection of Water Transfer Projects to Urmia Lake Basin. Iran. J. Water Resources Research, 2011. 7(2): p. 1-14.In persian.




Iranian Journal of Health, Safety and Environment e-ISSN: :2345-5535 Iran university of Medical sciences, Tehran, Iran