A Multi-Criteria Generalised L-R Intuitionistic Fuzzy TOPSIS with CRITIC for River Water Pollution Classification
DOI:
https://doi.org/10.11113/mjfas.v19n6.3105Keywords:
Intuitionistic fuzzy numbers, L-R type, river pollution, CRITIC, TOPSISAbstract
A generalised L-R intuitionistic fuzzy numbers is an L-R intuitionistic fuzzy numbers that incorporates confidence level for both membership and non-membership functions. Therefore, this intuitionistic fuzzy number is suitable for classifying the river water pollution. This study aims to introduce the generalised L-R intuitionistic fuzzy numbers (GLRIFNs) which includes the membership and non-membership functions to classify the river water pollution using TOPSIS with CRITIC method. Due to the insufficient river data, this study has simulated the river data using the bootstrap method. This study had classified river water pollution for several rivers in Johor, Malaysia, namely Kim Kim River, Sayong River, Telor River, Pelepah River, and Bantang River from 2017 to 2021. The result shows that the Bantang River is the cleanest river, while the Kim Kim River is the most polluted river. The results proved that the GLRIFNs is quite a reliable method to classify river water pollution.
References
Liu, J., Li, Y., Huang, G. H. (2013). Mathematical modeling for water quality management under interval and fuzzy uncertainties. Journal of Applied Mathematics, 2013, 1-14.
Department of Environment Malaysia. (2020). Interim National Water Quality Standards for Malaysia 2019 [cited 2020 17 January]; Available from: https://www.doe.gov.my/portalv1/en/.
Chowdhury, M. S. U., Othman, F., Jaafar, W. Z. W., Mood, N. C., Adham, M. I. (2018). Assessment of pollution and improvement measure of water quality parameters using scenarios modeling for Sungai Selangor basin. Sains Malaysiana, 47(3), 457-69.
Rubio-Arias, H., Contreras-Caraveo, M., Quintana, R. M., Saucedo-Teran, R. A., Pinales-Munguia, A. 2012. An overall Water Quality Index (WQI) for a man-made aquatic reservoir in Mexico. International Journal of Environmental Research and Public Health, 9(5),1687-98.
Zhou Z, Zhang X, Dong W. (2013). Fuzzy comprehensive evaluation for safety guarantee system of reclaimed water quality. Procedia Environmental Sciences, 18, 227-35.
Du, Y-W., Wang, S-S., Wang, Y-M. (2019). Group fuzzy comprehensive evaluation method under ignorance. Expert Systems with Applications;126, 92-111.
Jin-yao, L., Yuan-feng, Q. (2003). Fuzzy comprehensive evaluation for decision making of water saving irrigation system. Wuhan University Journal of Natural Sciences, 8(3), 837-41.
Yang L. (2019). Fuzzy evaluation of water quality based on micronucleus technology of vicia faba root tip. Mathematical Problems in Engineering.
Zhu, L. H. (2010). Fuzzy Complex index in water quality assessment of municipalities. Journal of Water Resource and Protection, 02(09), 809-13.
Taherdoost, H. Madanchian M. (2023). Multi-criteria decision making (MCDM) methods and concepts. Encyclopedia, 3(1), 77-87.
Gautam, S. S., Singh, S., Singh, S. (2016). TOPSIS for multi criteria decision making in intuitionistic fuzzy environment. International Journal of Computer Applications, 156(8), 0975-8887.
Memari, A., Dargi, A., Jokar, M. R. A., Ahmad, R., Rahim, A. R. A. (2019). Sustainable supplier selection: A multi-criteria intuitionistic fuzzy TOPSIS method. Journal of Manufacturing Systems, 50, 9-24.
Daneshvar Rouyendegh, B., Yildizbasi, A., Ü. Z. (2018). Using intuitionistic fuzzy TOPSIS in site selection of wind power plants in Turkey. Advances in Fuzzy Systems, 2018, 1-14.
Rostamzadeh, R., Ghorabaee, M. K., Govindan, K., Esmaeili, A., Nobar, H. B. K. (2018). Evaluation of sustainable supply chain risk management using an integrated fuzzy TOPSIS-CRITIC approach. Journal of Cleaner Production, 175, 651-69.
Alipour-Bashary, M., Ravanshadnia, M., Abbasianjahromi, H., Asnaashari, E. (2022). Building demolition risk assessment by applying a hybrid fuzzy FTA and fuzzy CRITIC-TOPSIS framework. International Journal of Building Pathology and Adaptation, 40(1), 134-59.
Asante, D., Ampah, J. D., Afrane, S., Adjei-Darko, P., Asante, B., Fosu, E. et al. (2022). Prioritizing strategies to eliminate barriers to renewable energy adoption and development in Ghana: A CRITIC-fuzzy TOPSIS approach. Renewable Energy, 195, 47-65.
Zadeh, L. A. (1965). Fuzzy sets. Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers by Lotfi A Zadeh. World Scientific. 394-432.
Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87-96.
Guha, D., Chakraborty, D. (2010). A theoretical development of distance measure for intuitionistic fuzzy numbers. International Journal of Mathematics and Mathematical Sciences.
Dubois, D., Prade, H. (1978). Operations on fuzzy numbers. International Journal of Systems Science, 9(6), 613-26.
Klir, G., Yuan, B. (1995). Fuzzy sets and fuzzy logic. Prentice hall New Jersey.
Hwang, C-L., Yoon, K., Hwang, C-L., Yoon, K. (1981). Methods for multiple attribute decision making. Multiple attribute decision making: methods and applications a state-of-the-art survey. 58-191.
Diakoulaki, D., Mavrotas, G., Papayannakis, L. 1995. Determining objective weights in multiple criteria problems: The critic method. Computers & Operations Research, 22(7), 763-70.
Chang, H-J., Wu, C-H., Ho, J-F., Chen, P-y. (2008). On sample size in using central limit Theorem for Gamma distribution. Information and Management, 19(1), 153-74.
Efron, B. (1979). Bootstrap methods: another look at the jackknife. Springer.
Singh, K., Xie, M. (2008). Bootstrap: A statistical method. Unpublished manuscript, Rutgers University, USA Retrieved from http://www.statweb.rutgers.edu/mxie/RCPapers/bootstrap.pdf. 1-14.
Gonçalves, S., Kilian, L. (2007). Asymptotic and bootstrap inference for AR (∞) processes with conditional heteroskedasticity. Econometric Reviews, 26(6), 609-41.
Hušková, M., Kirch, C. (2012). Bootstrapping sequential change-point tests for linear regression. Metrika. 75(5), 673-708.
Lee, C-S., Wang, M-H. 2010. A fuzzy expert system for diabetes decision support application. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 41(1), 139-53.
Yuan, J., Li, C., Xu, F., Sun, B., Li, W. (2016). A group decision making approach in interval-valued intuitionistic hesitant fuzzy environment with confidence levels. Journal of Intelligent & Fuzzy Systems, 31(3), 1909-19.
Ahmad, S. A. S., Mohamad, D., Azman, N. I. (2020). A ranking procedure of Murabahah applications using fuzzy inferior ratio with generalized fuzzy numbers. AIP Conference Proceedings. AIP Publishing LLC.
Shah, M. F., Devi, V. (2019). Dozens rushed to hospital due to toxic fumes: The star online; 2019 [updated 8 March; cited 2019 September 26]. Available from: https://www.thestar.com.my/news/nation/2019/03/08/dozens-rushed-to-hospital-due-to-toxic-fumes.
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