Abstract
As one of the important natural resources, water resources are related to the future survival and development of human beings. In recent years, the problem of water resources security has become more and more prominent, and it is urgent to study water resources security. Taking 30 provinces and cities in China as an example, this paper constructs a water resources security index system from three aspects of resource supply, social economy and ecological environment, and innovatively proposes a new evaluation method. Specifically, considering the influence of subjective and objective factors on the weighting of indicators, the best–worst method (BWM) and the criteria importance through intercriteria correlation (CRITIC) method are used to determine the subjective and objective weights of each indicator, and the two weights are unified in combination with the principle of maximizing variance. After that, the combined weight was applied to the technique for order preference by similarity to ideal solution (TOPSIS) method, and a new fuzzy multiple criteria decision-making (MCDM) method of combined weighting was finally developed to evaluate the water security status in China. This avoids the error of the traditional single-weight method evaluation to a certain extent, and also conducts other relevant empirical analysis. The research method developed in this paper provides a new idea for dealing with multi-criteria optimization problems in complex systems, and the research results provide a theoretical basis for ensuring regional water resources security and promoting coordinated development among regions.







Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Didovets, I., Lobanova, A., Bronstert, A., Snizhko, S., Maule, C., Krysanova, V.: Assessment of climate change impacts on water resources in three representative Ukrainian catchments using eco-hydrological modelling. Water 9(3), 204 (2017)
Gunda, T., Hess, D., Hornberger, G.M., Worland, S.: Water security in practice: the quantity–quality–society nexus. Water Secur. 6, 100022 (2019)
Huang, Z., Liu, J., Mei, C., Wang, H., Shao, W.: Water security evaluation based on comprehensive index in Jing-Jin-Ji District, China. Water Supply 20(7), 2698–2714 (2020)
Tang, L., Zhang, W., Liu, Z., Qi, Y.: Evaluation of water security based on capacity for socio-economic regulation. Water Supply 21(3), 1036–1049 (2021)
Yao, J., Wang, P., Wang, G., Shrestha, S., Xue, B., Sun, W.: Establishing a time series trend structure model to mine potential hydrological information from hydrometeorological time series data. Sci. Total Environ. 698, 134227 (2020)
Grey, D., Sadoff, C.W.: Sink or swim? Water security for growth and development. Water Policy 9(6), 545–571 (2007)
Bin, O.U., Shuyan, F.U., Yu, W., Liping, W.: The comprehensive evaluation of rural drinking water security in Yunnan Province. Procedia Earth Planet. Sci. 5, 155–158 (2012)
Li, X.S., Peng, Z.Y., Li, T.T.: An evaluation index system of water security in China based on macroeconomic data from 2000 to 2012. IOP Conf. Ser. Earth Environ. Sci. 39, 012045 (2016)
Liu, K.K., Li, C.H., Cai, Y.P., Xu, M., Xia, X.H.: Comprehensive evaluation of water resources security in the Yellow River Basin based on a fuzzy multi-attribute decision analysis approach. Hydrol. Earth Syst. Sci. Discuss. 11(1), 371–410 (2014)
Su, Y., Gao, W., Guan, D.: Integrated assessment and scenarios simulation of water security system in Japan. Sci. Total Environ. 671, 1269–1281 (2019)
Shao, W., Liu, H., Wang, H., Liu, J., Yan, D., Li, W., Zhou, J., Wang, H.: Evaluation of regional water security in China and recommendations for counter measures. Arab. J. Geosci. 13(3), 107 (2020)
Wang, X., Chen, Y., Li, Z., Fang, G., Wang, Y.: Development and utilization of water resources and assessment of water security in Central Asia. Agric. Water Manag. 240, 106297 (2020)
Baradaran, V., Ghorbani, E.: Development of fuzzy exploratory factor analysis for designing an e-learning service quality assessment model. Int. J. Fuzzy Syst. 22(6), 1772–1785 (2020)
Lin, C.M., Huynh, T.T.: Function-link fuzzy cerebellar model articulation controller design for nonlinear chaotic systems using TOPSIS multiple attribute decision-making method. Int. J. Fuzzy Syst. 20(6), 1839–1856 (2018)
Fei, L., Deng, Y., Hu, Y.: Hu: DS-VIKOR: a new multi-criteria decision-making method for supplier selection. Int. J. Fuzzy Syst. 21(1), 157–175 (2019)
Zavadskas, E.K., Turskis, Z., Tamošaitiene, J.: Risk assessment of construction projects. J. Civ. Eng. Manag. 16(1), 33–46 (2010)
Mayag, B., Grabisch, M., Labreuche, C.: A characterization of the 2-additive Choquet integral through cardinal information. Fuzzy Sets Syst. 184(1), 84–105 (2011)
Rezaei, J.: Best–worst multi-criteria decision-making method. Omega 53, 49–57 (2015)
Asadabadi, M.R., Chang, E., Zwikael, O., Saberi, M., Sharpe, K.: Hidden fuzzy information: requirement specification and measurement of project provider performance using the best–worst method. Fuzzy Sets Syst. 383, 127–145 (2020)
Keshavarz Ghorabaee, M.K., Zavadskas, E.K., Amiri, M., Esmaeili, A.: Multi-criteria evaluation of green suppliers using an extended WASPAS method with interval type-2 fuzzy sets. J. Clean. Prod. 137, 213–229 (2016)
Tuş, A., Aytaç Adalı, E.A.: The new combination with CRITIC and WASPAS methods for the time and attendance software selection problem. OPSEARCH 56(2), 528–538 (2019)
Kolagar, M.: Adherence to urban agriculture in order to reach sustainable cities; a BWM–WASPAS approach. Smart Cities 2(1), 31–45 (2019)
Su, W., Zhang, L., Zeng, S., Jin, H.: A fuzzy-social network multi-criteria group decision-making framework for selection of renewable energy project: a case of China. Int. J. Fuzzy Syst. (2021). https://doi.org/10.1007/s40815-021-01193-9
Akram, M., Adeel, A.: TOPSIS approach for MAGDM based on interval-valued hesitant fuzzy N-soft environment. Int. J. Fuzzy Syst. 21(3), 993–1009 (2019)
Alazemi, F.K.A., Ariffin, M.K.A.B.M., Mustapha, F.B., Supeni, E.E.B.: A new fuzzy TOPSIS-based machine learning framework for minimizing completion time in supply chains. Int. J. Fuzzy Syst. (2022). https://doi.org/10.1007/s40815-021-01226-3
Samanlioglu, F., Taskaya, Y.E., Gulen, U.C., Cokcan, O.: A fuzzy AHP–TOPSIS-based group decision-making approach to IT personnel selection. Int. J. Fuzzy Syst. 20(5), 1576–1591 (2018)
Gupta, P., Mehlawat, M.K., Grover, N.: A generalized TOPSIS method for intuitionistic fuzzy multiple attribute group decision making considering different scenarios of attributes weight information. Int. J. Fuzzy Syst. 21(2), 369–387 (2019)
Shaverdi, M., Ramezani, I., Tahmasebi, R., Rostamy, A.A.A.: Combining fuzzy AHP and fuzzy TOPSIS with financial ratios to design a novel performance evaluation model. Int. J. Fuzzy Syst. 18(2), 248–262 (2016)
Chen, T.Y., Li, C.H.: Determining objective weights with intuitionistic fuzzy entropy measures: a comparative analysis. Inf. Sci. 180(21), 4207–4222 (2010)
Sun, L.Y., Miao, C.L., Yang, L.: Ecological–economic efficiency evaluation of green technology innovation in strategic emerging industries based on entropy weighted TOPSIS method. Ecol. Indic. 73, 554–558 (2017)
Huang, W., Shuai, B., Sun, Y., Wang, Y., Antwi, E.: Using entropy-TOPSIS method to evaluate urban rail transit system operation performance: the China case. Transp. Res. A 111, 292–303 (2018)
Gu, T., Ren, P., Jin, M., Wang, H.: Tourism destination competitiveness evaluation in Sichuan Province using TOPSIS model based on information entropy weights. Discrete Contin. Dyn. Syst 12(4&5), 771 (2019)
García, F., Guijarro, F., Moya, I.: A goal programming approach to estimating performance weights for ranking firms. Comput. Oper. Res. 37(9), 1597–1609 (2010)
Chen, P.: Effects of the entropy weight on TOPSIS. Expert Syst. Appl. 168, 114186 (2021)
Deng, F., Li, Y., Lin, H., Miao, J., Liang, X.: A BWM–TOPSIS hazardous waste inventory safety risk evaluation. Int. J. Environ. Res. Public Health 17(16), 5765 (2020)
Gupta, H.: Assessing organizations performance on the basis of GHRM practices using BWM and fuzzy TOPSIS. J. Environ. Manag. 226, 201–216 (2018)
Tu, Y., Chen, K., Wang, H., Li, Z.M.: Regional water resources security evaluation based on a hybrid fuzzy BWM–TOPSIS method. Int. J. Environ. Res. Public Health 17(14), 4987 (2020)
Liu, H., Jia, Y., Niu, C., Gan, Y., Xu, F.: Evaluation of regional water security in China based on dualistic water cycle theory. Water Policy 20(3), 510–529 (2018)
Yao, J., Wang, G., Xue, B., Xie, G., Peng, Y.: Identification of regional water security issues in China, using a novel water security comprehensive evaluation model. Hydrol. Res. 51(5), 854–866 (2020)
Acknowledgements
This work was supported by the National Social Science Foundation of China (Grant No. 21CTJ024), The Humanities and Social Sciences Program of the Ministry of Education (Grant No. 20YJC790193), National Natural Science Foundation of China (Grant No. 71934001), and Higher Education Institutions in Anhui Province of China (Grant No. KJ2020A0006).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zhou, Y., Tao, W. & Song, M. Regional Water Resource Security in China Based on a New Fuzzy Method with Combination Weighting. Int. J. Fuzzy Syst. 24, 3584–3601 (2022). https://doi.org/10.1007/s40815-022-01298-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40815-022-01298-9