Evaluation on water resources and water ecological security with 2-tuple linguistic information

Article type: Research Article

Authors: Song, Gang-Fu

Affiliations: North China University of Water Resources and Electric Power, Zhengzhou, Henan 450046, China | Tel./Fax: +86 0371 65790618; E-mail: songgangfu@163.com

Correspondence: [*] Corresponding author: North China University of Water Resources and Electric Power, Zhengzhou, Henan 450046, China. Tel./Fax: +86 0371 65790618; E-mail: songgangfu@163.com.

Abstract: China is one of the countries who face the most serious contradiction between water supply and demand. In the meantime, there is a serious water pollution problem. The effective use and pollution control of water resources are the key to solve these problems. The industry supports the national economic development and is also the main user of the water resource. In 2014, the industrial water use accounted for 24% of China’s total, which was 20.71% in 1998. The industry is the main source of water pollution at the same time. Therefore, in a condition that the industrial economy develops positively, it is necessary to perform water-saving management and sewage control to promote rational allocation of water resources and sewage treatment technology. The key solution to these works are to increase green total-factor water efficiency. Hence, industrial green total-factor water efficiency has great research value. In this paper, we investigate the multiple attribute decision making (MADM) problems with 2-tuple linguistic information. We used the 2-tuple linguistic weighted Bonferroni mean (2TLWBM) operator to develop a procedure for multiple attribute decision making under the 2-tuple linguistic environments. Finally, a practical example for evaluating the water resources and water ecological security is given to verify the developed approach and to demonstrate its practicality and effectiveness.

Keywords: Multiple attribute decision making (MADM), linguistic information, Bonferroni mean, geometric Bonferroni mean, 2-tuple linguistic weighted Bonferroni mean (2TLWBM) operator, water resources and water ecological security

DOI: 10.3233/KES-180393

Journal: International Journal of Knowledge-based and Intelligent Engineering Systems, vol. 23, no. 1, pp. 1-8, 2019