School of Business, Central South University, Changsha, 410083, People's Republic of China.
School of Business, Hunan University, Changsha, 410082, People's Republic of China.
Environ Monit Assess. 2021 Mar 25;193(4):223. doi: 10.1007/s10661-021-08992-5.
To monitor and manage water environments, China developed a centralized multi-level administrative system where governments and agencies at each level are responsible for water quality within their regions. In this case, regional water quality assessment has become a critical issue. However, as a complex multi-criteria decision making (MCDM) problem, it faces many challenges such as diverse implement indicator framework, complicated indicator interrelations, and lack of reliable assessment methods. Therefore, this paper constructs a novel multistage decision support framework for regional water quality assessment. In phase I, we determine indicator framework strictly according to the national standards, involving PH, dissolved oxygen (DO), chemical oxygen demand (COD), etc., totally 21 water quality indicators where the temperature indicator is excluded due to its lack of assessment standard. In addition, considering the matching between the characteristic of water quality data and the probabilistic linguistic term set (PLTS) technique, we employ PLTS theory to process massive monitoring data. In phase II, relative weight considering indicators' interrelationship is produced by the proposed regression-based decision-making trial and evaluation laboratory (DEMATEL) method, and further forms combined weight by balancing single-factor weight. In phase III, we present a new PLTS measure and extend the fuzzy technique for order performance by similarity to ideal solution (FTOPSIS) method to generate assessment results. Then, we investigate water quality status of 16 administrative districts in Shanghai, China, with the proposed method. The collected data are derived from 26 water quality monitoring sites and covers the period during September 2018 to February 2019. The results confirm a hypothesis that the statistically significant interrelationship does exist among indicators, and point out that Huang Pu District remains the best water quality with highest values of [Formula: see text] in the range of (0.79-0.85) over the 6 months. Moreover, the parameter analysis and comparative analysis are further given that verifies the robustness and reliability of the model in details.
为了监测和管理水环境，中国建立了集中的多层次行政管理系统，各级政府和机构负责其所在地区的水质。在这种情况下，区域水质评估已成为一个关键问题。然而，作为一个复杂的多准则决策（MCDM）问题，它面临着许多挑战，如多样的实施指标框架、复杂的指标相互关系以及缺乏可靠的评估方法。因此，本文构建了一个用于区域水质评估的新型多阶段决策支持框架。在第一阶段，我们根据国家标准严格确定指标框架，涉及 PH、溶解氧（DO）、化学需氧量（COD）等，共 21 个水质指标，其中由于缺乏评估标准，温度指标被排除在外。此外，考虑到水质数据的特征与概率语言术语集（PLTS）技术的匹配，我们采用 PLTS 理论处理大量监测数据。在第二阶段，通过提出的基于回归的决策试验和评价实验室（DEMATEL）方法生成考虑指标相互关系的相对权重，并通过平衡单因素权重进一步形成组合权重。在第三阶段，我们提出了一种新的 PLTS 度量方法，并扩展了模糊技术用于相似理想解决方案的排序（FTOPSIS）方法，以生成评估结果。然后，我们使用所提出的方法研究了中国上海 16 个行政区的水质状况。所收集的数据来自 26 个水质监测站点，涵盖了 2018 年 9 月至 2019 年 2 月的时间段。结果证实了一个假设，即指标之间确实存在统计上显著的相互关系，并指出黄浦区在 6 个月内保持着最佳水质，[Formula: see text]的值最高，范围在（0.79-0.85）之间。此外，还进行了参数分析和比较分析，进一步验证了模型的稳健性和可靠性。
