کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8845476 | 1617113 | 2018 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
An improved approach for water quality evaluation: TOPSIS-based informative weighting and ranking (TIWR) approach
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موضوعات مرتبط
علوم زیستی و بیوفناوری
علوم کشاورزی و بیولوژیک
بوم شناسی، تکامل، رفتار و سامانه شناسی
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چکیده انگلیسی
A great deal of effort has been made on the development of approaches based on Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Little attention is, however, paid to how to couple water quality indicators and their officially-defined standards with consideration of inter-correlation among indicators when TOPSIS is applied for evaluating water quality. This study proposes an improved TOPSIS-based approach called the Informative Weighting and Ranking (TIWR) approach. It couples water quality indicators and associated standards over the entire process and considers inter-correlation among indicators using the Criteria Importance Through Inter-criteria Correlation (CRITIC) approach. The approach is applied to the water quality evaluations of the Shitoumenkou reservoir and the Lake Tai. Results suggest that it produces a delicate level Hi associated with water quality for an object/monitoring site, which avoids classifying several objects into the same typical level and makes them distinguishable. The TIWR approach agrees well with traditional approach when a level Hi is transformed to a typical level. In addition, it can avoid some unreasonable results obtained by traditional approach. These findings have implications for decision makers and researchers in applying the TIWR approach in water environment protection and management.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Ecological Indicators - Volume 89, June 2018, Pages 356-364
Journal: Ecological Indicators - Volume 89, June 2018, Pages 356-364
نویسندگان
Zhenya Li, Tao Yang, Ching-Sheng Huang, Chong-Yu Xu, Quanxi Shao, Pengfei Shi, Xiaoyan Wang, Tong Cui,