A Novel Group Decision-Making Method Based on Sensor Data and Fuzzy Information

Clicks: 194
ID: 265967
2016
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Abstract
Algal bloom is a typical phenomenon of the eutrophication of rivers and lakes and makes the water dirty and smelly. It is a serious threat to water security and public health. Most scholars studying solutions for this pollution have studied the principles of remediation approaches, but few have studied the decision-making and selection of the approaches. Existing research uses simplex decision-making information which is highly subjective and uses little of the data from water quality sensors. To utilize these data and solve the rational decision-making problem, a novel group decision-making method is proposed using the sensor data with fuzzy evaluation information. Firstly, the optimal similarity aggregation model of group opinions is built based on the modified similarity measurement of Vague values. Secondly, the approaches’ ability to improve the water quality indexes is expressed using Vague evaluation methods. Thirdly, the water quality sensor data are analyzed to match the features of the alternative approaches with grey relational degrees. This allows the best remediation approach to be selected to meet the current water status. Finally, the selection model is applied to the remediation of algal bloom in lakes. The results show this method’s rationality and feasibility when using different data from different sources.
Reference Key
bai2016sensorsa Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yu-Ting Bai;Bai-Hai Zhang;Xiao-Yi Wang;Xue-Bo Jin;Ji-Ping Xu;Ting-Li Su;Zhao-Yang Wang;Bai, Yu-Ting;Zhang, Bai-Hai;Wang, Xiao-Yi;Jin, Xue-Bo;Xu, Ji-Ping;Su, Ting-Li;Wang, Zhao-Yang;
Journal sensors
Year 2016
DOI
10.3390/s16111799
URL
https://doi.org/10.3390/s16111799
Keywords
group decision making vague set water environment management algal bloom remediation

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