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.
Highlights
Decision-making is the core of management and has been widely applied in the economy, industry and engineering [1,2,3,4]
The current remediation approaches to algal bloom are divided into three types: physical, chemical and biological methods
The alternative set is constituted of remediation approaches that are common both in the theoretical research and practical application
Summary
Decision-making is the core of management and has been widely applied in the economy, industry and engineering [1,2,3,4] It usually depends on administrators’ knowledge and experience but increasingly relies on objective data and information. Decision-making includes the process of presenting questions, setting goals, and designing and selecting approaches, while in the narrow sense, it means selecting the best approach among several alternatives [6]. This is usually achieved by the classic multi-attribute decision-making method. The multi-attribute method can solve the irrational analysis problem
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