Group Decision-Making Support for Sustainable Governance of Algal Bloom in Urban Lakes

Abstract

Algal bloom is a typical pollution of urban lakes, which threatens drinking safety and breaks the urban landscape. It is pivotal to select a reasonable governance approach for sustainable management. A decision-making support method was studied in this paper. First, a general framework was designed to organize the rational decision-making processes. Second, quantitative calculation methods were proposed, including expert selection and opinion integration. The methods can determine the vital decision elements objectively and automatically. Third, the method was applied in Yuyuantan Lake in Beijing, China. The monitoring information and decision-making process are presented and the rank of governance alternatives is given. The comparison and discussion show that the group decision-making method is feasible and effective. It can assist the sustainable management of algal bloom.

A cohort study on the association between changing occupational stress, hair cortisol concentration, and hypertension

“…The original score of each subitem of occupational stress was transformed into a total score T score with a mean of 50 and a standard deviation of 10 (T = 50+10×(x-x)/s) [25], where x was the original score and s was the standard deviation), and this was used to evaluate the degree of occupational stress. The T value of each subitem of the occupational stress norm of technical workers was used to grade the degree of occupational stress, and the occupational stress of the research object was evaluated [25]. The study was divided into four corresponding grades according to the T score.…”

Hybrid Deep Learning Predictor for Smart Agriculture Sensing Based on Empirical Mode Decomposition and Gated Recurrent Unit Group Model

“…The GRU is trained by the gradient descent algorithm and the parameters are continually updated until convergence. The methods proposed in this paper can be applied to other fields, such as water environment prediction and management control systems [75], IoT intelligent systems [76][77][78], and wireless sensor networks [79][80][81].…”

Fuzzy Boost Classifier of Decision Experts for Multicriteria Group Decision-Making

“…e proposed classification method for the decision expert selection is verified with a decision-making case. In the previous studies [42,43], we have analyzed the decision-making of water pollution governance. For the pollution of algal bloom in the urban lakes, it is necessary to monitor and predict its trends by using some parameter estimation methods [44][45][46][47][48][49][50][51][52] such as the recursive algorithms [18,50,[53][54][55][56][57][58].…”

“…e collected expert information is stored in a structured datasheet. A total of 56 experts and scholars related to algal bloom management are collected [42]. Each expert is represented with 11 attribute variables that can reflect the academic level and professional experience.…”