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

Yu-Ting Bai1, Bai-Hai Zhang2, Xiao-Yi Wang3

  • 1School of Automation, Beijing Institute of Technology, Beijing 100081, China. byting@bit.edu.cn.

Sensors (Basel, Switzerland)
|November 2, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a new group decision-making method to select the best algal bloom remediation approach using water quality sensor data and fuzzy logic. It addresses the limitations of subjective decision-making in water pollution control.

Keywords:
Vague setalgal bloom remediationgroup decision makingwater environment management

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Area of Science:

  • Environmental Science
  • Water Resource Management
  • Decision Science

Background:

  • Algal blooms, a result of eutrophication, degrade water quality and pose risks to public health.
  • Current remediation approach selection often relies on subjective data, neglecting valuable sensor information.
  • Limited research exists on the decision-making process for selecting effective algal bloom remediation strategies.

Purpose of the Study:

  • To develop a novel group decision-making method for selecting optimal algal bloom remediation approaches.
  • To integrate water quality sensor data with fuzzy evaluation for rational decision-making.
  • To address the subjectivity and data limitations in existing remediation selection methods.

Main Methods:

  • Developed an optimal similarity aggregation model for group opinions using modified Vague value similarity.
  • Quantified remediation approach effectiveness on water quality indexes via Vague evaluation.
  • Analyzed water quality sensor data to determine grey relational degrees between approaches and water status.
  • Applied the selection model to real-world algal bloom remediation scenarios in lakes.

Main Results:

  • The proposed method effectively integrates diverse data sources, including sensor readings and fuzzy evaluations.
  • Demonstrated the rationality and feasibility of the decision-making model in selecting appropriate remediation strategies.
  • Successfully matched alternative remediation approaches to specific water conditions based on data analysis.

Conclusions:

  • The novel group decision-making method offers a data-driven and objective approach to algal bloom remediation.
  • This approach enhances the selection process by utilizing water quality sensor data and fuzzy logic.
  • The model provides a practical tool for improving water security and public health by addressing eutrophication effectively.