A new weighting factor in combining belief function

• Published in: PloS one Vol. 12; no. 5; p. e0177695
• Main Authors: Zhou, Deyun, Pan, Qian, Chhipi-Shrestha, Gyan, Li, Xiaoyang, Zhang, Kun, Hewage, Kasun, Sadiq, Rehan
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 25.05.2017; Public Library of Science (PLoS)
• Subjects: Agricultural management; Agriculture; Agronomy; Animal models; Artificial intelligence; Bayesian analysis; Canals; Case studies; Catchments; Classification; Coefficients; Cognitive ability; Computer and Information Sciences; Computers; Convergence; Decision making; Decision support systems; Diagnosis; Earth Sciences; Ecology and Environmental Sciences; Electronics; Engineering; Engineering and Technology; Engineering schools; Entropy; Environmental assessment; Environmental impact; Environmental impact assessment; Environmental management; Environmental Monitoring; Environmental Pollution - prevention & control; Expert systems; Fault diagnosis; Functions (mathematics); Indicators; Influence; Information science; Intelligence; Land pollution; Land use; Mathematical models; Measurement; Mechanical engineering; Municipal wastewater; New combinations; Number theory; Physical Sciences; Pollution effects; Pollution monitoring; Preservation; Probability; Probability theory; Quality control; Remote sensors; Risk analysis; River water quality; Rivers; Rivers - chemistry; Science; Seasonal variations; Sewage; Social Sciences; Statistical analysis; Studies; Surface water; Systems engineering; Target recognition; Transformation; Wastewater; Wastewater management; Water monitoring; Water pollution; Water Quality; Water quality management; Water quality monitoring; Weighting (Statistics)
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0177695

Dempster-Shafer evidence theory has been widely used in various applications. However, to solve the problem of counter-intuitive outcomes by using classical Dempster-Shafer combination rule is still an open issue while fusing the conflicting evidences. Many approaches based on discounted evidence and weighted average evidence have been investigated and have made significant improvements. Nevertheless, all of these approaches have inherent flaws. In this paper, a new weighting factor is proposed to address this problem. First, a modified dissimilarity measurement is proposed which is characterized by both distance and conflict between evidences. Second, a measurement of information volume of each evidence based on Deng entropy is introduced. Then two kinds of weight derived from aforementioned measurement are combined to obtain a new weighting factor and a weighted average method based on the new weighting factor is proposed. Numerical examples are used to illustrate the validity and effectiveness of the proposed method. In the end, the new method is applied to a real-life application of river water quality monitoring, which effectively identify the major land use activities contributing to river pollution.