Fuzzy sets in pattern recognition: accomplishments and challenges
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
New similarity measures of intuitionistic fuzzy sets and application to pattern recognitions
Pattern Recognition Letters
Optimal consensus of fuzzy opinions under group decision making environment
Fuzzy Sets and Systems
Similarity measures on intuitionistic fuzzy sets
Pattern Recognition Letters
A new similarity measure of generalized fuzzy numbers and its application to pattern recognition
Pattern Recognition Letters
Evaluating Sensor Reliability in Classification Problems Based on Evidence Theory
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy risk analysis based on similarity measures of generalized fuzzy numbers
IEEE Transactions on Fuzzy Systems
Assessment of E-Commerce security using AHP and evidential reasoning
Expert Systems with Applications: An International Journal
Knowledge-Based Systems
A biologically inspired solution for fuzzy shortest path problems
Applied Soft Computing
A new method to determine basic probability assignment from training data
Knowledge-Based Systems
Supplier selection using AHP methodology extended by D numbers
Expert Systems with Applications: An International Journal
Environmental impact assessment based on D numbers
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Contaminant intrusion in a water distribution network is a complex but a commonly observed phenomenon, which depends on three elements - a pathway, a driving force and a contamination source. However, the data on these elements are generally incomplete, non-specific and uncertain. In an earlier work, Sadiq, Kleiner, and Rajani (2006) have successfully applied traditional Dempster-Shafer theory (DST) to estimate the ''risk'' of contaminant intrusion in a water distribution network based on limited uncertain information. However, the method used for generating basic probability assignment (BPA) was not very flexible, and did not handle and process uncertain information effectively. In this paper, a more pragmatic method is proposed that utilizes ''soft'' computing flexibility to generate BPAs from uncertain information. This paper compares these two methods through numerical examples, and demonstrates the efficiency and effectiveness of modified method.