Dempster-shafer theory for handling conflict in hydrological data: Case of snow water equivalent

Studying uncertainties in hydrological modeling is necessary because of data scarcity or abundance and quality issues. These uncertainties can have significant effects on environmental decision making. Traditionally, probabilistic methods have been used to study uncertainties; however, recently, more comprehensive methods are used in the treatment of uncertainty. These methods are capable of addressing uncertainty in the form of vagueness, ambiguity, and conflict, which cannot be studied efficiently using probabilistic frameworks. The Dempster-Shafer theory of evidence (DST) is one of the popular methods that can provide a unified platform to address data conflict and incompleteness. In this paper, the use of DST to model and propagate the uncertainty arising from two snow water equivalent data sets with a high degree of conflict (DST conflict k=0.74) is demonstrated. In DST, on the basis of the nature of data, e.g.,the degree of conflict, different combination rules are applicable. Here, four DST combination rules are applied including Dempster-Shafer, Yager, mixture, and the proportional conflict redistribution rule number 6 (PCR6). The outcomes from these rules are compared, and their effects on subsequent decision-making are discussed. Considering the specific condition of the data used, i.e.,high-conflict data with limited quality information, results indicate that mixture and PCR6 rules are more appropriate. The resultant uncertainty-driven data set is subsequently used as input into an illustrative hydrologic model demonstrating a method for propagating uncertainty. In addition, the issues of resolving conflict for less contradicting data sets, the dependency between bodies of evidence, and modeling incompleteness are also discussed. © 2012 American Society of Civil Engineers.