Today, a wide range of spatial analysis models are used in environmental risk zoning. Some models, such as hierarchical and fuzzy analyzes, despite the inclusion of uncertainty in the input variables, are unable to explain quantitatively the output uncertainty. In this study, the aim of evaluating the capabilities of the Dempster-Schaeffer algorithm is to explain the uncertainty in the outcomes for landslide hazard zonation in the south of Chalus. Therefore, after field studies and review of similar studies, a map of 10 factors was provided in the GIS environment and was introduced as input data along with a map of the distribution of landslides to the model. Landslide hazard zonation was performed by integrating different weights in the Dempster-Sheffer model and in order to evaluate the output of the model, a logistic regression model was used; the performance of the two models was based on the output results of the models and using two indicators of the density ratio (Dr) And the sum of utility (Qs) was evaluated and verified. The results of Dr showed that both models had good performance in identifying high-risk classes compared to low risk classes. Based on the Qs index, the Dempster-Schafer model with QS = 98/2 was good compared to Logistic regression model with QS = 91/66 has a better relative utility. Therefore, the D-S model is more successful in identifying risk classes (finiteness) and consequently hazard classes (uncertainty) in the region.
