Landslide is a type of natural hazards causing many casualties in mountainous and rainy areas. Therefore, recognizing areas those that have potentials for happening such type of hazards is an important task. For this, methods of landslide susceptibility mapping, categorized mainly into two general data- and knowledge-driven approaches, have been widely developed and applied. In this regard, stochastic and systemic errors, respectively, associated with adequacy in the number of known landslide locations and subjectivity of expert judgment applied to assign weights of landslide conditioning factors are two main issues affecting the data- and knowledge-driven approaches. These issues are, in fact, types of bias and uncertainties that adversely affect landslide susceptibility mapping practices. This paper aims to adapt continuous fuzzification and geometric average multi-criteria decisionmaking approaches to overcome the aforementioned disadvantages of the existing landslide susceptibility mapping methods. In the method proposed weights of landslide conditioning factors are continuously assigned without using known landslide locations as training points, and without using expert opinion in categorization of values of landslide conditioning factors into arbitrary classes and assigning subjective weights. We applied the procedure proposed on a dataset of Oshvand watershed, Hamadan Province, Iran, to demonstrate its effectiveness. The results demonstrated that the continuous weighting method applied is more reliable than the existing methods those which apply classified values of landslide conditioning factors.
