A Novel Approach for Modelling Uncertainty and Sensitivity in GIS Environment

GIS based Multicriteria Decision Analysis (MCDA) methods are increasingly being used as effective methodology for environmental assessment and modelling. However, the uncertainties associated with MCDA techniques are rarely studied. Such uncertainties may have significant impacts on the results, which may sometimes lead to inaccurate outcomes and undesirable consequences. Within this paper, three different GIS-MCDA methods are analyzed for landslide susceptibility mapping in the Urmia lake basin. In terms of sensitivity and uncertainty analyses, the Monte Carlo Simulation, as well as the Dempster-Shafer Theory methods are employed. Our research methodology consist two stages. The first-stage analysis makes use of the GIS-MCDA in conjunction with the Monte Carlo Simulation leading to support the second-stage of the uncertainty analysis reliability assessment using the Dempster-Shafer Theory. The results of this study reveal that through the integration of GIS and MCDA models, it is possible to identify strategies of choosing an appropriate method for landslide susceptibility mapping. Furthermore, results of this study indicate that the integration of MCDA and Monte Carlo Simulation can significantly improve the accuracy of the results.