LAUSR

The main objective of this research is to model the uncertainty associated with GIS-based multi-criteria decision analysis (MCDA) for crop suitability assessment. To achieve this goal, an integrated approach using GIS-MCDA in association with Monte Carlo simulation (MCS) and global sensitivity analysis (GSA) were applied for Saffron suitability mapping in East-Azerbaijan Province in Iran. The results of this study indicated that integration of MCDA with MCS and GSA could improve modeling precision by reducing data variance. Results indicated that applying the MCS method using the local training data leads to computing the spatial correlation between criteria weights and characteristics of the study area. Results of the GSA method also allow us to obtain the priority of criteria and identify the most important criteria and the variability of outputs under uncertainty conditions for model inputs. The findings showed that, commonly used primary zoning methods, without considering the interaction effects of variables, had significant errors and uncertainty in the output of MCDA-based suitability models, which should be minimized by the advanced complementarity of sensitivity and uncertainty analysis.