Quantifying uncertainty in Pareto fronts arising from spatial data

Multi-objective spatial optimization problems require data input that can contain uncertainties. Via the validation of constraints and computation objective values this uncertainty propagates to Pareto fronts. Here, we develop a method quantify in fronts by finding extreme lower upper bound range optimal space, i.e. interval. The is demonstrated on land use allocation problem with initial (for objectives constraints) soil fertility one objective) as uncertain data. intervals resulting from were wide irregularly shaped, whereas ones narrow regularly shaped. Furthermore, some objective-space regions, patterns remained relatively stable under uncertainty, while elsewhere they clouded. This information be used select solutions robust uncertainty.