A Modular Design Principle for GIS-Based Soft Computing Decision Support Systems with Controlled Uncertainty
Keywords:
Modular Decision Support Systems, GIS-Based Decision Support, Soft Computing, Fuzzy Analytic Hierarchy Process (FAHP), Multi-Criteria Decision Analysis (MCDA), Weighted Linear Combination (WLC), TOPSIS, Sensitivity AnalysisAbstract
Contemporary GIS-based decision support systems frequently combine soft computing and multi-criteria decision analysis techniques; however, their effectiveness is often compromised by methodological overlap, uncontrolled uncertainty propagation, and improper coupling of spatial evaluation with decision ranking. This paper formalizes a set of modular design principles for GIS-based soft computing decision support systems that enforce strict analytical role separation across weighting, spatial modelling, aggregation, and decision stages. Uncertainty is intentionally confined to the criteria-weighting process using the Fuzzy Analytic Hierarchy Process, while spatial suitability is derived through a constrained Weighted Linear Combination to preserve spatial consistency. Decision prioritization is performed exclusively at the alternative level using the Technique for Order Preference by Similarity to Ideal Solution, thereby avoiding pixel-level decision distortion. Sensitivity analysis is restricted to the final decision stage to evaluate robustness without altering spatial outcomes. The proposed principles are instantiated within a generalized system architecture and validated using municipality-scale geospatial and planning data. The results demonstrate improved transparency, reproducibility, and stability of decision outcomes. By elevating system design from ad-hoc integration to principled modularization, this work provides a transferable foundation for robust GIS-based decision support in urban planning, environmental management, and sustainability-oriented applications.
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