Abstract

Identifying key factors and analyzing their causal relationships significantly enhance decision-making effectiveness in site selection. Although numerous studies have applied Multi-Criteria Decision-Making (MCDM) methods to site selection, these traditional approaches often overlook or inadequately represent causal interdependencies among factors. This study addresses these limitations by utilizing open data for transparency and employing Bayesian Networks (BN) as a robust probabilistic modeling alternative. BNs effectively represent complex factor interactions, capturing both causal relationships and uncertainties. Experimental evaluations demonstrate that the proposed framework effectively calculates final site suitability probabilities by explicitly considering hierarchical dependencies, offering enhanced decision-making insights.