Enhancement of Spatial-MCDA Using Machine Learning: Integration of FAHP and ANN Model

The increasing complexity of spatial decision-making tasks necessitates robust analytical models capable of addressing uncertainty, subjectivity, and large datasets. This study proposes an integrated Fuzzy Analytical Hierarchy Process–Artificial Neural Network (FAHP-ANN) model to enhance the interpretability and computational capacity of Multi-Criteria Decision Analysis (MCDA). The FAHP component captures human judgment through triangular fuzzy numbers to reflect ambiguity in expert assessments, while the ANN component leverages these weights to learn complex patterns across criteria and alternatives. A structured methodological framework is developed, incorporating preference aggregation, ReLU activation functions, Net Flow Score computation, and threshold-based classification for decision support. Monte Carlo simulation and sensitivity analysis are employed to evaluate model stability and robustness. Results demonstrate that the FAHP-ANN model significantly outperforms conventional ANN models in both accuracy (reduction in prediction error) and consistency, particularly under fuzzy environments. The proposed model presents a scalable and interpretable decision-support tool suitable for applications in spatial planning, infrastructure prioritization, and policy evaluation, effectively bridging the gap between human-centric reasoning and data-driven analytics.