Machine Learning for Criteria Weighting in GIS-Based Multi-Criteria Evaluation: A Case Study of Urban Suitability Analysis

Geographic Information System-based Multi-Criteria Evaluation (GIS-MCE) methods are designed to assist in various spatial decision-making problems using spatial data. Deriving criteria weights is an important component of GIS-MCE, typically relying on stakeholders' opinions or mathematical methods. These approaches can be costly, time-consuming, and prone to subjectivity or bias. Therefore, the main objective of this study is to investigate the use of Machine Learning (ML) techniques to support criteria weight derivation within GIS-MCE. The proposed ML-MCE method is explored in a case study of urban development suitability analysis of the City of Kelowna, Canada. Feature importance values drawn from three ML techniques-Random Forest (RF), Extreme Gradient Boosting (XGB), and Support Vector Machine (SVM)-are used to derive criteria weights. The suitability scores obtained using the ML-MCE methodology are compared with Equal-Weights (EW) and the Analytical Hierarchy Process (AHP) approach for criteria weighting. The results indicate that ML-derived criteria weights can be used in GIS-MCE, where RF and XGB techniques provide more similar values for criteria weights than those derived from SVM. The similarities and differences are confirmed with Kappa indices obtained from comparing pairs of suitability maps. The proposed new ML-MCE methodology can support various decision-making processes in urban land-use planning.