Analytic hierarchy process vs. machine learning: Mapping school suitability in Batna city, Algeria with spatial analysis

The present study considers the suitability of sites for elementary schools in Batna, Algeria. The Analytic Hierarchy Process (AHP) method is compared to the three machine learning (ML) models, namely Random Forest (RF), Gradient Tree Boosting (GTB) and Classification and Regression Trees (CART). Spatial analysis techniques based on Geographic Information System (GIS) such as K-Nearest Neighbors (KNN), Moran?s I, inverse distance weighting (IDW) and Buffer Analysis were conducted prior to comparison to detect principal deficiencies and showed that 44.6 % of the 83 schools are located in 52% of the residential sectors. Peripheral gaps were also highlighted Nearest Neighbors Ratio (NNR = 0.69, Moran?I = 0.32, p <0.001). AHP prioritized population density: 51.99%, while RF with an Area Under the Curve (AUC = 0.75) emphasized environmental limitations (slope, river spacing), differing from GTB (AUC = 0.65) and CART (AUC = 0.61). The combined suitability maps showed complementarity and guide planners to effectively address Batna's educational inequalities.