Sustainable land management under uncertainty: A hybrid Bayesian BWM and RPM framework for spatial prioritization

Sustainable land management is a complex problem .due to environmental constraints and decision-maker uncertainty. This study presents an integrated solution combining Geographic Information Systems (GIS), the Bayesian Best-Worst Method (Bayesian BWM), and the Ratio Product Model (RPM) to improve the accuracy of spatial prioritization in land-use decisions. The integration of the Bayesian BWM and RPM statistically minimizes the cognitive uncertainty in expert opinions whilst enabling the most suitable areas to be ranked with precision within their own categories, without any normalization constraints. The proposed model was developed for three main categories —residential, industrial, and agricultural— in the province of Balıkesir. The analysis results revealed that the ‘ease of access to services’ (0.5660) criterion was the most decisive factor for residential suitability, whilst ‘distance to power transmission lines’ (0.1322) and ‘average rainfall’ (0.1315) were the most decisive factors for industry and agriculture, respectively. The suitability maps show that the Bandırma-Susurluk corridor is strategically prioritized for industry, whilst the Manyas-Gönen basin is prioritized for agricultural production. RPM scores (0.2750 for settlement, 0.6036 for industry, and 0.3868 for agriculture) quantify implementation priorities, providing a reliable decision-support tool for managing uncertainty in spatial planning.