Optimizing solar panel placement using fuzzy OWA–BWM approach

Selecting reliable sites for solar-panel installation remains challenging because most GIS-based multi-criteria decision-making approaches produce a single deterministic suitability map and therefore do not reveal how site priorities change under different levels of risk acceptance. This study proposes a risk-sensitive GIS framework that integrates fuzzy spatial standardization, the Best–Worst Method (BWM), and Fuzzy Ordered Weighted Averaging (Fuzzy OWA) for solar-panel site selection across six climatically distinct regions. The methodological innovation lies in combining the relatively consistent and comparison-efficient criterion weighting of BWM with OWA aggregation applied to ordered BWM-weighted fuzzy values under multiple α-based decision scenarios. This integration generates conservative, intermediate, and risk-tolerant suitability maps and distinguishes spatially stable priority zones from locations that become suitable only when greater compensation among criteria is permitted. Nine climatic, topographic, geological, and accessibility criteria were evaluated. Solar radiation received the highest importance, but the final suitability patterns were also strongly influenced by road accessibility, slope aspect, distances to rivers and faults, and regional climatic conditions. Estimated annual electricity generation ranged from 5544.99 to 17,663.66 kWh/year under the BWM scenario and from 3377.24 to 17,704.66 kWh/year under the OWA–BWM scenarios for the assumed effective panel areas. These differences demonstrate that risk attitude can materially alter both the extent of suitable land and the associated energy-production potential. ROC validation produced AUC values of 0.721–0.971, indicating moderate to excellent discrimination among suitable and unsuitable locations, while also showing that model reliability varies among climatic regions. The framework therefore improves preliminary decision quality by supporting the identification of robust low-risk sites, prioritization of field verification, and comparison of alternative development scenarios. Potential environmental benefits, including reduced soil-surface exposure and improved soil-moisture retention, are interpreted as post-selection co-benefits.