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• GIS-MCDA identifies optimal wind-solar sites in Koung-khi, Cameroon. • Nineteen geospatial criteria modeled for site suitability analysis. • Hydro-geomorphic areas show 75% high suitability with APT-HG. • AHP and TOPSIS emerge as the most impactful MCDA methods. • Study enables precise site selection for sustainable energy systems. The hybridization of renewable energy sources has proven to be an innovative technological approach to ensuring energy supply in certain regions of the world. However, it requires a careful and optimized selection of the installation site to fully harness the available energy resources. Within the present framework, the paper investigates and develops a geospatial-based parametric multi-criteria decision algorithm (GIS-MCDA), i.e., TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation), and AHP (Analytic Hierarchy Process), which is applied to determine the most suitable locations for the installation of wind-solar hybrid renewable energy systems in a sub-area (Koung-Khi) of the West Region of Cameroon. Nineteen geospatial criteria factors were employed in the study, namely: aspect, distance to roads, distance to drainage networks, drainage density, elevation, electricity distribution, geologic/rock type, illumination angle (hillshed), land cover, pedologic/soil type, population habitat type, slope gradient, solar radiation areas, stream power index, sediment transport index, suburban and rural areas, terrain roughness index, topographic wetness index, and wind speed between 1,300 and 2,000 meters of altitude. The nineteen criteria factors were modeled and analyzed through the integration of three parameter categories: hydro-geomorphic, landscape environment and topography, and socio-economic, using Multiple-criteria decision-making methods (MCDM). The results demonstrated that the hydro-geomorphic (HG) parameter, particularly with AHP, revealed 35% of the area as highly suitable and 40% as moderately suitable, while the integrated APT (AHP + PROMETHEE + TOPSIS)-HG model showed an even higher high-suitability rate of 75%, largely driven by the strong influence of geological and soil characteristics. For the landscape environment and topography (LET) parameter, high and moderate suitability areas remained consistent across all MCDM methods, averaging 25% and 40% respectively, with variations mainly linked to solar radiation and wind speed. The socio-economic (SE) parameter showed even more favorable results, with high and extra suitability areas averaging 44% and 45%, and the APT-SE integration reaching a remarkable 80% of the area in high suitability, double that of individual models, reflecting growing sub-urbanization, limited grid electricity, and evolving land use. The final integration of all three parameters (APT-HG-LET-SE) revealed that more than 50% of the entire study area is highly suitable for hybrid renewable energy systems, while unsuitable areas were restricted to valleys and sharply inclined slopes. The findings confirm the effectiveness of the integrated GIS-MCDM approach in capturing the multi-dimensional nature of site suitability. Notably, the APT hybrid models consistently outperformed individual methods in identifying broader and more reliable zones of high suitability. Across parameters, the synergy between the criteria weights and geospatial context drove robust differentiation of potential energy sites. The results also highlight that high-altitude, stable, and accessible terrains in the study area offer the best opportunities for implementation. This indicates a strong match between methodological reliability and real-world site potential, reinforcing the feasibility of decentralized renewable energy deployment in such mountainous regions.
Mbufua et al. (Wed,) studied this question.