A novel GeoAI-based multidisciplinary model for SpatioTemporal Decision-Making of utility-scale wind–solar installations: To promote green infrastructure in Iraq

[Display omitted] The dual use of wind and solar energy holds great promise for low-cost and high-performance green infrastructure. However, for such hybrid systems to operate successfully, comprehensive and simultaneous dimensional planning is required, a goal that single-perspective assessment approaches fail to attain. This paper proposes a novel SpatioTemporal Decision-Making (STDM) model based on Geospatial Artificial Intelligence (GeoAI) for the optimal allocation of onshore wind-solar hybrid plants, with application on a national scale in Iraq. To this end, a wide range of 21 evaluative and restrictive spatial criteria were covered. The temporal synergy factor between renewable resources was considered for the first time in this type of study. Unique global weightings for decision factors were derived using Random Forest (RF) and SHapley Additive exPlanations (SHAP) algorithms supported by sample inventories of wind and solar plants worldwide. Finally, weighted linear combination (WLC) and fuzzy overlay techniques were harnessed in a GIS environment for spatiotemporal suitability mapping of energy systems. According to the RF-SHAP model, the techno-economic criteria demonstrated substantial contributions to the placement of wind and solar systems compared with the socio-environmental criteria. The spatiotemporal suitability map identified three promising opportunities for Iraq at South Dhi-Qar, East Wasit, and West Diyala, with total areas of 780, 2166, and 649 km2, respectively. We anticipate that our findings will encourage government agencies, decision-makers, and stakeholders to increase funding for clean energy transition initiatives.