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


Citation

Sachit, Mourtadha Sarhan and Mohd Shafri, Helmi Zulhaidi and Abdullah, Ahmad Fikri and Mohd Rafie, Azmin Shakrine and A. Gibril, Mohamed Barakat (2024) A novel GeoAI-based multidisciplinary model for SpatioTemporal Decision-Making of utility-scale wind–solar installations: to promote green infrastructure in Iraq. Egyptian Journal of Remote Sensing and Space Science, 27 (1). pp. 120-136. ISSN 1110-9823; eISSN: 2090-2476

Abstract

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.


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Additional Metadata

Item Type: Article
Divisions: Faculty of Engineering
International Institute of Aquaculture and Aquatic Science
DOI Number: https://doi.org/10.1016/j.ejrs.2024.02.001
Publisher: Elsevier B.V.
Keywords: GeoAI; GIS; Solar energy; Spatiotemporal assessment; Wind energy
Depositing User: Ms. Che Wa Zakaria
Date Deposited: 27 Mar 2025 07:29
Last Modified: 27 Mar 2025 07:29
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1016/j.ejrs.2024.02.001
URI: http://psasir.upm.edu.my/id/eprint/116388
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