{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T01:38:25Z","timestamp":1780537105443,"version":"3.54.1"},"reference-count":98,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,7,26]],"date-time":"2022-07-26T00:00:00Z","timestamp":1658793600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>An assessment of site suitability for wind and solar plants is a strategic step toward ensuring a low-cost, high-performing, and sustainable project. However, these issues are often handled on a local scale using traditional decision-making approaches that involve biased and non-generalizable weightings. This study presents a global wind and solar mapping approach based on eXplainable Artificial Intelligence (XAI). To the best of the author\u2019s knowledge, the current study is the first attempt to create global maps for siting onshore wind and solar power systems and formulate novel weights for decision criteria. A total of 13 conditioning factors (independent variables) defined through a comprehensive literature review and multicollinearity analysis were assessed. Real-world renewable energy experiences (more than 55,000 on-site wind and solar plants worldwide) are exploited to train three machine learning (ML) algorithms, namely Random Forest (RF), Support Vector Machine (SVM), and Multi-layer Perceptron (MLP). Then, the output of ML models was explained using SHapley Additive exPlanations (SHAP). RF outperformed SVM and MLP in both wind and solar modeling with an overall accuracy of 90% and 89%, kappa coefficient of 0.79 and 0.78, and area under the curve of 0.96 and 0.95, respectively. The high and very high suitability categories accounted for 23.2% (~26.84 million km2) of the site suitability map for wind power plants. In addition, they covered more encouraging areas (24.0% and 19.4%, respectively, equivalent to ~50.31 million km2) on the global map for hosting solar energy farms. SHAP interpretations were consistent with the Gini index indicating the dominance of the weights of technical and economic factors over the spatial assessment under consideration. This study provides support to decision-makers toward sustainable power planning worldwide.<\/jats:p>","DOI":"10.3390\/ijgi11080422","type":"journal-article","created":{"date-parts":[[2022,7,26]],"date-time":"2022-07-26T22:03:42Z","timestamp":1658873022000},"page":"422","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Global Spatial Suitability Mapping of Wind and Solar Systems Using an Explainable AI-Based Approach"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7679-0596","authenticated-orcid":false,"given":"Mourtadha Sarhan","family":"Sachit","sequence":"first","affiliation":[{"name":"Department of Civil Engineering and Geospatial Information Science Research Center (GISRC), Faculty of Engineering, Universiti Putra Malaysia (UPM), Serdang 43400, Malaysia"},{"name":"Department of Civil Engineering, College of Engineering, University of Thi-Qar, Nasiriyah 64001, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Helmi Zulhaidi Mohd","family":"Shafri","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering and Geospatial Information Science Research Center (GISRC), Faculty of Engineering, Universiti Putra Malaysia (UPM), Serdang 43400, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmad Fikri","family":"Abdullah","sequence":"additional","affiliation":[{"name":"Institut Antarabangsa Akuakultur dan Sains Akuatik (I-AQUAS), Universiti Putra Malaysia (UPM), Si Rusa 71050, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Azmin Shakrine Mohd","family":"Rafie","sequence":"additional","affiliation":[{"name":"Department of Aerospace Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM), Serdang 43400, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6465-6231","authenticated-orcid":false,"given":"Mohamed Barakat A.","family":"Gibril","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering and Geospatial Information Science Research Center (GISRC), Faculty of Engineering, Universiti Putra Malaysia (UPM), Serdang 43400, Malaysia"},{"name":"GIS & Remote Sensing Center, Research Institute of Sciences and Engineering, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"117167","DOI":"10.1016\/j.energy.2020.117167","article-title":"Multi-Criteria Decision-Making Model for Assessment of Large Photovoltaic Farms in Brazil","volume":"197","author":"Rediske","year":"2020","journal-title":"Energy"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"117996","DOI":"10.1016\/j.apenergy.2021.117996","article-title":"High-Resolution Data Shows China\u2019s Wind and Solar Energy Resources Are Enough to Support a 2050 Decarbonized Electricity System","volume":"306","author":"Li","year":"2022","journal-title":"Appl. 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