{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T11:38:02Z","timestamp":1781350682221,"version":"3.54.1"},"reference-count":59,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,11,12]],"date-time":"2021-11-12T00:00:00Z","timestamp":1636675200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Foundation of the Educational Department of Liaoning Province","award":["lnqn202018"],"award-info":[{"award-number":["lnqn202018"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Machine learning (ML) as a powerful data-driven method is widely used for mineral prospectivity mapping. This study employs a hybrid of the genetic algorithm (GA) and support vector machine (SVM) model to map prospective areas for Au deposits in Karamay, northwest China. In the proposed method, GA is used as an adaptive optimization search method to optimize the SVM parameters that result in the best fitness. After obtaining evidence layers from geological and geochemical data, GA\u2013SVM models trained using different training datasets were applied to discriminate between prospective and non-prospective areas for Au deposits, and to produce prospectivity maps for mineral exploration. The F1 score and spatial efficiency of classification were calculated to objectively evaluate the performance of each prospectivity model. The best model predicted 95.83% of the known Au deposits within prospective areas, occupying 35.68% of the study area. The results demonstrate the effectiveness of the GA\u2013SVM model as a tool for mapping mineral prospectivity.<\/jats:p>","DOI":"10.3390\/ijgi10110766","type":"journal-article","created":{"date-parts":[[2021,11,11]],"date-time":"2021-11-11T23:02:41Z","timestamp":1636671761000},"page":"766","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Mapping Mineral Prospectivity Using a Hybrid Genetic Algorithm\u2013Support Vector Machine (GA\u2013SVM) Model"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6107-5407","authenticated-orcid":false,"given":"Xishihui","family":"Du","sequence":"first","affiliation":[{"name":"School of Transportation Engineering, Shenyang Jianzhu University, Shenyang 110168, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kefa","family":"Zhou","sequence":"additional","affiliation":[{"name":"Xinjiang Research Center for Mineral Resources, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China"},{"name":"Xinjiang Key Laboratory of Mineral Resources and Digital Geology, Urumqi 830011, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yao","family":"Cui","sequence":"additional","affiliation":[{"name":"British Columbia Geological Survey, Victoria, BC V8W 9N3, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinlin","family":"Wang","sequence":"additional","affiliation":[{"name":"Xinjiang Research Center for Mineral Resources, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China"},{"name":"Xinjiang Key Laboratory of Mineral Resources and Digital Geology, Urumqi 830011, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuguang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Xinjiang Research Center for Mineral Resources, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China"},{"name":"Xinjiang Key Laboratory of Mineral Resources and Digital Geology, Urumqi 830011, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1007\/s11053-019-09564-8","article-title":"Prospectivity mapping for Tungsten Polymetallic mineral resources, Nanling Metallogenic Belt, South China: Use of random forest algorithm from a perspective of data imbalance","volume":"29","author":"Li","year":"2020","journal-title":"Nat. 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