{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T20:27:57Z","timestamp":1782937677986,"version":"3.54.5"},"reference-count":39,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T00:00:00Z","timestamp":1722988800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Harran University Scientific Research Projects Coordination Office","award":["21074"],"award-info":[{"award-number":["21074"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In soil science, the allocation of soil samples to their respective origins holds paramount significance, as it serves as a crucial investigative tool. In recent times, with the increasing use of proximal sensing and advancements in machine-learning techniques, new approaches have accompanied these developments, enhancing the effectiveness of soil utilization in soil science. This study investigates soil classification based on four parent materials. For this purpose, a total of 59 soil samples were collected from 12 profiles and the vicinity of each profile at a depth of 0\u201330 cm. Surface soil samples were analyzed for elemental concentrations using X-Ray fluorescence (XRF) and inductively coupled plasma\u2013optical emission spectrometry (ICP-OES) and soil spectra using a visible near-infrared (Vis-NIR) spectrometer. Soil samples collected from soil profiles (12 soil samples) and surface (47 soil samples) were used to classify parent materials using machine learning-based algorithms such as Support Vector Machine (SVM), Ensemble Subspace k-Near Neighbor (ESKNN), and Ensemble Bagged Trees (EBTs). Additionally, as a validation of the classification techniques, the dataset was subjected to five-fold cross-validation and independent sample set splitting (80% calibration and 20% validation). Evaluation metrics such as accuracy, F score, and G mean were used to evaluate prediction performance. Depending on the dataset and algorithm used, the classification success rates varied between 70% and 100%. Overall, the ESKNN (99%) produced better results than other classification methods. Additionally, Relief algorithms were employed to identify key variables for each dataset (ICP-OES: CaO, Fe2O3, Al2O3, MgO, and MnO; XRF: SiO2, CaO, Fe2O3, Al2O, and MnO; Vis-NIR: 567, 571, 572, 573, and 574 nm). Subsequent soil reclassification using these reduced variables revealed reduced accuracies using Vis-NIR data, with ESKNN still yielding the best results.<\/jats:p>","DOI":"10.3390\/s24165126","type":"journal-article","created":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T12:13:14Z","timestamp":1723119194000},"page":"5126","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Machine Learning-Based Classification of Soil Parent Materials Using Elemental Concentration and Vis-NIR Data"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9740-0013","authenticated-orcid":false,"given":"Y\u00fcsra","family":"\u0130nci","sequence":"first","affiliation":[{"name":"Organized Industrial Zone Vocational School, Harran University, Sanliurfa 63300, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4727-8283","authenticated-orcid":false,"given":"Ali Volkan","family":"Bilgili","sequence":"additional","affiliation":[{"name":"Department of Soil Science and Plant Nutrition, Faculty of Agriculture, Harran University, Sanliurfa 63300, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Recep","family":"G\u00fcndo\u011fan","sequence":"additional","affiliation":[{"name":"Department of Soil Science and Plant Nutrition, Faculty of Agriculture, Harran University, Sanliurfa 63300, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gafur","family":"G\u00f6z\u00fckara","sequence":"additional","affiliation":[{"name":"Department of Soil Science and Plant Nutrition, Eskisehir Osmangazi University, Eskisehir 26160, T\u00fcrkiye"},{"name":"Department of Soil Science, University of Wisconsin-Madison, Madison, WI 53706, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5167-4054","authenticated-orcid":false,"given":"Kerim","family":"Karada\u011f","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics, Faculty of Engineering, Harran University, Sanliurfa 63300, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mehmet Emin","family":"Tenekeci","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Engineering, Harran University, Sanliurfa 63300, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.scijus.2010.10.006","article-title":"Characterization of soils from the Algarve region (Portugal): A multidisciplinary approach for forensic applications","volume":"51","author":"Guedes","year":"2011","journal-title":"Sci. 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