Digital Mapping of Soil pH and Electrical Conductivity: A Comparative Analysis of Kriging and Machine Learning Approaches

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dc.contributor.authorÖztürk, Mustafa
dc.contributor.authorKiliç, Miraç
dc.contributor.authorGünal, Hikmet
dc.date.accessioned2026-06-19T06:32:07Z
dc.date.available2026-06-19T06:32:07Z
dc.date.issued2024
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractSoil pH and electrical conductivity (EC) are critical soil properties influencing agricultural productivity and environmental sustainability. This study evaluates the performance of stacked machine learning models in predicting and mapping soil pH and EC values. Base models such as Ordinary Kriging (OK), Universal Kriging (UK), and Disjunctive Kriging (DK) were employed, and their outputs were integrated into a Multilayer Perceptron (MLP) neural network meta-model. The results reveal the superior performance of the MLP meta-model across all metrics. For instance, in predicting pH, the MLP model achieved an RMSE of 0.028, an MAE of 0.020, and an R2 of 0.858 on the training dataset. For EC predictions, the MLP model outperformed others on the test dataset, with an RMSE of 0.039, an MAE of 0.028, and an R2 of 0.900. In contrast, the UK and DK methods exhibited lower accuracy, particularly on test datasets. This study shows the advantage of modern machine learning algorithms in modeling nonlinear spatial relationships and their significant potential in digital soil mapping. The findings demonstrate the applicability of these approaches in enhancing agricultural productivity and supporting sustainable soil management practices. © 2024, Institute of Economic Development and Social Researches. All rights reserved.
dc.identifier.doi10.5281/zenodo.14542860
dc.identifier.endpage1185
dc.identifier.issn2757-5675
dc.identifier.issue4
dc.identifier.scopus2-s2.0-105037179754
dc.identifier.scopusqualityN/A
dc.identifier.startpage1168
dc.identifier.urihttps://doi.org/10.5281/zenodo.14542860
dc.identifier.urihttps://hdl.handle.net/20.500.12899/4990
dc.identifier.volume9
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Economic Development and Social Researches
dc.relation.ispartofMAS Journal of Applied Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20260612
dc.subjectDigital Soil Mapping
dc.subjectElectrical Conductivity
dc.subjectKriging
dc.subjectMachine Learning
dc.subjectSoil Ph
dc.titleDigital Mapping of Soil pH and Electrical Conductivity: A Comparative Analysis of Kriging and Machine Learning Approaches
dc.typeArticle

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