Prediction of electrical conductivity using ANN and MLR: a case study from Turkey

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dc.contributor.authorKeskin, TUlay Ekemen
dc.contributor.authorOzler, Emre
dc.contributor.authorSander, Emrah
dc.contributor.authorDugenci, Muharrem
dc.contributor.authorAhmed, Mohammed Yadgar
dc.date.accessioned2024-09-29T15:51:27Z
dc.date.available2024-09-29T15:51:27Z
dc.date.issued2020
dc.departmentKarabük Üniversitesien_US
dc.description.abstractThe study areas are located in Turkey (Kastamonu, Bartin, Karabuk, Sivas) and contain very different rock types, various mining and agricultural activity opportunities. So, the areas have groundwaters that have different chemical compositions and electrical conductivity (EC) values. The EC can be measured using EC meter, and it must be measured in situ. But, the measurement of EC in situ is laborious, time-consuming, expensive, and difficult in arduous terrain environments. In recent years, machine learning models have been a primary focus of interest for a lot of study by providing often highly accurate forecast for solutions of such problems. The aim of the study is to forecast EC of groundwater using artificial neural networks (ANN) and multiple linear regressions (MLR). Twelve different hydrochemical parameters, which affect the EC, such as major/minor ions and trace elements, were used in the analysis. Multilayer feed-forward ANN trained with backpropagation in Python machine learning libraries was used in this study. In order to obtain the most appropriate ANN architecture, trial-and-error procedure was used and different numbers of hidden layers, neurons, activation functions, optimizers, and test sizes were constructed. This study also tests the usability of input parameters in EC prediction studies. As a result, comparisons between the measured and predicted values indicated that the machine learning models could be successfully applied and provide high accuracy and reliability for EC and similar parameters forecasting.en_US
dc.description.sponsorshipCUBAP (Cumhuriyet Univ. Sci. Res. Proj. Com.); TUBTAK [114Y031]en_US
dc.description.sponsorshipThe authors want to thank the CUBAP (Cumhuriyet Univ. Sci. Res. Proj. Com.) and TUBTAK (Project No: 114Y031) contributing to the financial section of the project.en_US
dc.identifier.doi10.1007/s11600-020-00424-1
dc.identifier.endpage820en_US
dc.identifier.issn1895-6572
dc.identifier.issn1895-7455
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85085552368en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage811en_US
dc.identifier.urihttps://doi.org/10.1007/s11600-020-00424-1
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4089
dc.identifier.volume68en_US
dc.identifier.wosWOS:000533832100001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Int Publ Agen_US
dc.relation.ispartofActa Geophysicaen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectPrediction of ECen_US
dc.subjectWater quality parametersen_US
dc.subjectArtificial neural network (ANN)en_US
dc.subjectMultiple linear regression (MLR)en_US
dc.titlePrediction of electrical conductivity using ANN and MLR: a case study from Turkeyen_US
dc.typeArticleen_US

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