Measurements and modelling of wind erosion rate in different tillage practices using a portable wind erosion tunnel

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dc.contributor.authorCarman, Kazim
dc.contributor.authorMarakoglu, Tamer
dc.contributor.authorTaner, Alper
dc.contributor.authorMikailsoy, Fariz
dc.date.accessioned2020-03-26T19:25:02Z
dc.date.available2020-03-26T19:25:02Z
dc.date.issued2016
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractArtificial intelligence systems are widely accepted as a technology providing an alternative method to solve complex and ill-defined problems. Artificial neural network (ANN) is a technique with a flexible mathematical structure, which is capable of identifying a complex nonlinear relationship between the input and output data. The objective of this study was to investigate the relationship between dust concentration and wind erosion rate, and to illustrate how ANN might play an important role in the prediction of wind erosion rate. Data were recorded via field experiments by using a portable field wind tunnel. The experiments were carried out for eight different tillage applications that include the conventional, six different reduced tillage and the direct seeding practices. Particulate matter (PM) concentration generally decreased with a decrease in number or intensity of tillage operations. Direct seeding resulted in the lowest PM,, concentration. After tillage applications, wind erosion rate varied between 113 and 1365 g m(-2) h(-1). Results showed that wind erosion rate was lower in direct seeding than in conventional and reduced tillage applications. In this paper, a sophisticated intelligent model, based on a 1-(8-5)-1 ANN model with a back-propagation learning algorithm, was developed to predict the changes in the wind erosion rate due to dust concentration occurring during tillage. In addition, the prediction of the model was made according to traditional methods of wind erosion rate by using the programme Statistica, version 5. The verification of the proposed model was carried out by applying various numerical error criteria. The ANN model consistently provided better predictions compared with the nonlinear regression-based model. The relative error of the predicted values was found to be less than the acceptable limits (10%). Based on the results of this study, ANN appears to be a promising technique for predicting wind erosion rate.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [111 O 182]en_US
dc.description.sponsorshipThis project was supported by The Scientific and Technological Research Council of Turkey (TUBITAK, code 111 O 182).en_US
dc.identifier.doi10.13080/z-a.2016.103.042en_US
dc.identifier.endpage334en_US
dc.identifier.issn1392-3196en_US
dc.identifier.issue3en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage327en_US
dc.identifier.urihttps://dx.doi.org/10.13080/z-a.2016.103.042
dc.identifier.urihttps://hdl.handle.net/20.500.12395/33775
dc.identifier.volume103en_US
dc.identifier.wosWOS:000385609800012en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherLITHUANIAN RESEARCH CENTRE AGRICULTURE & FORESTRYen_US
dc.relation.ispartofZEMDIRBYSTE-AGRICULTUREen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectartificial neural networken_US
dc.subjectconservation tillageen_US
dc.subjectdust concentrationen_US
dc.subjectsoil erodibility by winden_US
dc.titleMeasurements and modelling of wind erosion rate in different tillage practices using a portable wind erosion tunnelen_US
dc.typeArticleen_US

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