Explaining Data Preprocessing Methods for Modeling and Forecasting with the Example of Product Drying

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dc.contributor.authorKorkmaz, Cem
dc.contributor.authorKacar, Ilyas
dc.date.accessioned2024-11-07T13:31:31Z
dc.date.available2024-11-07T13:31:31Z
dc.date.issued2024
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractAlthough regression is a traditional data processing method, machine and deep learning methods have been widely used in the literature in recent years for both modelling and prediction. However, in order to use these methods efficiently, it is important to perform a preliminary evaluation to understand the data type. Therefore, preevaluation procedures are described in this study. Experimental uncertainty analysis was performed to determine the measurement uncertainties in the measurement devices and sensors used in the drying experimental setup. Significant and insignificant relationships between variables in the data set were determined by Pearson correlation matrix. Autocorrelation and partial autocorrelation functions were used to determine the time series lag in the drying data and an AR(5) series with 5 lags was determined. The data were found to have variable variance due to peaks and troughs in the raw data resulting from the natural behaviour of the drying process. Modelling success was achieved with the normalisation pre -evaluation process performed without distorting the raw data. Thus, it has been shown that better models can be obtained compared to traditional models. In order to avoid unnecessary time and computational costs in the trial and error method used to determine the number of hidden layers and neurons in the machine learning method, various formulas proposed in the literature were compared. It is shown that the correlation coefficient alone is not sufficient to determine the goodness of the model. In modelling the data in this study, the NARX model was found to converge to the desired value faster and with less error than ANFIS and LSTM models. In the simulation of a rotary drum dryer, the optimum number of mesh elements was determined as 1137 by mesh independence analysis. In this way, unnecessary over -calculations were also prevented. Of course, all these methods are already available in statistical science. However, in this study, the methods to be used for modelling and prediction purposes are carefully selected and explained with examples, especially for young researchers who are outside this field to gain speed and easy comprehension.
dc.identifier.doi10.33462/jotaf.1300122
dc.identifier.endpage500
dc.identifier.issn1302-7050
dc.identifier.issn2146-5894
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85191404533
dc.identifier.scopusqualityQ3
dc.identifier.startpage482
dc.identifier.trdizinid1231254
dc.identifier.urihttps://doi.org/10.33462/jotaf.1300122
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1231254
dc.identifier.urihttps://hdl.handle.net/11480/14883
dc.identifier.volume21
dc.identifier.wosWOS:001229186000009
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.publisherUniv Namik Kemal
dc.relation.ispartofJournal of Tekirdag Agriculture Faculty-Tekirdag Ziraat Fakultesi Dergisi
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241106
dc.subjectData processing
dc.subjectRegression
dc.subjectPearson
dc.subjectAutocorrelation function
dc.subjectPartial autocorrelation function
dc.subjectHeteroscedasticity
dc.subjectConvergence
dc.subjectValidation
dc.titleExplaining Data Preprocessing Methods for Modeling and Forecasting with the Example of Product Drying
dc.title.alternativeModelleme ve Tahmin Amaçlı Veri Ön İşleme Yöntemlerinin Ürün Kurutma Örneği ile Açıklanması
dc.typeArticle

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