An automata networks based preprocessing technique for artificial neural network modelling of primary production levels in reservoirs

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dc.contributor.authorKılıç, Hürevren
dc.contributor.authorSoyupak, Selçuk
dc.contributor.authorTüzün, İlhami
dc.contributor.authorİnce, Özlem
dc.contributor.authorBaşaran, Gökben
dc.date.accessioned2020-06-25T17:43:55Z
dc.date.available2020-06-25T17:43:55Z
dc.date.issued2007
dc.descriptionKilic, Hurevren/0000-0003-2647-8451; Kilic, Hurevren/0000-0002-9058-0365
dc.description.abstractPrimary production in lakes and reservoirs develops as a result of complex reactions and interactions. Artificial neural networks (ANN) emerges as an approach in quantification of primary productivity in reservoirs. Almost all of the past ANN applications employed input data matrices whose vectors represent either water quality parameters or environmental characteristics. Most of the time, the components of input matrices are determined using expert opinion that implies possible factors that affect output vector. Major disadvantage of this approach is the possibility of ending-up with an input matrix that may have high correlations between some of its components. In this paper, an automata networks (AN) based preprocessing technique was developed to select suitable and appropriate constituents of input matrix to eliminate redundancy and to enhance calculation efficiency. The proposed technique specifically provides an apriori rough behavioral modeling through identification of minimal AN interaction topology. Predictive ANN models of primary production levels were developed for a reservoir following AN based pre-modeling step. The achieved levels of model precisions and performances were acceptable: the calculated root mean square error values (RMSE) were low; a correlation coefficient (R) as high as 0.83 was achieved with an ANN model of a specific structure. (c) 2006 Elsevier B.V. All rights reserved.en_US
dc.identifier.citationclosedAccessen_US
dc.identifier.doi10.1016/j.ecolmodel.2006.09.026
dc.identifier.endpage368en_US
dc.identifier.issn0304-3800
dc.identifier.issn1872-7026
dc.identifier.issue3-4en_US
dc.identifier.scopus2-s2.0-33846653402
dc.identifier.scopusqualityQ1
dc.identifier.startpage359en_US
dc.identifier.urihttps://doi.org10.1016/j.ecolmodel.2006.09.026
dc.identifier.urihttps://hdl.handle.net/20.500.12587/3944
dc.identifier.volume201en_US
dc.identifier.wosWOS:000244598300011
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevieren_US
dc.relation.ispartofEcological Modelling
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectautomata networksen_US
dc.subjectbehavioral modelingen_US
dc.subjectinteger linear programmingen_US
dc.subjectquasi newton methoden_US
dc.subjectprimary productivityen_US
dc.subjectreservoirsen_US
dc.titleAn automata networks based preprocessing technique for artificial neural network modelling of primary production levels in reservoirsen_US
dc.typeArticle

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