Forecasting contamination in an ecosystem based on a network model

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dc.authoridhttp://orcid.org/0000-0003-0508-2917en_US
dc.authoridhttp://orcid.org/0000-0003-3140-7922en_US
dc.authoridhttp://orcid.org/0000-0002-2838-3651en_US
dc.authoridhttp://orcid.org/0000-0003-2970-0863en_US
dc.authoridhttp://orcid.org/0000-0002-0825-5951en_US
dc.contributor.authorSarı, Murat
dc.contributor.authorYalçın, İbrahim Ertuğrul
dc.contributor.authorTaner, Mahmut
dc.contributor.authorCoşgun, Tahir
dc.contributor.authorÖzyiğit, İbrahim İlker
dc.date.accessioned2023-05-05T08:22:33Z
dc.date.available2023-05-05T08:22:33Z
dc.date.issued2023en_US
dc.departmentİstanbul Gelişim Meslek Yüksekokuluen_US
dc.description.abstractThis paper aims to predict heavy metal pollution based on ecological factors with a new approach, using artificial neural networks (ANNs), by significantly removing typical obstacles like time-consuming laboratory procedures and high implementation costs. Pollution prediction is crucial for the safety of all living things, for sustainable development, and for policymakers to make the right decisions. This study focuses on predicting heavy metal contamination in an ecosystem at a significantly lower cost because pollution assessment still primarily relies on conventional methods, which are recognized to have disadvantages. To accomplish this, the data collected for 800 plant and soil materials have been utilized in the production of an ANN. This research is the first to use an ANN to predict pollution very accurately and has found the network models to be very suitable systemic tools for modelling in pollution data analysis. The findings appear are promising to be very illuminating and pioneering for scientists, conservationists, and governments to swiftly and optimally develop their appropriate work programs to leave a functioning ecosystem for all living things. It has been observed that the relative errors calculated for each of the polluting heavy metals for training, testing, and holdout data are significantly low.en_US
dc.identifier.doi10.1007/s10661-023-11050-xen_US
dc.identifier.endpage14en_US
dc.identifier.issn0167-6369
dc.identifier.issn1573-2959
dc.identifier.issue5en_US
dc.identifier.pmid37010616en_US
dc.identifier.scopus2-s2.0-85151791943en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://hdl.handle.net/11363/4558
dc.identifier.urihttps://doi.org/
dc.identifier.volume195en_US
dc.identifier.wosWOS:000962872500004en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherSPRINGER, VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDSen_US
dc.relation.ispartofEnvironmental Monitoring and Assessmenten_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectEnvironmental pollutionen_US
dc.subjectCadmiumen_US
dc.subjectChromiumen_US
dc.subjectLeaden_US
dc.subjectNeural network modelen_US
dc.titleForecasting contamination in an ecosystem based on a network modelen_US
dc.typeArticleen_US

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