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dc.contributor.authorCoşkuner, Gülnuren_US
dc.contributor.authorJassim, Majeed S.en_US
dc.contributor.authorZontul, Metinen_US
dc.contributor.authorKarateke, Sedaen_US
dc.identifier.citationCoskuner, G., Jassim, M. S., Zontul, M., & Karateke, S. Application of artificial intelligence neural network modeling to predict the generation of domestic, commercial and construction wastes. Waste Management & Research, 9. doi:10.1177/0734242x20935181en_US
dc.descriptionZontul, Metin (Arel Author), Karateke, Seda (Arel Author)en_US
dc.description.abstractReliable prediction of municipal solid waste (MSW) generation rates is a significant element of planning and implementation of sustainable solid waste management strategies. In this study, the multi-layer perceptron artificial neural network (MLP-ANN) is applied to verify the prediction of annual generation rates of domestic, commercial and construction and demolition (C&D) wastes from the year 1997 to 2016 in Askar Landfill site in the Kingdom of Bahrain. The proposed robust predictive models incorporated selected explanatory variables to reflect the influence of social, demographical, economic, geographical and touristic factors upon waste generation rates (WGRs). The Mean Squared Error (MSE) and coefficient of determination (R-2) are used as performance indicators to evaluate effectiveness of the developed models. MLP-ANN models exhibited strong accuracy in predictions with highR(2)and low MSE values. TheR(2)values for domestic, commercial and C&D wastes are 0.95, 0.99 and 0.91, respectively. Our results show that the developed MLP-ANN models are effective for the prediction of WGRs from different sources and could be considered as a cost-effective approach for planning integrated MSW management systems.en_US
dc.relation.ispartofWaste Management & Researchen_US
dc.subjectArtificial Neural Networken_US
dc.subjectMulti-Layer Perceptronen_US
dc.subjectPredictive Modelingen_US
dc.subjectMunicipal Solid Wasteen_US
dc.subjectTrend Analysisen_US
dc.titleApplication of artificial intelligence neural network modeling to predict the generation of domestic, commercial and construction wastesen_US
dc.departmentMühendislik ve Mimarlık Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US

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