Advanced Search

Show simple item record

dc.contributor.authorCoşkuner, Gülnuren_US
dc.contributor.authorJassim, Majeed S.en_US
dc.contributor.authorZontul, Metinen_US
dc.date.accessioned2021-08-17T13:10:41Z
dc.date.available2021-08-17T13:10:41Z
dc.date.issued2021en_US
dc.identifier.citationJassim, M. S., Coskuner, G., & Zontul, M. (2021). Comparative performance analysis of support vector regression and artificial neural network for prediction of municipal solid waste generation. Waste Management & Research, 0734242X211008526.en_US
dc.identifier.issn0734-242X
dc.identifier.issn1096-3669
dc.identifier.urihttps://doi.org/10.1177/0734242X211008526
dc.identifier.urihttps://hdl.handle.net/20.500.12294/2820
dc.description.abstractThe evolution of machine learning (ML) algorithms provides researchers and engineers with state-of-the-art tools to dynamically model complex relationships. The design and operation of municipal solid waste (MSW) management systems require accurate estimation of generation rates. In this study, we applied rapid, non-linear and non-parametric data driven ML algorithms independently, multi-layer perceptron artificial neural network (MLP-ANN) and support vector regression (SVR) models to predict annual MSW generation rates in Bahrain. Models were trained and tested with MSW generation data for period of 1997-2019. The population, gross domestic product, annual tourist numbers, annual electricity consumption and total annual CO2 emissions were selected as explanatory variables and incorporated into developed models. The zero score normalization (ZSN) and minimum maximum normalization (MMN) methods were utilized to improve the quality of data and subsequently enhances the performance of ML algorithms. Statistical metrics were employed to discriminate performance of MLP-ANN and SVR models. The linear, polynomial, radial basis function (RBF) and sigmoid kernel functions were investigated to find the optimal SVR model. Results showed that RBF-SVR model with R-2 value of 0.97% and 4.82% and absolute forecasting error (AFE) for the period of 2008 and 2019 exhibits superior prediction and robustness in comparison to MLP-ANN. The efficacy of MLP-ANN model was also reasonably successful with R-2 value of 0.94. It was shown that MMN pre-processing generated optimal MLP-ANN model while ZSN pre-processing produced optimal RBF-SVR model. This work also highlights the importance of application of ML modelling approaches to plan and implement their roadmap for waste management systems by policymakers.en_US
dc.language.isoengen_US
dc.publisherSage Publications Ltden_US
dc.relation.ispartofWaste Management & Researchen_US
dc.identifier.doi10.1177/0734242X211008526en_US
dc.identifier.doi10.1177/0734242X211008526
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectBahrainen_US
dc.subjectMachine Learningen_US
dc.subjectMulti-layer Perceptronen_US
dc.subjectMunicipal Solid Wasteen_US
dc.subjectPredictive Modellingen_US
dc.subjectWaste Generation Rateen_US
dc.titleComparative Performance Analysis of Support Vector Regression and Artificial Neural Network for Prediction of Municipal Solid Waste Generationen_US
dc.typearticleen_US
dc.departmentMühendislik ve Mimarlık Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record