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Predicting municipal solid waste using a coupled artificial neural network with archimedes optimisation algorithm and socioeconomic components

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dc.citedby18
dc.contributor.authorLiang G.en_US
dc.contributor.authorPanahi F.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorEhteram M.en_US
dc.contributor.authorBand S.S.en_US
dc.contributor.authorElshafie A.en_US
dc.contributor.authorid57205198052en_US
dc.contributor.authorid55368172500en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57113510800en_US
dc.contributor.authorid57221738247en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T09:05:57Z
dc.date.available2023-05-29T09:05:57Z
dc.date.issued2021
dc.descriptionForecasting; Genetic algorithms; Health risks; Mean square error; Model structures; Municipal solid waste; Particle swarm optimization (PSO); 'current; Fuzzy reasoning; Inclusive multiple model; Multiple-modeling; Neural-networks; Optimization algorithms; Particle swarm; Sine-cosine algorithm; Solid waste generation; Swarm optimization; Neural networksen_US
dc.description.abstractSolid Waste (SW) is one of the critical challenges of urban life. These SWs are considered environmental pollutants that are a threat to ecology and human health. Predicting SW generation is an essential topic for scholars to better manage SWs. This study investigates the application of optimised ANN models for predicting monthly SW generation in Iran using datasets about seven Iranian megacities. The Archimedes Optimisation Algorithm (AOA), Sine Cosine Algorithm (SCA), Particle Swarm Optimisation (PSO) technique, and Genetic Algorithms (GA) were used for training the ANN model. The enhanced gamma test was used to determine the best input combination. AOA and the gamma test were used concurrently to reduce the time needed for choosing the best input combination. Gross domestic product (GDP), population, household size, and numbers of months were the best input combination set. This best input combination was then inputted into the hybrid and standalone ANN models for predicting monthly SW generation. During the final phase, the outputs of ANN-AOA, ANN-SCA, ANN-PSO, ANN-GA, and ANN models were used as inputs for an inclusive multiple model (IMM) in order to enhance model accuracy. The IMM model reduced the training phase root mean square error (RMSE) of ANN-AOA, ANN-SCA, ANN-PSO, ANN-GA, and ANN models by 55%, 59%, 68%, 72%, and 73%, respectively. Although ANN-AOA provided higher R2 and lower RMSE values than ANN-PSO, ANN-SCA, ANN-GA and ANN models, the IMM model outperformed ANN-AOA, considering that it integrates the advantages of all models used in the current study. The current study also used the fuzzy reasoning concept for modifying ANN model structures. The results indicated that such ANN models' time requirement was lower than those without fuzzy reasoning concept. The general results of the current study indicate that the ANN-AOA and the fuzzy-reasoning based Inclusive Multiple Model have a high ability for predicting different target variables. � 2021 Elsevier Ltden_US
dc.description.natureFinalen_US
dc.identifier.ArtNo128039
dc.identifier.doi10.1016/j.jclepro.2021.128039
dc.identifier.scopus2-s2.0-85109017497
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85109017497&doi=10.1016%2fj.jclepro.2021.128039&partnerID=40&md5=c7a2f1fd042c21d8b596822f27ff9343
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25994
dc.identifier.volume315
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleJournal of Cleaner Production
dc.titlePredicting municipal solid waste using a coupled artificial neural network with archimedes optimisation algorithm and socioeconomic componentsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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