Predicting municipal solid waste using a coupled artificial neural network with archimedes optimisation algorithm and socioeconomic components
Forecasting; 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;...
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2023
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my.uniten.dspace-259942023-05-29T17:05:58Z Predicting municipal solid waste using a coupled artificial neural network with archimedes optimisation algorithm and socioeconomic components Liang G. Panahi F. Ahmed A.N. Ehteram M. Band S.S. Elshafie A. 57205198052 55368172500 57214837520 57113510800 57221738247 16068189400 Forecasting; 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 networks Solid 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 Ltd Final 2023-05-29T09:05:57Z 2023-05-29T09:05:57Z 2021 Article 10.1016/j.jclepro.2021.128039 2-s2.0-85109017497 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109017497&doi=10.1016%2fj.jclepro.2021.128039&partnerID=40&md5=c7a2f1fd042c21d8b596822f27ff9343 https://irepository.uniten.edu.my/handle/123456789/25994 315 128039 Elsevier Ltd Scopus |
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Forecasting; 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 networks |
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57205198052 |
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57205198052 Liang G. Panahi F. Ahmed A.N. Ehteram M. Band S.S. Elshafie A. |
| format |
Article |
| author |
Liang G. Panahi F. Ahmed A.N. Ehteram M. Band S.S. Elshafie A. |
| spellingShingle |
Liang G. Panahi F. Ahmed A.N. Ehteram M. Band S.S. Elshafie A. Predicting municipal solid waste using a coupled artificial neural network with archimedes optimisation algorithm and socioeconomic components |
| author_sort |
Liang G. |
| title |
Predicting municipal solid waste using a coupled artificial neural network with archimedes optimisation algorithm and socioeconomic components |
| title_short |
Predicting municipal solid waste using a coupled artificial neural network with archimedes optimisation algorithm and socioeconomic components |
| title_full |
Predicting municipal solid waste using a coupled artificial neural network with archimedes optimisation algorithm and socioeconomic components |
| title_fullStr |
Predicting municipal solid waste using a coupled artificial neural network with archimedes optimisation algorithm and socioeconomic components |
| title_full_unstemmed |
Predicting municipal solid waste using a coupled artificial neural network with archimedes optimisation algorithm and socioeconomic components |
| title_sort |
predicting municipal solid waste using a coupled artificial neural network with archimedes optimisation algorithm and socioeconomic components |
| publisher |
Elsevier Ltd |
| publishDate |
2023 |
| _version_ |
1806426302374215680 |
| score |
13.211869 |