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231225s2021 xx |||||o 00| ||eng c |
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|a 10.1016/j.wasman.2021.02.029
|2 doi
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|a pubmed25n1073.xml
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|a (DE-627)NLM322201608
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|a (NLM)33662770
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|a (PII)S0956-053X(21)00098-2
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Xu, Ankun
|e verfasserin
|4 aut
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| 245 |
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|a Applying artificial neural networks (ANNs) to solve solid waste-related issues
|b A critical review
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|c 2021
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| 336 |
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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|a Date Completed 16.03.2021
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|a Date Revised 16.03.2021
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a Copyright © 2021 Elsevier Ltd. All rights reserved.
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|a Artificial neural networks (ANNs) have recently attracted significant attention in environmental areas because of their great self-learning capability and good accuracy in mapping complex nonlinear relationships. These properties of ANNs benefit their application in solving different solid waste-related issues. However, the configurations, including ANN framework, algorithm, data set partition, input parameters, hidden layer, and performance evaluation, vary and have not reached a consensus among relevant studies. To address the current state of the art of ANN application in the solid waste field and identify the commonalities of ANNs, this critical review was conducted by focusing on a modeling perspective and using 177 relevant papers published over the last decade (2010-2020). We classified the reviewed studies into four categories in terms of research scales. ANNs were found to be applied widely in waste generation and technological parameter prediction and proven effective in solving meso-microscale and microscale issues, including waste conversion, emissions, and microbial and dynamic processes. Given the difficulty of data collection in many solid waste-related issues, most studies included a data size of 101-150. For mathematical optimization, dividing the data into training-validation-test sets is preferable, and the training set is supposed to account for ~70%. A single hidden layer is usually sufficient, and the optimal numbers of hidden layer nodes most likely range from 4 to 20. This review is supposed to contribute basic and comprehensive knowledge to the researchers in general waste management and specialized ANN study on solid waste-related issues
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|a Journal Article
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|a Review
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|a Feedforward neural network
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4 |
|a Model configuration
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| 650 |
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|a Prediction
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| 650 |
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|a Solid waste
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| 650 |
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|a artificial neural network (ANN)
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| 650 |
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7 |
|a Solid Waste
|2 NLM
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| 700 |
1 |
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|a Chang, Huimin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Xu, Yingjie
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Rong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Xiang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhao, Yan
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t Waste management (New York, N.Y.)
|d 1999
|g 124(2021) vom: 01. Apr., Seite 385-402
|w (DE-627)NLM098197061
|x 1879-2456
|7 nnas
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| 773 |
1 |
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|g volume:124
|g year:2021
|g day:01
|g month:04
|g pages:385-402
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|u http://dx.doi.org/10.1016/j.wasman.2021.02.029
|3 Volltext
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