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231225s2020 xx |||||o 00| ||eng c |
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|a 10.1016/j.wasman.2020.04.015
|2 doi
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|a pubmed24n1029.xml
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|a (DE-627)NLM308839048
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|a (NLM)32299033
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|a (PII)S0956-053X(20)30175-6
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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 |
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|a Wu, Fan
|e verfasserin
|4 aut
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|a New insights into regional differences of the predictions of municipal solid waste generation rates using artificial neural networks
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|c 2020
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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
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|2 rdacarrier
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|a Date Completed 21.05.2020
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|a Date Revised 21.05.2020
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a Copyright © 2020 Elsevier Ltd. All rights reserved.
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|a As one of the most popular non-linear models, artificial neural network (ANN) has been successfully applied in the prediction of municipal solid waste (MSW). Despite its high accuracy achieved in a specific city or region, little progress is made on a larger-scale, which would be resulted from the regional difference. In this study, ANN models for MSW prediction in mainland China are developed and optimized. Besides a model aiming for all cities, regional models are developed by grouping these cities into three categories. Impact of regional difference in MSW prediction is analyzed by evaluation of model's dependence on each predictor, and comparisons made between these models. Results show that regional difference has huge impact on MSW prediction. Accuracy of MSW prediction would increase from 0.916 in R2 and 59.3 in rooted mean squared error (RMSE) to 0.968/0.946/0.943 in R2 and 6.4/9.7/17.6 in RMSE for southern/northern/western region after a three-region division. Models for MSW prediction in southern and northern region of mainland China share much similarity in dependence on predictors, which differs a lot from that for western region. Further cross-prediction process confirmed that models for southern or northern regions might be suitable for the MSW prediction in another, yet not apply to that in western region. Such large-scale based model can be used by cities lacking historical data for prediction of their local MSW generation, the predictive result would be helpful in MSW disposal planning and the analysis of regional difference would be helpful in establishing regional policy, especially for the three regions in mainland China
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|a Journal Article
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|a Artificial neural network
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|a Cross prediction method
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|a MSW prediction
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|a Mainland China
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|a Predictor-exclusive method
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|a Regional difference
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| 650 |
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|a Solid Waste
|2 NLM
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| 700 |
1 |
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|a Niu, Dongjie
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Dai, Shijin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wu, Boran
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t Waste management (New York, N.Y.)
|d 1999
|g 107(2020) vom: 15. Apr., Seite 182-190
|w (DE-627)NLM098197061
|x 1879-2456
|7 nnns
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| 773 |
1 |
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|g volume:107
|g year:2020
|g day:15
|g month:04
|g pages:182-190
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|u http://dx.doi.org/10.1016/j.wasman.2020.04.015
|3 Volltext
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