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231226s2024 xx |||||o 00| ||eng c |
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|a 10.1177/0734242X231192766
|2 doi
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|a pubmed24n1426.xml
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|a eng
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| 100 |
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|a Zhao, Ying
|e verfasserin
|4 aut
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| 245 |
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|a Prediction of municipal solid waste generation and analysis of dominant variables in rapidly developing cities based on machine learning - a case study of China
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|c 2024
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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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| 500 |
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|a Date Completed 01.06.2024
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|a Date Revised 01.06.2024
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a Prediction of municipal solid waste (MSW) generation plays an essential role in effective waste management. The main objectives of this study were to develop models for accurate prediction of MSW generation (MSWG) and analyze the influence of dominant variables on MSWG. To elevate the model's prediction accuracy, more than 50 municipal variables were considered original variables, which were selected from 12 categories. According to the screening results, the dominant variables are classified into four categories: urban greening, population size and residential density, regional economic development and resident income and expenditure. Among the seven machine learning methods, back propagation (BP) neural network has the best model evaluation effect. The R2 of the BP neural network model of Jiangsu, Zhejiang and Shandong provinces were 0.969, 0.941 and 0.971 respectively. The prediction accuracy of Shandong province (93.8%) was the best, followed by Jiangsu province (92.3%) and Zhejiang province (72.7%). The correlation between dominant variables and the MSWG was mined, suggesting that regional GDP and the total retail sales of consumer goods were the most important dominant variables affecting MSWG. Moreover, the MSWG might not absolutely associate with the population size and residential density. The method used in this study is a practical tool for policymakers on regional/local waste management and MSWG control
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|a Journal Article
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|a BP neural network
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|a Municipal solid waste
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| 650 |
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|a dominant variables
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| 650 |
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|a machine learning
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|a prediction model
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| 650 |
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|a waste management
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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 Tao, Zhe
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Ying
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Sun, Huige
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Tang, Jingrui
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Qianya
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Guo, Liang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Song, Weiwei
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Bailian Larry
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA
|d 1991
|g 42(2024), 6 vom: 31. Juni, Seite 476-484
|w (DE-627)NLM098164791
|x 1096-3669
|7 nnns
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| 773 |
1 |
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|g volume:42
|g year:2024
|g number:6
|g day:31
|g month:06
|g pages:476-484
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|u http://dx.doi.org/10.1177/0734242X231192766
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