Demand gap analysis of municipal solid waste landfill in Beijing: Based on the municipal solid waste generation. (October 2021)
- Record Type:
- Journal Article
- Title:
- Demand gap analysis of municipal solid waste landfill in Beijing: Based on the municipal solid waste generation. (October 2021)
- Main Title:
- Demand gap analysis of municipal solid waste landfill in Beijing: Based on the municipal solid waste generation
- Authors:
- Liu, Bingchun
Zhang, Lei
Wang, Qingshan - Abstract:
- Highlights: The MSW generation is related to social index, economic index and population index. The Grey Relational Analysis method can improve the prediction performance accuracy. The GRA-LSTM model precisely and accurately predict the MSW generation in Beijing. Advice for urban development are proposed from the MSW source and the MSW disposal. Abstract: Achieving accurate prediction of the Municipal Solid Waste (MSW) generation is essential for the sustainable development of the city. This paper selects Beijing as the research object, building a neural network model based on Grey Relational Analysis and Long and Short-Term Memory (GRA-LSTM), and choosing 14 influencing factors of MSW generation as the input indicators, to realize the effective prediction of MSW generation. Then this study obtains the landfill area in Beijing by using the aforementioned prediction results and the calculation formula of the landfill. Firstly, the GRA method is used to sort the influencing factors of the MSW generation for obtain the key influencing indexes. Secondly, the LSTM model is used to learn features of the key influencing indexes. Finally, the area of Beijing landfill is estimated by the calculation formula of landfill area. The results show that, first of all, the MAPE value of the GRA-LSTM combined model established in this paper is 7.3, and the prediction performance of this model is better than the other seven structural methods. Secondly, the area demand for landfills in BeijingHighlights: The MSW generation is related to social index, economic index and population index. The Grey Relational Analysis method can improve the prediction performance accuracy. The GRA-LSTM model precisely and accurately predict the MSW generation in Beijing. Advice for urban development are proposed from the MSW source and the MSW disposal. Abstract: Achieving accurate prediction of the Municipal Solid Waste (MSW) generation is essential for the sustainable development of the city. This paper selects Beijing as the research object, building a neural network model based on Grey Relational Analysis and Long and Short-Term Memory (GRA-LSTM), and choosing 14 influencing factors of MSW generation as the input indicators, to realize the effective prediction of MSW generation. Then this study obtains the landfill area in Beijing by using the aforementioned prediction results and the calculation formula of the landfill. Firstly, the GRA method is used to sort the influencing factors of the MSW generation for obtain the key influencing indexes. Secondly, the LSTM model is used to learn features of the key influencing indexes. Finally, the area of Beijing landfill is estimated by the calculation formula of landfill area. The results show that, first of all, the MAPE value of the GRA-LSTM combined model established in this paper is 7.3, and the prediction performance of this model is better than the other seven structural methods. Secondly, the area demand for landfills in Beijing shows an upward trend. At last, this paper put forward relevant suggestions to achieve sustainable urban development and deal with the increase in the MSW generation and the demand for landfills. … (more)
- Is Part Of:
- Waste management. Volume 134(2021)
- Journal:
- Waste management
- Issue:
- Volume 134(2021)
- Issue Display:
- Volume 134, Issue 2021 (2021)
- Year:
- 2021
- Volume:
- 134
- Issue:
- 2021
- Issue Sort Value:
- 2021-0134-2021-0000
- Page Start:
- 42
- Page End:
- 51
- Publication Date:
- 2021-10
- Subjects:
- Municipal solid waste landfill -- Municipal solid waste generation -- Sustainable development -- Long Short Term Memory (LSTM)
Hazardous wastes -- Periodicals
Refuse and refuse disposal -- Periodicals
363.728 - Journal URLs:
- http://www.sciencedirect.com/science/journal/0956053X ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.wasman.2021.08.007 ↗
- Languages:
- English
- ISSNs:
- 0956-053X
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 9266.674500
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 18579.xml