| Title |
Demand gap analysis of municipal solid waste landfill in Beijing: Based on the municipal solid waste generation |
| ID_Doc |
17920 |
| Authors |
Liu, BC; Zhang, L; Wang, QS |
| Title |
Demand gap analysis of municipal solid waste landfill in Beijing: Based on the municipal solid waste generation |
| Year |
2021 |
| Published |
|
| DOI |
10.1016/j.wasman.2021.08.007 |
| 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 sus-tainable urban development and deal with the increase in the MSW generation and the demand for landfills. |
| Author Keywords |
Municipal solid waste landfill; Municipal solid waste generation; Sustainable development; Long Short Term Memory (LSTM) |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000692322200005 |
| WoS Category |
Engineering, Environmental; Environmental Sciences |
| Research Area |
Engineering; Environmental Sciences & Ecology |
| PDF |
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