Liu Bingchun, Zhang Lei, Wang Qingshan
Research Institute of Circular Economy, Tianjin University of Technology, Tianjin 300384, China.
Research Institute of Circular Economy, Tianjin University of Technology, Tianjin 300384, China.
Waste Manag. 2021 Oct;134:42-51. doi: 10.1016/j.wasman.2021.08.007. Epub 2021 Aug 15.
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.
实现城市生活垃圾（MSW）产生量的准确预测对城市的可持续发展至关重要。本文选取北京作为研究对象，构建基于灰色关联分析和长短时记忆网络（GRA-LSTM）的神经网络模型，并选择14个城市生活垃圾产生量的影响因素作为输入指标，以实现对城市生活垃圾产生量的有效预测。然后，本研究利用上述预测结果和填埋场计算公式得出北京的填埋场面积。首先，运用灰色关联分析方法对城市生活垃圾产生量的影响因素进行排序，以获得关键影响指标。其次，使用长短时记忆网络（LSTM）模型学习关键影响指标的特征。最后，通过填埋场面积计算公式估算北京填埋场的面积。结果表明，首先，本文建立的GRA-LSTM组合模型的平均绝对百分比误差（MAPE）值为7.3，该模型的预测性能优于其他七种结构方法。其次，北京填埋场的面积需求呈上升趋势。最后，本文提出了相关建议，以实现城市的可持续发展，并应对城市生活垃圾产生量的增加和填埋场需求。
