Deep learning hybrid predictions for the amount of municipal solid waste: A case study in Shanghai.

Researchers

Journal

Modalities

Models

Abstract

It is crucial to precisely estimate the municipal solid waste (MSW) amount for its sustainable management. Recently, deep learning becomes one of the useful tools as a result of learning complicated and abstract features between the factors and target, which seems to have great potential to predict the MSW amount. Therefore, this study aimed to design an MSW amount predicted system in Shanghai, consisting of Attention (A), one-dimensional convolutional neural network (C), and long short-term memory (L), to investigate the relationship between exogenous series (24 socioeconomics factors and past MSW amount) and target (MSW amount). The role of Attention, 1D-CNN, LSTM played on the MSW predicted amount also have investigated. The results show that the role Attention played as decoding the encoding information, which would have better performance between predicted and known MSW amount (R2 in A-L-C, L-A-C, L-C-A was 89.45%, 90.77%, and 95.31%, respectively.). In terms of CNN, it seems that there are no significant differences among the place CNN modules put in MSW predicted system. Finally, R2 in L-A-C, A-L-C, and A-C-L was 85.44%, 91.61%, and 89.45%, which suggested that LSTM as an intermediary between CNN and Attention modules seems a wise measure to predict the MSW amount based on the correlation efficiency. In addition, some socioeconomic factors including the average number of people in households and budget revenue may be chosen for the decision-making of MSW management in Shanghai city in the future, according to the weight of neurons in fully connected layers by the visual technology.Copyright © 2022. Published by Elsevier Ltd.