王德运 Long short-term memory neural network and improved particle swarm optimization-based modeling and scenario analysis for municipal solid waste generation in Shanghai, China.

来源：古天乐代言太阳集团发布时间：2023-05-15 点击次数：

我校古天乐代言太阳集团王德运老师在T2级别期刊——《Environmental science and pollution research international》上发表题为“Long short-term memory neural network and improved particle swarm optimization-based modeling and scenario analysis for municipal solid waste generation in Shanghai, China.”。论文第一作者王德运为古天乐代言太阳集团副教授，博士生导师。

Abstract / 摘要：

Accurate estimations of municipal solid waste (MSW) generation are vital to effective MSW management systems. While various single-point estimation approaches have been developed, the non-linearity and multiple site-specific influencing factors associated with MSW management systems make it challenging to forecast MSW generation quantities precisely. To address these concerns, this study developed a two-stage modeling and scenario analysis procedure for MSW generation and taking Shanghai as a test case demonstrated its viability. In the first stage, nine influencing factors were selected, and a hybrid novel forecasting model based on a long short-term memory neural network and an improved particle swarm optimization (IPSO-LSTM) was proposed for the forecasting of the MSW generation quantities, after which actual Shanghai data from 1980 to 2019 were used to test the performance. In the second stage, the future influencing variable values in different scenarios were predicted using an improved grey model, after which the predicted Shanghai MSW generation quantities from 2025 to 2035 were evaluated under various scenarios. It was found that (1) the proposed IPSO-LSTM had higher accuracy than the benchmark models; (2) the MSW generation quantities are expected to respectively increase to 9.971, 9.684, and 9.090 million tons by 2025 and 11.402, 11.285, and 10.240 by 2035 under the low, benchmark, and high scenarios; and (3) the MSW generation differences between the high and medium scenarios were decreasing.

论文信息;

Title/题目:

Long short-term memory neural network and improved particle swarm optimization-based modeling and scenario analysis for municipal solid waste generation in Shanghai, China.

Authors/作者:

Wang Deyun;Yuan YingAn;Ben Yawen;Luo Hongyuan;Guo Haixiang

Key Words / 关键词：

Forecasting;Improved GM (1,1);Improved particle swarm optimization;Long short-term memory;Municipal solid waste;Scenario analysis

DOI: 10.1007/S11356-022-20438-0

全文链接：https://pubmed.ncbi.nlm.nih.gov/35567684/

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