LSTM-Based Forecasting for Urban Construction Waste Generation

Li Huang, Ting Cai, Ya Zhu, Yuliang Zhu, Wei Wang and Kehua Sun
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Li Huang: Key Laboratory of Coastal Disaster and Defence of Ministry of Education, Hohai University, Nanjing 210098, China
Ting Cai: Business School, Sichuan University, Chengdu 610065, China
Ya Zhu: College of Political Science, Nanjing Agricultural University, Nanjing 210095, China
Yuliang Zhu: Key Laboratory of Coastal Disaster and Defence of Ministry of Education, Hohai University, Nanjing 210098, China
Wei Wang: Key Laboratory of Coastal Disaster and Defence of Ministry of Education, Hohai University, Nanjing 210098, China
Kehua Sun: Shanghai Communications Construction Co., Ltd., Shanghai 200136, China

Sustainability, 2020, vol. 12, issue 20, 1-12

Abstract: Accurate forecasts of construction waste are important for recycling the waste and formulating relevant governmental policies. Deficiencies in reliable forecasting methods and historical data hinder the prediction of this waste in long- or short-term planning. To effectively forecast construction waste, a time-series forecasting method is proposed in this study, based on a three-layer long short-term memory (LSTM) network and univariate time-series data with limited sample points. This method involves network structure design and implementation algorithms for network training and the forecasting process. Numerical experiments were performed with statistical construction waste data for Shanghai and Hong Kong. Compared with other time-series forecasting models such as ridge regression (RR), support vector regression (SVR), and back-propagation neural networks (BPNN), this paper demonstrates that the proposed LSTM-based forecasting model is effective and accurate in predicting construction waste generation.

Keywords: environmental engineering; construction waste; short and long-term memory (LSTM) network; time-series forecasting; deep learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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