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LSTM-Based Forecasting for Urban Construction Waste Generation

Author

Listed:
  • Li Huang

    (Key Laboratory of Coastal Disaster and Defence of Ministry of Education, Hohai University, Nanjing 210098, China
    School of Public Administration, 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
    College of Harbour, Coastal and Offshore Engineering, Hohai University, Nanjing 210098, China)

  • Wei Wang

    (Key Laboratory of Coastal Disaster and Defence of Ministry of Education, Hohai University, Nanjing 210098, China
    College of Harbour, Coastal and Offshore Engineering, Hohai University, Nanjing 210098, China)

  • Kehua Sun

    (Shanghai Communications Construction Co., Ltd., Shanghai 200136, China)

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.

Suggested Citation

  • Li Huang & Ting Cai & Ya Zhu & Yuliang Zhu & Wei Wang & Kehua Sun, 2020. "LSTM-Based Forecasting for Urban Construction Waste Generation," Sustainability, MDPI, vol. 12(20), pages 1-12, October.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:20:p:8555-:d:428988
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    References listed on IDEAS

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    1. Wei Wang & Li Huang & Jian Gu & Liupeng Jiang, 2019. "Green port project scheduling with comprehensive efficiency consideration," Maritime Policy & Management, Taylor & Francis Journals, vol. 46(8), pages 967-981, November.
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    Cited by:

    1. Vidas Raudonis & Agne Paulauskaite-Taraseviciene & Linas Eidimtas, 2022. "ANN Hybrid Model for Forecasting Landfill Waste Potential in Lithuania," Sustainability, MDPI, vol. 14(7), pages 1-16, March.

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