Construction and Demolition Waste Generation Forecasting Using a Hybrid Intelligent Method

Ting Cai, Gangqing Wang, Zhaoxia Guo
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引用次数: 2

Abstract

To effectively forecast the construction and demolition waste (C&DW) generation, this paper proposes a hybrid intelligent method, which integrates singular spectrum analysis (SSA), support vector regression (SVR) and long and short-term memory (LSTM) network. The key idea of this method is to use SSA to divide the original time series into trend and fluctuation sub-series, separately perform LSTM and SVR approach, then to combine the forecasts generated from each model. Grid search optimization is further utilized to seek for the optimal parameters of the proposed forecasting model. Experiments are conducted to compare the proposed method and several benchmark approaches. Results show that it is statistically significant that the proposed method can provide much better forecasts than other approaches, indicating that our method can be used as a promising tool for the C&DW generation forecasting.
基于混合智能方法的建筑与拆除垃圾产生量预测
为有效预测建筑垃圾的产生,提出了一种将奇异谱分析(SSA)、支持向量回归(SVR)和长短期记忆(LSTM)网络相结合的混合智能预测方法。该方法的关键思想是利用SSA将原始时间序列划分为趋势子序列和波动子序列,分别进行LSTM和SVR方法,然后将每个模型生成的预测结果进行组合。进一步利用网格搜索优化方法寻找预测模型的最优参数。通过实验将该方法与几种基准方法进行了比较。结果表明,该方法的预测效果明显优于其他方法,表明该方法可以作为一种有前途的C&DW生成预测工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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