基于长短期记忆网络的城市建筑垃圾产量预测  

LSTM-Based Forecasting for Urban Construction Waste Generation

作　　者：孙柯华 蔡婷 王伟[3] 吴晓南 刘弘昱 郑虢 Sun Kehua;Cai Ting;Wang Wei;Wu Xiaonan;Liu Hongyu;Zheng Guo(Shanghai Communications Construction Co.,Ltd.,Shanghai 200136,China;Business School,Sichuan University,Chengdu 610065,China;College of Harbour,Coastal and Offshore Engineering,Hohai University,Nanjing 210098,China)

机构地区：[1]上海交通建设总承包有限公司,上海200136 [2]四川大学商学院,四川成都610064 [3]河海大学港口海岸与近海工程学院,江苏南京210098

出　　处：《华东交通大学学报》2020年第6期28-35,共8页Journal of East China Jiaotong University

基　　金：国家自然科学基金项目(71974052);江苏省社会科学基金项目(18GLB013);江苏省水利科技项目(2018022)。

摘　　要：为了有效解决建筑垃圾预测问题,从有限样本点的单变量时序数据出发,提出一种基于3层长短期记忆(LSTM)网络的时间序列预测方法,涉及Dropout层与网络结构设计、网络训练与预测过程实现算法等。并以上海市建筑垃圾统计数据为例进行数值实验,通过与其他时间序列预测模型的实验对比,验证了LSTM预测模型在建筑垃圾产量预测的有效性和准确性。Accurately predicting the amount of construction waste is of great significance for carrying out the recycling treatment of construction waste and guiding the government to formulate relevant policies.However,the lack of reliable forecasting methods and historical data makes it difficult to predict the construction waste in the long-or short-term planning.On the basis of the univariate time series data of limited sample points,this paper puts forward a short and long memory(LSTM)time series prediction method to effectively solve the problem of construction waste prediction,which involves network structure with dropout layer and the algorithm of network training and prediction process.Taking Shanghai as a case,compared with other time series prediction models,numerical experiments were conducted to verify the effectiveness and accuracy of the LSTM prediction model in the filed of predicting construction waste generation.

关 键 词：建筑垃圾 LSTM网络 时间序列预测 深度学习 

分 类 号：U294.13[交通运输工程—交通运输规划与管理] TU993.3[交通运输工程—道路与铁道工程]