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

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.