Details

Autor(en) / Beteiligte

• Vu, Hoang Lan
• Ng, Kelvin Tsun Wai
• Bolingbroke, Damien

Titel

Time-lagged effects of weekly climatic and socio-economic factors on ANN municipal yard waste prediction models

Ist Teil von

• Waste management (Elmsford), 2019-02, Vol.84, p.129-140

Ort / Verlag

United States: Elsevier Ltd

Erscheinungsjahr

2019

Quelle

ScienceDirect

Beschreibungen/Notizen

• •The importance of climatic variables and their lag effects was revealed.•Climatic group seemed to have better results than socio-economic or hybrid group.•Optimal lag time of the weekly yard models generally ranged from 1 to 5 weeks.•The lag time predicted by the training and testing sets agreed within ±1 week.•The optimal ANN model was the Temp-Pop model with a 2-11-1 structure. Efficient and effective solid waste management requires sufficient ability to predict the operational capacity of a system correctly. Waste prediction models have been widely studied and these models are always being challenged to perform more accurately. Unlike waste prediction models for mixed wastes, variables for yard waste are time sensitive and the effects of lag must be explicitly considered. This study is the first to specifically look at lag times relating to variables that attempt to predict municipal yard waste generation using machine learning approaches. Weekly averaged climatic and socio-economic variables are screened through correlation analysis and the significant variables are then used to develop yard waste models. These models then utilize artificial neural networks (ANN) where the variables are time lagged for a different number of weeks. This helps to realize a reduction in the error of the predicted weekly yard waste generation. Optimal lag times for each model varied from 1 to 11 weeks. The best model used both the ambient air temperature and population variables, in an ANN model with 3 layers, 11 neurons in the hidden layer, and an optimal lag time of 1 week. A mean absolute percentage error of 18.72% was obtained during the testing stage. One model saw a 55.4% decrease in the mean squared error at training, showing the value of lag time on the accuracy of weekly yard waste prediction models.