Modeling of municipal waste disposal behaviors related to meteorological seasons using recurrent neural network LSTM models

Abstract

The literature suggests that long short-term memory (LSTM) paired with recurrent neural network (RNN) can better express long- and short-term reliance of a data set. The study objectives are to quantify mixed waste disposal (MWD) behaviors at a Canadian landfill from 2013 to 2021, and develop separate RNN-LSTM models to predict MWD rates under four meteorological seasons. Seasonal variations are clearly presented in the historical disposal data, with higher MWD of 417.8 t/month in summer and about 289.7 t/month in winter. The variabilities of MWD are also different among the seasons. Winter experienced the least variation, probably due to similarities in inhabitants' lifestyles. All seasonal sets are negatively skewed, and the highest skewness is observed in summer. The overall model performance using the entire data range is generally satisfactory, with R2 values between 0.72–0.86. Meteorological seasons appear to be a significant factor in waste disposal rate modeling. The model performances are less reliable for smaller disposal rates <200 t/day, with 0.01 < R2 < 0.59. The results suggest the disposal behaviors on a quiet day can be quite different. The use of distinct time series related to seasons on MWD modeling is original. The proposed analytical approach provides an alternative waste modeling approach accounting for both short term (seasonal) and longer term (annual) effects.