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. Study one quantifies 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 tonnes/month in summer and about 289.7 tonnes/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 left-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 less than 200 tonnes/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. Study two further explored the use of astronomical seasons in municipal solid waste disposal rates modeling. The study quantifies seasonal variations of municipal solid waste (MSW) disposal rates in a mid-sized Canadian city with respect to both meteorological and astronomical seasons using RNN – LSTM models. Meteorological seasons are related to the annual temperature cycle, whereas the astronomical seasons are based on the position of Earth in relation to the Sun. It is hypothesized that the number of hours of natural daylight could also affect MSW generation rates. The use of both meteorological and astronomical seasons in waste modeling is original. During the study period, considerably higher rates in summer (417.8-418.2 tonne/day) than in winter (289.0-290.0 tonne/day) were observed. Waste disposal behaviors in winter are however more consistent. The astronomical models appear to better handle extreme situations in the winter and summer. The results indicate that the astronomical models may be more appropriate in some cases. Overall, the predictive accuracy of all models are acceptable, with R2 ranging from 0.70 to 0.86. In general, meteorological models outperformed astronomical models slightly, with RMSE ranged from 72-95 tonne/day. The RMSE of the winter sets are lower, probably due to the lower disposal rates in winter. The results generally support the use of astronomical data to supplement meteorological data in waste seasonal variation studies. The results of both the studies will help policy makers to better implement solid waste management strategies in both meteorological and astronomical seasons.