We will like to know the most appropriate way to model and predict the effect of socioeconomic features on waste generation in three different income class areas, ie low, medium and high income areas.
Researcher have employed artificial neutral networks, time series intervention, grey fuzzy dynamic, two-stage support- vector-regression models.
Please consider these papers
1. Abbasi M, Abduli MA, Omidvar B, Baghvand A (2012) Results uncertainty of support vector machine and hubrid of wavelet transform-support vector machine models for solid waste generation forecasting. Environmental progress and Sustainable Energy 00:1-9.
2. Abdoli M A, Nezhad M F, Sede R S, Behboudian S (2011) Long term forecasting of solid waste generation by the artificial neural networks. Environmental Progress and Sustainable Energy 31 (4):628-636.
3. Antanasijevic D, Pocajt V, Popovic I, Redzic N, Ristic M (2013) The forecasting of municipal waste generation using artificial neural networks and sustainability indicators. Sustain Science 8:37-46.
4. Beigl P, Lebersorger S, Salhofer S (2008) Modelling municipal solid waste generation: A review. Waste Management 28:200-214.
5. Chang NB, Lin YT (1997) An analysis of recycling impacts on solid waste generation by time series intervention modeling. Resources Conservation and Recycling 19:165–186.
6. Chen HW, Chang NB (2000) Prediction analysis of solid waste generation based on grey fuzzy dynamic modeling. Resources,
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