Results uncertainty of solid waste generation forecasting by hybrid of wavelet transform-ANFIS and wavelet transform-neural network

Both planning and design of municipal solid waste management systems (MSWMS) require accurate prediction of waste generation (WG). In this study, the hybrid of wavelet transform-adaptive neuro-fuzzy inference system (WT-ANFIS) and wavelet transform-artificial neural network (WT-ANN) is used to predict the weekly WG in Tehran, concerning complexity and dynamic MSWMS. In order to input variables preprocessing is done by WT and then new variables entered to ANFIS and ANN models. Consequently, output uncertainty of WT-ANFIS and WT-ANN models is done. The results achieved in this research indicate the positive effect of input variables preprocessing by WT in the prediction of weekly WG in Tehran, and it has led to noticeable increase in the accuracy of two model calculations. However, WT-ANFIS model had better results than WT-ANN model, because of the smaller uncertainty than WT-ANN model.