Monthly and seasonal modeling of municipal waste generation using radial basis function neural network

Accurate modeling of municipal solid waste (MSW) generation is vital as a reliable support for decision-making processes ensuring the success of the future development and management of wastes. The present study aims to forecast monthly and seasonal MSW generation using radial basis function (RBF) neural network and assess the effect of the gender of educated people with a combination of meteorological, socioeconomic, and demographic variables on waste generation. The study was implemented on data obtained from a megacity for the period of 1991–2013. Cross validation technique was employed to evaluate modeling performance. Performance of the RBF model were also compared with adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) models. The results proved that the number of educated women was highly associated with MSW generation while the number of educated men was not a significant factor. Modeling outputs demonstrated that the RBF neural network model could successfully predict both monthly and seasonal variations of MSW generation. Compared to ANFIS and ANN, RBF was the best-performing model for monthly and seasonal forecasting of MSW generation. The results suggested that soft computing methods like RBF improve the estimate of MSW generation in metropolises. Hence, RBF network can be applied for forecasting and modeling MSW generation on a national scale.