Longterm forecasting of solid waste generation by the artificial neural networks

Abstract

This study presents a new approach—preprocessing for reaching the stationary chain in time series—to unravel the interpolating problem of artificial neural networks (ANN) for long-term prediction of solid waste generation (SWG). To evaluate the accuracy of the prediction by ANN, comparison between the results of the multivariate regression model and ANN is performed. Monthly time series datasets, by the yrs 2000–2010, for the city of Mashhad, are used to simulate the generated solid waste. Different socioeconomic and environmental factors are assessed, and the most effective ones are used as input variables. The projections of these explanatory variables are used in the estimated model to predict the generated solid waste values through the yr 2032. Ultimately, various structures of ANN models are examined to select the best result based on the performance indices criteria. Research findings clearly indicate that such a new approach can be a practical method for long-term prediction by ANNs. Furthermore, it can reduce uncertainties and lead to noticeable increase in the accuracy of the long-term forecasting. Results indicate that multilayer perception approach has more advantages in comparison with traditional methods in predicting the municipal SWG. © 2011 American Institute of Chemical Engineers Environ Prog, 2011