Due to inefficient waste sorting in primary and secondary waste generation sources Lithuania fails in trying to meet EU requirements for waste management sector regarding the amount of waste flow that reaches landfills. Especially sensitive situation is with hazardous waste, which often are disposed along with municipal solid waste and with it reaches landfills and due to the fact that mechanical and biological treatment plant are only now being established in the biggest cities of Lithuania, landfills becomes a big issue. The main purpose of this research is to find out which mathematical modelling methods could be fitted and if it is possible to forecast annual hazardous waste generation by using automotive, medical and daylight lamps waste generation statistical data. This is part of a research of medical, automotive and daylight lamps waste generation forecasting possibilities. Tests on the performance of artificial neural networks, multiple linear regression, partial least squares, support vector machines and four nonparametric regression methods were conducted on two developed data sets. The best and most promising results in both cases were demonstrated by generalized additives method (R2 = 0.99) and kernel regression (R2 = 0.99).

Article in: English

Article published: 2016-10-24