title: Forecasting medical waste generation using short and extra short datasets: Case study of Lithuania.
abstract: The aim of the study is to evaluate the performance of various mathematical modelling methods, while forecasting medical waste generation using Lithuania's annual medical waste data. Only recently has a hazardous waste collection system that includes medical waste been created and therefore the study access to gain large sets of relevant data for its research has been somewhat limited. According to data that was managed to be obtained, it was decided to develop three short and extra short datasets with 20, 10 and 6 observations. Spearman's correlation calculation showed that the influence of independent variables, such as visits at hospitals and other medical institutions, number of children in the region, number of beds in hospital and other medical institutions, average life expectancy and doctor's visits in that region are the most consistent and common in all three datasets. Tests on the performance of artificial neural networks, multiple linear regression, partial least squares, support vector machines and four non-parametric regression methods were conducted on the collected datasets. The best and most promising results were demonstrated by generalised additive (R(2) = 0.90455) in the regional data case, smoothing splines models (R(2) = 0.98584) in the long annual data case and multilayer feedforward artificial neural networks in the short annual data case (R(2) = 0.61103).
authors.affiliation: Department of Environmental Technology, Kaunas University of Technology, Kaunas, Lithuania medianaa@gmail.com.
authors.forename: Aistė
authors.initials: A
authors.lastname: Karpušenkaitė
authors.affiliation: The Faculty of Mathematics and Natural Science, Kaunas University of Technology, Kaunas, Lithuania.
authors.forename: Tomas
authors.initials: T
authors.lastname: Ruzgas
authors.affiliation: Department of Environmental Technology, Kaunas University of Technology, Kaunas, Lithuania.
authors.forename: Gintaras
authors.initials: G
authors.lastname: Denafas
mesh_terms: D002648:Child; D016208:Databases, Factual; D006256:Hazardous Waste / Q000032:analysis / Q000706:statistics & numerical data; D006761:Hospitals; D006801:Humans; D008017:Life Expectancy; D016014:Linear Models; D008097:Lithuania; D008506:Medical Waste / Q000032:analysis* / Q000706:statistics & numerical data; D008962:Models, Theoretical; D016571:Neural Networks, Computer
chemical_list: D006256:Hazardous Waste; D008506:Medical Waste
keywords: Forecasting; generalised additives; generation; mathematical modelling; medical waste; smoothing splines