Time-series-based hybrid mathematical modelling method adapted to forecast automotive and medical waste generation: Case study of Lithuania.

The aim of the study was to create a hybrid forecasting method that could produce higher accuracy forecasts than previously used 'pure' time series methods. Mentioned methods were already tested with total automotive waste, hazardous automotive waste, and total medical waste generation, but demonstrated at least a 6% error rate in different cases and efforts were made to decrease it even more. Newly developed hybrid models used a random start generation method to incorporate different time-series advantages and it helped to increase the accuracy of forecasts by 3%-4% in hazardous automotive waste and total medical waste generation cases; the new model did not increase the accuracy of total automotive waste generation forecasts. Developed models' abilities to forecast short- and mid-term forecasts were tested using prediction horizon.

Forecasting medical waste generation using short and extra short datasets: Case study of Lithuania.

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).