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

Abstract

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 of the unknown end-of-life tire flow for control and decision making in urban solid waste management: A case study

“…Abbasi and El Hanandeh (2016) and Abunama et al (2018) focused on the management of collection system operation, as well as usage of MSW. Other works (Adamović et al, 2018; Antanasijević et al, 2013; Chhay et al, 2018; Karpušenkaitė et al, 2018; Singh et al, 2018; Singh and Dhiman, 2018) developed ANN models for short-step ahead prediction related to MSW.…”

Ensemble Voting Regression Based on Machine Learning for Predicting Medical Waste: A Case from Turkey

“…The authors concluded that ANN outperformed MLR in dealing with the nonlinearity between dependent and independent variables. Karpušenkait ė et al [19] developed a hybrid model using the coefficients generated by moving average (MA) and Holt's method in the regression equation. They proved that the waste generation rate was positively correlated with the numbers of inpatients and outpatients.…”

“…According to a number of studies, classical and ML-based algorithms for time-series data competed, just as for prediction issues [21,22]. Survey MLR R-squared [13] Survey MLR R-squared [14] Survey MLR R-squared [15] Survey MLR R-squared [16] Official data LR R-squared [17] Official data ARIMA MAPE, MAE [18] Official data ANN, MLR, PLS, and SVM R-squared, RMSE, MAE, MAPE [19] Official data MA, Holt's MAPE [20] Survey MLR, ANN MAE, RMSE, R-squared ML algorithms can be used to estimate MW amounts in order to discover trends, patterns, and changes with greater accuracy than traditional regression analysis, as previous research has demonstrated [9]. Furthermore, most studies estimating MW quantity have not included the most substantial input factors, which may be critical information for an effective medical waste management system.…”

Forecasting of Disassembly Waste Generation under Uncertainties Using Digital Twinning-Based Hidden Markov Model

“…The existing approaches for waste generation forecasting are classified into four main categories: traditional statistical models [9,10], the gray and fuzzy models [11], simulation models [12], and nonprobabilistic statistical learning models [13]. In terms of traditional statistical models, Karpušenkait ė [14], Box [15], Giannouli [16], Chen [17], Althaf [18], and others have studied the time series prediction of uni-variate models involving environmental applications. Denafas [19] applied the municipal waste composition data to a time series prediction model, which can quantitatively estimate seasonally changing waste generation.…”