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- Title
PREDICTION MODELS FOR IMPROVING WASTE DECISION SUPPORT MANAGEMENT IN ROMANIA IN ASSOCIATION WITH V4 MEMBER COUNTRIES.
- Authors
PETREA, Stefan-Mihai; SIMIONOV, Ira-Adeline; ANTACHE, Alina; NICA, Aurelia; ANTOHI, Cristina; CRISTEA, Dragos Sebastian; ROŞU, Adrian; CALMUC, Valentina; ROŞU, Bogdan
- Abstract
The present study results are based on the application of XGBoost machine-learning algorithms and indicate that total waste, as a dependent parameter, can be accurately evaluated considering plastic wastes (feature importance-FI = 1.53, Rsq.= 0.75, RMSE = 0.47) in the case of V4 group, while for Romania, the dependent parameters identified as most reliable are chemical wastes (FI = 0.58) and industrial effluent sludges (FI = 0.04), with lower accuracy metrics (Rsq. = 0.46, RMSE = 0.75). In terms of waste treatment (WT), the portable batteries and accumulators' market (FI=0.45) presents high reliability to be used as the main predictor (Rsq. = 0.80, RMSE=0.42) for V4 support tool, while for Romania, the waste generation (FI = 1.57, Rsq.= 0.85, RMSE=0.36) highly explains the variability of WT. However, batteries and accumulators waste (FI = 0.77, Rsq. = 0.82, RMSE=0.39) can be used as a reliable predictor for WT variation in a more extended analytical framework, in the case of Romania. It can be concluded that waste decision support management can be supported based on ML models which are different in the case of Romania, compared to V4, emphasizing the regional importance when developing environmental modeling-based tools.
- Subjects
ROMANIA; MACHINE learning; PREDICTION models; WASTE treatment; PLASTIC scrap; INDUSTRIAL wastes; WATER treatment plant residuals
- Publication
Scientific Papers. Series E. Land Reclamation, Earth Observation & Surveying, Environmental Engineering, 2023, Vol 12, p158
- ISSN
2285-6064
- Publication type
Article