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SMEJKALOVÁ, V. ŠOMPLÁK, R. NEVRLÝ, V. PAVLAS, M.
Original Title
Design and Decomposition of Waste Prognostic Model with Hierarchical Structures
Type
journal article in Scopus
Language
English
Original Abstract
The waste management is a dynamically progressive area, with the current trend leading to circular economy scheme. The development in this area requires quality prognosis reflecting the analysed timeframe. The forecast of the waste production and composition of waste is an important aspect with regards to the planning in waste management. However, the regular prognostic methods are not appropriate for these purposes due to short time series of hisorical data and unavailability of socio-economic data. The paper proposes a general approach via mathematical model for forecasting of future waste-related parameters based on spatially distributed data with hierarchical structure. The approach is based on principles of regression analysis with final balance to ensure the compliance of aggregated data values. The selection of the regression function is a part of mathematical model for high-quality description of data trend. In addition, outlier values are cleared, which occur abundantly in the database. The decomposition of the model into subtasks is performed in order to simpler implementation and reasonable time solvability. The individual algorithm steps are applied to municipal waste production data in the Czech Republic.
Keywords
waste production, forecasting, prognostic model, short time series, regression analysis, nonlinear regression
Authors
SMEJKALOVÁ, V.; ŠOMPLÁK, R.; NEVRLÝ, V.; PAVLAS, M.
Released
26. 6. 2018
Publisher
VUT
Location
Brno
ISBN
1803-3814
Periodical
Mendel Journal series
Year of study
2018
Number
1
State
Czech Republic
Pages from
85
Pages to
92
Pages count
8
URL
https://mendel-journal.org/index.php/mendel/article/view/27
BibTex
@article{BUT149956, author="Veronika {Smejkalová} and Radovan {Šomplák} and Vlastimír {Nevrlý} and Martin {Pavlas}", title="Design and Decomposition of Waste Prognostic Model with Hierarchical Structures", journal="Mendel Journal series", year="2018", volume="2018", number="1", pages="85--92", doi="10.13164/mendel.2018.1.085", issn="1803-3814", url="https://mendel-journal.org/index.php/mendel/article/view/27" }