Design and Decomposition of Waste Prognostic Model with Hierarchical Structures

  • Veronika Smejkalova
  • Radovan Somplak
  • Vlastimir Nevrly
  • Martin Pavlas
Keywords: waste production, forecasting, prognostic model, short time series, regression analysis, nonlinear regression


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 historical 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.


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How to Cite
Smejkalova, V., Somplak, R., Nevrly, V., & Pavlas, M. (2018). Design and Decomposition of Waste Prognostic Model with Hierarchical Structures. MENDEL, 24(1), 85-92. Retrieved from