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Prediction of Municipal Waste Generation in Poland Using Neural Network Modeling

Author

Listed:
  • Monika Kulisz

    (Management Faculty, Lublin University of Technology, 20-618 Lublin, Poland)

  • Justyna Kujawska

    (Environmental Engineering Faculty, Lublin University of Technology, 20-618 Lublin, Poland)

Abstract

Planning is a crucial component of short- and long-term municipal waste management. Establishing the relationships between the factors that determine the amount of waste generated by municipalities and forecasting the waste management needs plays a fundamental role in the development of effective planning strategies and implementation of sustainable development. Artificial Neural Network employed for verifying the forecasts pertaining to the amount of rainfall in Poland were presented in the studies. The proposed models included selected explanatory indices in order to reflect the impact of social, demographic and economic factors on the amount of generated waste. Mean squared error ( MSE ) and regression value ( R ) are used as indices of efficiency of the developed models. The ANN models exhibited high accuracy of forecasts at high R values ( R = 0.914, R = 0.989) and low MSE values. Derived from the socioeconomic data for 2003–2019, the model predicts that the future waste generation in 2024 will increase by 2%. The results indicate that the employed ANN models are effective in predicting the amount of waste and can be considered a cost-effective approach to planning integrated waste management systems.

Suggested Citation

  • Monika Kulisz & Justyna Kujawska, 2020. "Prediction of Municipal Waste Generation in Poland Using Neural Network Modeling," Sustainability, MDPI, vol. 12(23), pages 1-16, December.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:23:p:10088-:d:455465
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    References listed on IDEAS

    as
    1. Soolmaz L. Azarmi & Akeem Adeyemi Oladipo & Roozbeh Vaziri & Habib Alipour, 2018. "Comparative Modelling and Artificial Neural Network Inspired Prediction of Waste Generation Rates of Hospitality Industry: The Case of North Cyprus," Sustainability, MDPI, vol. 10(9), pages 1-18, August.
    2. Yanmin He & Hideki Kitagawa & YeeKeong Choy & Xin Kou & Peii Tsai, 2020. "What Affects Chinese Households’ Behavior in Sorting Solid Waste? A Case Study from Shanghai, Shenyang, and Chengdu," Sustainability, MDPI, vol. 12(21), pages 1-21, October.
    3. Jozef Švajlenka & Mária Kozlovská & Terézia Pošiváková, 2018. "Analysis of Selected Building Constructions Used in Industrial Construction in Terms of Sustainability Benefits," Sustainability, MDPI, vol. 10(12), pages 1-16, November.
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    Cited by:

    1. Justyna Kujawska & Monika Kulisz & Piotr Oleszczuk & Wojciech Cel, 2023. "Improved Prediction of the Higher Heating Value of Biomass Using an Artificial Neural Network Model Based on the Selection of Input Parameters," Energies, MDPI, vol. 16(10), pages 1-16, May.
    2. Beáta Stehlíková & Katarína Čulková & Marcela Taušová & Ľubomír Štrba & Eva Mihaliková, 2021. "Evaluation of Communal Waste in Slovakia from the View of Chosen Economic Indicators," Energies, MDPI, vol. 14(16), pages 1-22, August.
    3. Eda Puntarić & Lato Pezo & Željka Zgorelec & Jerko Gunjača & Dajana Kučić Grgić & Neven Voća, 2022. "Prediction of the Production of Separated Municipal Solid Waste by Artificial Neural Networks in Croatia and the European Union," Sustainability, MDPI, vol. 14(16), pages 1-13, August.

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