ANN Hybrid Model for Forecasting Landfill Waste Potential in Lithuania

Vidas Raudonis, Agne Paulauskaite-Taraseviciene and Linas Eidimtas
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Vidas Raudonis: Department of Automation, Kaunas University of Technology, Studentu 48, 51367 Kaunas, Lithuania
Agne Paulauskaite-Taraseviciene: Department of Applied Informatics, Kaunas University of Technology, Studentu 50, 51368 Kaunas, Lithuania
Linas Eidimtas: “Dts Solutions” UAB, Naujoji Str. 2, LT-60180 Raseiniai, Lithuania

Sustainability, 2022, vol. 14, issue 7, 1-16

Abstract: Waste management is currently a fast-growing environmental business and one of solutions to manage the huge amount of waste being generated on landfills is to use the disposed waste as an energy source. There is a major focus on energy forecasting, highlighting the importance of having reliable data on the volume and composition of municipal solid waste in landfills. However, the lack of historical data is forcing the development of machine-learning based models. This study contributes to this field by proposing a hybrid ANN-based model to forecast the total amount of landfill waste, different waste fraction and the potential for energy recovery. The proposed model includes an adaptive number of inputs adjusted to the relevant waste fraction and to the specific landfill. The obtained results substantiated that the proposed model allows for stable and accurate forecasting of recovered energy potential in cases where there is insufficient historical data. The experiments showed that the model with 12 inputs (meaning the forecast of the future value takes into account the last 12 months of data) was the most accurate in the energy forecasting task, with the lowest forecasting error in terms of mean absolute error −8.9878 gigawatt hours per year.

Keywords: municipal solid waste; landfill; forecasting; energy; artificial neural networks (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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