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Predicting solid waste generation based on the ensemble artificial intelligence models under uncertainty analysis

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Abstract

Waste is a critical issue the modern world is facing, and its management involves many imperative parameters with distinct, negative impacts on the environment. From a fundamental perspective, the primary parameter in waste management is the very generation of waste. SWG standing for solid waste generation is a phenomenon influenced straightforwardly by many factors. This paper introduces an adaptive hybrid model based on the ensemble empirical mode decomposition (EEMD) and AI models to improve the prediction accuracy of monthly municipal SWG. First, to determine the optimum antecedent values of SWG, a partial autocorrelation function is used, and then all input/output variables are decomposed by EEMD to overcome the non-stationary and non-linearity of the time series in the SWG dataset. Data collected from Tehran between 1991 and 2013 helped evaluate the effectiveness of the suggested model. The proposed model was compared with several accredited single models to showcase its performance. Statistical metrics showed that suggested hybrid EEMD–MARS (R = 0.82, NSE = 0.67, RMSE = 9.656, MAE = 7.538) outperformed EEMD–LSSVM and standard LSSVM and MARS models for prediction of SWG. Additionally, the researchers confirmed the reliability of the results based on the uncertainty analysis using a Monte Carlo algorithm, concluding that the EEMD–MARS model had a higher certainty and displayed significant potential in simulating solid waste generation.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors are grateful to the learned reviewers and their critical comments. Their comments have guided the authors to improve the manuscript.

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Authors and Affiliations

  1. Department of Civil Engineering, Roudehen Branch, Islamic Azad University, Roudehen, Iran

    Forough Ghanbari & Amirpouya Sarraf

  2. Department of Civil Engineering, Pardis Branch, Islamic Azad University, Pardis, Iran

    Hamidreza Kamalan

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  1. Forough Ghanbari
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  2. Hamidreza Kamalan
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  3. Amirpouya Sarraf
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Correspondence to Hamidreza Kamalan.

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Ghanbari, F., Kamalan, H. & Sarraf, A. Predicting solid waste generation based on the ensemble artificial intelligence models under uncertainty analysis. J Mater Cycles Waste Manag 25, 920–930 (2023). https://doi.org/10.1007/s10163-023-01589-9

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  • DOI: https://doi.org/10.1007/s10163-023-01589-9

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