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David Lomeling
Santino Wani Kenyi
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Santino Wani Kenyi
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Journal of Environment and Waste Management

Forecasting solid waste generation in Juba Town, South Sudan using Artificial Neural Networks (ANNs) and Autoregressive Moving Averages (ARMA)

David Lomeling* and Santino Wani Kenyi

Department of Agricultural Sciences, College of Natural Resources and Environmental Studies (CNRES), University of Juba, P.O. Box 82, Juba, South Sudan

Accepted 23 August 2017

Citation: Lomeling D and Kenyi SW (2017) Forecasting solid waste generation in Juba Town, South Sudan using Artificial Neural Networks (ANNs) and Autoregressive Moving Averages (ARMA). Journal of Environment and Waste Management 4(2): 211-223.

Copyright: © 2017 Lomeling and Kenyi. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Prediction of solid waste generation is critical for any long term sustainable waste management, especially of a fast-growing municipality. Lack of, or inaccurate solid waste generation records poses unparalleled challenges in developing cohesive and workable waste management strategies for any concerned authorities, as this is influenced by several interlinked demo-graphic, economic, and socio-cultural factors. The objective of this study was to compare two models in forecasting of MSW generation and how this would be built into an effective MSW management program. Two models, the Autoregressive Moving Average (ARMA 1,1) and the Artificial Neural Networks (ANNs) were tested for their ability to predict weekly waste generation of 14 households in Juba Town, Central Equatoria State (CES), South Sudan. Results showed that both the artificial intelligence model ANNs and the traditional ARMA model had good prediction performances; where for ANNs the RMSE, MAPE and r² were 0.080, 10.64%, 0.238 respectively, whereas for ARMA the RMSE, MAPE and r² were 0.102, 6.98% and 0.274 respectively. Both models showed no significant differences and could be therefore be used for Solid Waste (SW) forecasting. Based on the results, the weekly SW generated 52 weeks later (end of year) had reached 0.365 kg/capita indicating a 18.2% rise from 0.3 kg/capita at the start of the study. Under the current consumption rate, the weekly SW per capita in Juba Town is expected to reach 0.596 kg by 2020.

Keywords: Artificial Neural Networks, Autoregressive Moving Averages, Continuous Wavelet Transform, Waste Generation Forecasting,


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