Municipal solid waste data quality on artificial neural network performance

Journal article

Authors / Editors

Muzenda, Edison

Research Areas

ENGINEERING AND TECHNOLOGY (Frascati classification)

Publication Details

Subtitle: Municipal solid waste data quality on artificial neural network performance

Author list: Muzenda, Edison

Publisher: Elsevier

Publication year: 2017

ISSN: 0893-6080

URL: https://biust.pure.elsevier.com/en/publications/municipal-solid-waste-data-quality-on-artificial-neural-network-p

Languages: English-United States (EN-US)

Abstract

Short and long-term municipal solid waste (MSW) management requires adequate planning. Understanding the relationship among variables that affect MSW generation and predicting MSW based on them is needed for an effective planning. Methodologies to forecast MSW are numerous and have been implemented at different level of data granularity. Lack of data in many African cities and countries has hampered effective waste management plan. The lackof data has mainly been attributed to insufficient budget and lack of capacity to implement such management structure. In this study, we investigated the impact of data quality on forecasting efficiency using advanced prediction techniques. It was observed that the quality of waste related data variables determines the extent of model reliability and prediction accuracy.

Keywords

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