Artificial neural network models to support urban waste management: A technological resource that drives the achievement of Sustainable Development Goals

Modelos de redes neurais artificiais para apoiar a gestão de resíduos urbanos: um recurso tecnológico que impulsiona a concretização dos Objetivos de Desenvolvimento Sustentável

ABSTRACT

Waste management is crucial to achieving the Sustainable Development Goals (SDGs) established by the United Nations. However, traditional on-site waste characterization techniques require specialized professionals, who are exposed to biological, chemical, and physical risks. In this sense, the use of artificial neural networks (ANN) in models for characterizing municipal solid waste has been discussed, especially after the advent of the COVID-19 pandemic. Predictions made by ANN can be carried out with little or no handling of waste, making the process faster, cleaner, and safer. However, ANN models rely on datasets often provided by third parties, so they require diligent monitoring to ensure that an updated dataset is available at the appropriate regularity. This study presented two standard ANN models that were not available due to a lack of up-to-date datasets and demonstrated that dataset interchangeability may be critical for the long-term use of ANN developed to achieve SDG. Furthermore, interchangeability led to the formulation of a hypothesis about the relevance of the variable associated with basic sanitation in the greater assertiveness of one of the models during the pandemic period, resulting in the identification of abnormal patterns relating to the disposal of textiles and sanitary papers in the years 2020 and 2021. Additionally, this study can be the starting point for the development of more sophisticated interchangeable models developed with alternative datasets, meticulously chosen to reduce the effective error of the desired predictions by reducing the amplitude of the intersection set formed by different models.

Keywords:
waste management; artificial intelligence; socioeconomic; population profile; pandemic