Data mining and knowledge discovery in databases for urban solid waste management: A scientific literature review

Abstract

The processes related to solid waste management (SWM) are being revised as new technologies emerge and are applied in the area to achieve greater environmental, social and economic sustainability for society. To achieve our goal, two robust review protocols (Population, Intervention, Comparison, Outcome, and Context (PICOC) and Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA)) were used to systematically analyze 62 documents extracted from the Web of Science database to identify the main techniques and tools for Knowledge Discovery in Databases (KDD) and Data Mining (DM) as applied to SWM and explore the technological potential to optimize the stages of collecting and transporting waste. Moreover, it was possible to analyze the main challenges and opportunities of KDD and DM for SWM. The results show that the most used tools for SWM are MATLAB (29.7%) and GIS (13.5%), whereas the most used techniques are Artificial Neural Networks (35.8%), Linear Regression (16.0%) and Support Vector Machine (12.3%). In addition, 15.3% of the studies were conducted with data from China, 11.1% from India and 9.7% of the studies analyzed and compared data from several other countries. Furthermore, the research showed that the main challenges in the field of study are related to the collection and treatment of data, whereas the opportunities appear to be linked mainly to the impact on the pillars of sustainable development. Thus, this study portrays important issues associated with the use of KDD and DM for optimal SWM and has the potential to assist and direct researchers and field professionals in future studies.

Covid-19 Outbreak in Brazil: Health, Social, Political, and Economic Implications

“…32 The ISI Web of Science Core Collection database was searched for eligible publications, as it has a large number of research documents from high-impact journals. [33][34][35][36][37][38] The documents were exported on August 14, 2021. Quality assessment attributes are shown in Table 2.…”

Global knowledge base for municipal solid waste management: Framework development and application in waste generation prediction

“…SWMS-related knowledge can be categorized into three major components: the socioeconomic context of SWMS, the SWMS themselves, and the performance of SWMS. The regional socioeconomic context of SWMS consists of the general public (characterized by its demographics, lifestyles, and education/environmental awareness), regional economy, climate patterns, and governance (Abdallah et al, 2020;Beigl et al, 2008;Dias et al, 2021;Kolekar et al, 2016). The broader socioeconomic context of SWMS includes domestic and international trades, as well as markets for secondary and primary materials (Brooks et al, 2018;Das et al, 2019;Matter et al, 2015).…”

“…First, existing MSW data are mainly stored in "silos" confined by geographical boundaries or topical focuses (Cohen and Gil, 2021). There is not a single centralized repository to organize all the information in a systemic manner, which is verified by the lack of relevant peer-reviewed articles on multi-attribute MSW knowledge systems (Dias et al, 2021). Second, due to the financial and organizational costs of data collection, waste characterization studies are infrequently conducted or reported, especially in low-income countries (Kaza et al, 2018).…”

Sociedade 5.0: explorando os dilemas do ecossistema social do futuro

“…Para atender ao objetivo do estudo, realizou-se uma revisão sistemática da literatura, de cunho crítico-reflexivo, apoiada pelo protocolo PRISMA-P. A base de dados ISI Web of Science (WoS) Core Collection foi utilizada por se tratar de uma base de dados robusta com trabalhos de qualidade e revistas de alto fator de impacto indexadas (SEVERO et al, 2021;KOLLING et al, 2021;DIAS et al, 2021;SOTT et al, 2022). Foram selecionadas apenas revisões da literatura, bibliometrias e mapeamentos científicos do campo de estudo, a fim de refinar as buscas, considerando que os estudos dos tipos selecionados devem ser completos e abrangentes por já terem revisado a literatura previamente.…”