Landfill area estimation based on solid waste collection prediction using ANN model and final waste disposal options

Hoque, Md. Maruful; Rahman, Mohd Tadza Abdul

doi:10.1016/j.jclepro.2020.120387

Cited by 82 publications

(30 citation statements)

References 30 publications

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“…Despite the good performance of the ML algorithms mentioned above (e.g. ANN, SVM, and ANFIS), they are considered as black boxes ( Hoque and Rahman, 2020 ). It is very difficult to gain a clear and insightful interpretation of these models owing to the sophisticated relationship between the variables and the output.…”

Section: Discussionmentioning

confidence: 99%

Application of machine learning algorithms in municipal solid waste management: A mini review

et al. 2021

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Population growth and the acceleration of urbanization have led to a sharp increase in municipal solid waste production, and researchers have sought to use advanced technology to solve this problem. Machine learning (ML) algorithms are good at modeling complex nonlinear processes and have been gradually adopted to promote municipal solid waste management (MSWM) and help the sustainable development of the environment in the past few years. In this study, more than 200 publications published over the last two decades (2000–2020) were reviewed and analyzed. This paper summarizes the application of ML algorithms in the whole process of MSWM, from waste generation to collection and transportation, to final disposal. Through this comprehensive review, the gaps and future directions of ML application in MSWM are discussed, providing theoretical and practical guidance for follow-up related research.

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Section: Discussionmentioning

confidence: 99%

Application of machine learning algorithms in municipal solid waste management: A mini review

et al. 2021

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“…In addition to all the advantages related to the use of DM for knowledge extraction, it is also worth mentioning that the technique consists of a long and laborious process, in which collecting reliable data (quantity and quality) and treating them in the correct way for significant results can be a great challenge (Bagheri et al, 2019;Colvero et al, 2019;Fernández-Braña et al, 2021;Hoque and Rahman, 2020;Kumar et al, 2018;Niu et al, 2020). According to Kannangara et al (2018), the scarcity of data sources is related to infrastructure and waste management practices.…”

Section: Main Challenges and Opportunitiesmentioning

confidence: 99%

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

et al. 2021

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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.

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“…As mentioned above, modeling the C&T optimization process involves multiple variables and might be difficult due to the nonlinear behavior exhibited by these variables. Owing to its innovative surge and ability to handle large data, map nonlinear relationships, and solve complex problems at high speed, ANN has gained superiority over other models in C&T optimization modeling studies (Hoque & Rahman, 2020 ). As shown in Fig.…”

Section: Collection and Transportation Of Mswmentioning

confidence: 99%

Source separation, transportation, pretreatment, and valorization of municipal solid waste: a critical review

Zhang

Liu

Chen

et al. 2021

Environ Dev Sustain

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Landfill area estimation based on solid waste collection prediction using ANN model and final waste disposal options

Cited by 82 publications

References 30 publications

Application of machine learning algorithms in municipal solid waste management: A mini review

Application of machine learning algorithms in municipal solid waste management: A mini review

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

Source separation, transportation, pretreatment, and valorization of municipal solid waste: a critical review

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