Dumpster: Predictive Model to trade Bio-waste from Agricultural Lands

We present a survey of machine learning works that attempt to organize the process flow of waste management in smart cities. Unlike past reviews, we focused on the waste generation and disposal phases in which citizens, households, and municipalities try to eliminate their solid waste by applying intelligent computational models. To this end, we synthesized and reviewed 42 articles published between 2010 and 2021. We retrieved the selected studies from six major academic research databases. Next, we deployed a comprehensive data extraction strategy focusing on the objectives of studies, trends of ML adoption, waste datasets, dependent and independent variables, and AI-ML-DL predictive models of waste generation. Our analysis revealed that most studies estimated waste material classification, amount of generated waste per area, and waste filling levels per location. Demographic data and images of waste type and fill levels are used as features to train the predictive models. Although various studies have widely deployed artificial neural networks (ANN) and convolutional neural networks (CNN) to classify waste, other techniques, such as gradient boosting regression tree (GBRT), have also been utilized. Critical challenges hindering the prediction of solid waste generation and disposal include the scarcity of real-time time series waste datasets, the lack of performance benchmarking tests of the proposed models, the reliability of the analytics models, and the long-term forecasting of waste generation. Our survey concludes with the implications and limitations of the selected models to inspire further research efforts.

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Solid Waste Generation and Disposal Using Machine Learning Approaches: A Survey of Solutions and Challenges

Namoun

Tufail

Khan

et al. 2022

Sustainability

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Digitalization and Digital Applications in Waste Recycling: An Integrative Review

Onur,

Alan,

Demirel

et al. 2024

Sustainability

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The rapid growth of urbanization and industrialization has brought the issue of waste management to the forefront. Industrial, household, and medical waste management and disposal are major issues affecting the whole world. The adoption of digital technologies across society is largely a result of the increasing processing power of waste and decreasing costs. Waste management and recycling is also benefiting from emerging digital technologies. The Internet of Things, cloud computing, artificial intelligence, robotics, and data analytics are a few examples of specific digital technologies that are currently in use and are predicted to have a significant impact on the efficiency of the waste recycling industry in the future. The objective of this review, which was conducted using the bibliometric method and visualized with scientific mapping, is to demonstrate how the digital transformation of waste recycling has evolved over the last decade and to identify which issues have been overlooked or have become more prominent. The scope of the research is based on studies carried out all over the world and on digital applications and works in the field of waste recycling. In this review, bibliometric analysis was used to scan the entire field and the results were classified and interpreted according to the PRISMA (preferred reporting of systematic reviews and meta-analyses) methodology.

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Dumpster: Predictive Model to trade Bio-waste from Agricultural Lands

Cited by 2 publications

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Solid Waste Generation and Disposal Using Machine Learning Approaches: A Survey of Solutions and Challenges

Solid Waste Generation and Disposal Using Machine Learning Approaches: A Survey of Solutions and Challenges

Digitalization and Digital Applications in Waste Recycling: An Integrative Review

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