Recent advances in applications of artificial intelligence in solid waste management: A review

Ihsanullah, I.; Alam, Gulzar; Jamal, Arshad; Shaik, Feroz

doi:10.1016/j.chemosphere.2022.136631

Cited by 64 publications

(31 citation statements)

References 96 publications

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“…There has been a scaleup in the advancement of operative waste management systems adopting innovative technologies, which is vital to tackle the issues faced by unconventional methods (Ihsanullah et al, 2022). Waste management is beneficial from an environmental, social, and economic perspective (Sahar, 2019).…”

Section: Benefits Of Solid Waste Managementmentioning

confidence: 99%

Influence of Recovery Efforts on Solid Waste Management Practices in Nigeria: A Review

Moro¹

2023

ETSJ

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The predominance of unwanted materials lying fallow in the environment, and the availability of information on waste management, offer the opportunity to explore the use of these resources as sources of recovered material. Regulatory drivers aim to harmonise the recovery potentials of wastes, thereby reducing the volumes sent to landfill. In this vein, this paper investigates the emissions from municipal solid wastes, which can lead to major environmental impacts. This study assessed the influence of recovery efforts on solid waste management practices in Nigeria. This was done by examining available literature chronicling pollution from unregulated dumpsites which can bioaccumulate in the environment, causing climatic impacts. It was discovered that emissions from waste can impact soil, water, and air, thus contributing to greenhouse gas emissions. Many developed countries have recorded significant improvement in adopting treatment facilities that can enable them to meet up sustainable development goals. However, developing countries still need assistance in developing a sustainable waste management programme that can help change their lifestyle, thus improving the management of the waste they produce. It becomes imperative that making the right choices can change the consumption pattern, thus, reducing the influx of pollutants whilst fostering a clean environment. Thus, a high-level recovery of resources can secure a sustainable future and a healthy environment.

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Section: Benefits Of Solid Waste Managementmentioning

confidence: 99%

Influence of Recovery Efforts on Solid Waste Management Practices in Nigeria: A Review

Moro¹

2023

ETSJ

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“…Artificial intelligence provides several methods that can be used to build intelligent waste management systems (Sinthiya et al, 2022 ; Abdoli et al, 2011 ). AI-based systems are used to solve challenging issues, deal with uncertainty, and demonstrate the effectiveness of intelligent systems (Ihsanullah et al, 2022 ). Integrated solid waste management strategies seek to generate methodologies that allow reducing the volume of garbage dumped in landfills.…”

Section: Introductionmentioning

confidence: 99%

A hybrid machine learning-mathematical programming optimization approach for municipal solid waste management during the pandemic

Ochoa-Barragán

Munguía-López

Ponce-Ortega

2023

Environ Dev Sustain

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This paper provides a mathematical optimization strategy for optimal municipal solid waste management in the context of the COVID-19 epidemic. This strategy integrates two approaches: optimization and machine learning models. First, the optimization model determines the optimal supply chain for the municipal waste management system. Then, machine learning prediction models estimate the required parameters over time, which helps generate future projections for the proposed strategy. The optimization model was coded in the General Algebraic Modeling System, while the prediction model was coded in the Python programming environment. A case study of New York City was addressed to evaluate the proposed strategy, which includes extensive socioeconomic data sets to train the machine learning model. We found the predicted waste collection over time based on the socioeconomic data. The results show trade-offs between the economic (profit) and environmental (waste sent to landfill) objectives for future scenarios, which can be helpful for possible pandemic scenarios in the following years. Supplementary Information The online version contains supplementary material available at 10.1007/s10668-023-03354-2.

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“…For example, overflow of bins can make the environment dirty and unhealthy, especially in public places, which is hazardous to citizens’ health. Therefore, a proper vacant schedule, appropriate bin placement and uncertainty prediction are huge challenges [ 11 , 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, several researchers reported that various models and hybrid techniques have been used to predict municipal waste used for quantity estimation, segregation and recycling of solid waste [ 11 ]. An artificial neural network (ANN) is widely used in a complex system, which demonstrates its performance in processing nonlinear data [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Forecasting the Status of Municipal Waste in Smart Bins Using Deep Learning

Ahmed

Mubarak

et al. 2022

IJERPH

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The immense growth of the population generates a polluted environment that must be managed to ensure environmental sustainability, versatility and efficiency in our everyday lives. Particularly, the municipality is unable to cope with the increase in garbage, and many urban areas are becoming increasingly difficult to manage. The advancement of technology allows researchers to transmit data from municipal bins using smart IoT (Internet of Things) devices. These bin data can contribute to a compelling analysis of waste management instead of depending on the historical dataset. Thus, this study proposes forecasting models comprising of 1D CNN (Convolutional Neural Networks) long short-term memory (LSTM), gated recurrent units (GRU) and bidirectional long short-term memory (Bi-LSTM) for time series prediction of public bins. The execution of the models is evaluated by Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Coefficient determination (R2) and Root Mean Squared Error (RMSE). For different numbers of epochs, hidden layers, dense layers, and different units in hidden layers, the RSME values measured for 1D CNN, LSTM, GRU and Bi-LSTM models are 1.12, 1.57, 1.69 and 1.54, respectively. The best MAPE value is 1.855, which is found for the LSTM model. Therefore, our findings indicate that LSTM can be used for bin emptiness or fullness prediction for improved planning and management due to its proven resilience and increased forecast accuracy.

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Recent advances in applications of artificial intelligence in solid waste management: A review

Cited by 64 publications

References 96 publications

Influence of Recovery Efforts on Solid Waste Management Practices in Nigeria: A Review

Influence of Recovery Efforts on Solid Waste Management Practices in Nigeria: A Review

A hybrid machine learning-mathematical programming optimization approach for municipal solid waste management during the pandemic

Forecasting the Status of Municipal Waste in Smart Bins Using Deep Learning

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