CQUniversity
Browse

Forecasting the status of municipal waste in smart bins using deep learning

Download (1.92 MB)
journal contribution
posted on 2023-06-19, 01:49 authored by Sabbir Ahmed, Sameera Mubarak, Jia Du, Santoso WibowoSantoso Wibowo
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 forecast 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.

History

Volume

19

Issue

24

Start Page

1

End Page

15

Number of Pages

15

eISSN

1660-4601

ISSN

1660-4601

Publisher

MDPI

Publisher License

CC BY 4.0

Additional Rights

CC BY 4.0

Language

en

Peer Reviewed

  • Yes

Open Access

  • Yes

Acceptance Date

2022-12-12

External Author Affiliations

University of South Australia

Author Research Institute

  • Centre for Intelligent Systems

Era Eligible

  • Yes

Medium

Electronic

Journal

International Journal of Environmental Research and Public Health

Article Number

ARTN 16798

Usage metrics

    CQUniversity

    Categories

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC