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

Sabbir Ahmed (), Sameera Mubarak, Jia Tina Du and Santoso Wibowo
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Sabbir Ahmed: UniSA STEM, University of South Australia, Adelaide, SA 5001, Australia
Sameera Mubarak: UniSA STEM, University of South Australia, Adelaide, SA 5001, Australia
Jia Tina Du: UniSA STEM, University of South Australia, Adelaide, SA 5001, Australia
Santoso Wibowo: School of Engineering and Technology, Central Queensland University, Melbourne, VIC 3000, Australia

IJERPH, 2022, vol. 19, issue 24, 1-15

Abstract: 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 (R 2 ) 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.

Keywords: waste prediction; municipal; deep learning; time series; waste management (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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