Forecasting the Status of Municipal Waste in Smart Bins Using Deep Learning
Bibliographic Data
| ID | 15460980 |
|---|---|
| Authors | Sabbir Ahmed (0000-0001-7969-1462, University of South Australia, corresponding author), Sameera Mubarak (0000-0002-0003-6682, University of South Australia), Jia Tina Du (0000-0002-3243-5768, University of South Australia), Santoso Wibowo (0000-0002-5318-8428, Central Queensland University) |
| Year | 2022 |
| Volume | 19 |
| Issue | 24 |
| Pages | 16798-16798 |
| Publication date | 2022-12-14 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph192416798 |
| PMID | 36554676 |
| OpenAlex | W4311510814 |
| Language | EN |
| Citations received | 2 |
| References cited | 43 |
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
Artificial neural network · Convolutional neural network · Data mining · Deep learning · Machine learning · Mean absolute error · Mean absolute percentage error · Mean squared error · Recurrent neural network · Statistics · Air Quality Monitoring and Forecasting · Computer Science · Mathematics · Municipal Solid Waste Management · Recycling and Waste Management Techniques · Artificial Intelligence
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Waste Mismanagement in Developing Countries
The coefficient of determination R-squared is more informative than SMAPE, MAE, Mape, MSE and RMSE in regression analysis evaluation
Bidirectional recurrent neural networks
Long Short-Term Memory
Organic Waste Buyback as a Viable Method to Enhance Sustainable Municipal Solid Waste Management in Developing Countries
Variables Influencing per Capita Production, Separate Collection, and Costs of Municipal Solid Waste in the Apulia Region (Italy)
| Unique citing works | 2 |
|---|---|
| Citations per year | 2 |
| Citation span | 2025 - 2026 (2) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 2 |