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

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 (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.

Optimized Operation Management With Predicted Filling Levels of the Litter Bins for a Fleet of Autonomous Urban Service Robots

“…Yusoff et al also developed a sensor device which was deployed to a university [43]. While all other approaches used classical discriminative ML methods such as Linear Regressions or Support Vector Machines, Ahmed et al applied time-series models to a historical dataset of bin filling levels for the city of Wyndham, Melbourne, Australia, which were obtained using smart sensors [44]. Walk et al also used a time-series model to produce hourly forecasts.…”

Global Models of Smart Cities and Potential IoT Applications: A Review

“…IoT-generated data from waste bins, collection vehicles, and landfill sensors can be analyzed to identify patterns and trends in waste generation, collection efficiency, and recycling rates. This data-driven approach allows municipalities and waste management companies to optimize their operations further and make informed decisions [85].…”

Environmental and Health Consequences of E-Waste Dumping and Recycling Carried out by Selected Countries in Asia and Latin America

“…Despite the establishment of national regulations and hazardous waste laws in developing countries (including the selected countries), the majority of the e-waste generated is still treated as general waste and is informally recycled. The rising growth of e-waste generated in developing countries has fuelled the enlargement of a pervasive and low-cost informal recycling sector that is inherently hazard-ridden [6,7,[178][179][180][181][182][183][184][185].…”