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Title	Smart Monitoring Of Waste Bins And Municipal Solid Waste Forecast Using Lstm For Smart Cities
ID_Doc	51237
Authors	Helmy M.; Hassan M.S.
Year	2024
Published	AIP Conference Proceedings, 3034, 1
DOI	http://dx.doi.org/10.1063/5.0195035
Abstract	Managing municipal waste is a critical issue in the modern world. The accumulation of waste and improper management has led to serious health and environmental problems. Traditionally, trashcans are distributed among different areas for waste collection. Unfortunately, this gives rise to serious problems including overflow of bins, which can cause spreading in surrounding areas, pollution and causing hazardous inconvenience to the public. Therefore, there is a need for smart and efficient monitoring systems that can predict and then prevent such incidents by notifying the municipality when collection is needed. This can be done using a model that is capable of forecasting short-term waste generation at the bin level to help in optimizing waste collection, where only bins that are expected to reach a certain level of fullness during the next shift will be collected. Such a system can reduce the operation cost while minimizing traveled distances, and eventually reduce emissions caused by collecting trucks. Consequently, we propose an IoT-based monitoring system for effective management of waste. The proposed system is equipped with a predictive module that uses historical waste collection data gathered by implanted sensors to forecast the daily waste generation and accordingly suggests a smart collection schedule. For this purpose, LSTM neural network was used for future waste generation. © 2024 Author(s).
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