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One of the most challenging aspects of waste generation collection occurs during working hours. Sensors in smart cities are used to track the actual conditions of waste generation. These sensors assist in the monitoring of bin usage. However, controlling a single bin is impractical and requires many resources. Another approach will be to use historical data to assist decision-makers. Thus, based on historical data, this paper proposes an appropriate and effective method using recurrent neural networks (RNNs) to predict waste generation levels in smart cities. [ABSTRACT FROM AUTHOR]