Optimizing Urban Waste Collection and Recycling using the LSTM-ARIMA Method

Developed and underdeveloped nations alike prioritize urbanization. More and more people are moving to cities in quest of better education and other contemporary conveniences, which poses a serious problem for waste management. Despite the clear association between urban population increase and waste output, many cities still use outdated, inefficient, and expensive methods for sorting garbage. Urban rubbish collection and recycling rely heavily on automated waste sorting and management technologies. The data preparation, feature extraction, and model training processes in this work are conducted in a systematic manner. Prior to analysis, data was preprocessed. We trained an ARIMA-LSTM model to improve predictive accuracy after extracting features with Baker PCA and Fisher's LDA. Outperforming current best practices, the suggested method improves accuracy by 97.45%. These results demonstrate the potential for predictive modelling and automation to improve recycling and trash collection in urban areas. Innovative, data-driven approaches can help cities reduce expenses, environmental impacts, and waste management challenges. Research in the future should primarily concentrate on how well automation technologies scale in urban settings.