Forecasting urban construction and demolition waste generation with LSTM neural networks.

This study article emphasises the Long Short-Term Memory (LSTM) model while utilising cutting-edge machine learning techniques to provide a thorough investigation into the subject of predicting the development of construction and demolition trash. For urban planners and environmentalists, the efficient management of building waste is a critical issue given global trends towards growing urbanisation. We carefully preprocessed the sizeable historical data sets from Shanghai and Hong Kong used in this study to ensure quality and representativeness. The study compares the LSTM model to machine learning techniques like Artificial Neural Networks (ANN), Random Forests (RF), and Support Vector Machines (SVM). When the model's ability to predict is thoroughly examined, it is clear that the LSTM routinely outperforms its competitors in terms of precision, F1 score, recall, and accuracy. Our findings also show how cutting-edge machine learning has the revolutionary potential to solve waste management and urban planning issues. LSTM and similar models provide a practical way to lessen environmental impact and lower waste disposal costs in urban settings by maximising resource allocation and promoting sustainable waste management practises. The ongoing discussion about garbage disposal in structures and sustainable urban development is furthered by this effort. It demonstrates the significance of LSTM and modern machine learning algorithms in transforming waste management paradigms and ultimately pointing us in the direction of a more environmentally friendly and sustainable future. [ABSTRACT FROM AUTHOR]