An Effective Method for Managing Waste in Smart Cities Based on Unified CNN and LSTM Approach

The government of India launched programs like Smart Cities and the Clean Development Mission to build eco-friendly and technologically advanced cities. Solid waste management (SWM) is becoming more important to these types of programs. Because of unplanned urbanization and the fast increase of urban populations (as a result of migration), the dynamics of urban waste are changing, making it extremely difficult for local authorities in urban areas to design an efficient SWM policy. The development of a long-term SWM plan that is in line with the goals of government initiatives requires an in-depth knowledge of the waste qualities, quantities, and present management processes. Data preprocessing, feature extraction, and training the model ought to take precedence. Data from the characterization are linked to the entire magnitude of that influence for the relevant area and period in this step of preprocessing, which is where normalization is applied. It employs principal component analysis (PCA) for feature extraction. Feature selection is an essential first step in training unified CNN-LSTM models. Compared to the current front-runners, CNN and LSTM, the proposed technique performs far better. The accuracy was increased by 95.85% when the method was used.