Automated Assessment of Municipal Solid Wastes Using a Hybrid Sine Cosine Algorithm-Based Neural Network

Municipal solid waste management has emerged in the recent few years in contemporary built environments due to the rapid increase in population and urbanization. Hence, this research aims at developing a hybrid sine cosine algorithm-based feed-forward artificial neural network model for forecasting waste quantities in Poland. In addition, the developed hybrid model is compared against the classical feed-forward artificial neural network model. The performance evaluation analysis is explored using the indicators of mean bias error, root-mean-squared error, Pearson correlation coefficient, Willmott’s index of agreement, and coefficient of efficiency. Test results illustrated that the developed hybrid feed-forward artificial neural network model trained using sine cosine algorithm significantly outperformed the classical neural network model. It can be argued that the developed model could assist decision-makers in the proper management of growing quantities of municipal solid wastes.