Analysis of Contributing Factors and Prediction of Urban Waste Generation Using PSO-ANN

This research examines the factors influencing waste generation in urban areas, with a focus on East Java, which has experienced increased waste due to population growth and urbanization. Using the Spearman correlation method, it was found that unemployment (ρ = 0.87) and population (ρ = 0.865) are significantly related to waste generation. However, HDI (ρ = -0.152) and population density (ρ = -0.169) are uncorrelated with waste generation. Furthermore, waste generation predictions will be built using the Particle Swarm Optimization-Artificial Neural Network (PSO-ANN) model. The modeling results showed that the PSO-ANN architecture with one hidden layer achieved RMSE of 0.125 and MAE of 0.109, while the model with two hidden layers achieved RMSE of 0.123 and MAE of 0.105. These findings indicate that the two-hidden-layer PSO-ANN model is more effective in predicting waste generation than the single-layer model. This study recommends exploring alternative methods and additional variables to provide a more comprehensive examination and analysis of waste disposal management in the future.