Prediction of municipal waste generation using multi-expression programming for circular economy: a data-driven approach.

Abstract

The existing surge in municipal waste generation (MWG), characterized by swiftly changing and uncontrollable factors, poses a significant challenge to sustainable development. This prompted the need for improved predictive models to guide strategic waste management within the circular economy framework. This study aims to develop a predictive model using multi-expression programming (MEP) to assess MWG. The model was developed using historical data on socioeconomic and environmental factors and validated via comparative analyses with artificial neural network (ANN), random forest (RF), and multiple linear regression (MLR) using various evaluation metrics. The parametric and sensitivity analyses of the MEP model were also conducted. The MEP, ANN, RF, and MLR models have a coefficient of determination (R2) (for testing datasets) of 0.977, 0.974, 0.957, and 0.964, respectively. The MEP model is superior in terms of accuracy and performance for the prediction of MWG when compared to the other three models. The sensitivity analysis revealed the relative importance of each input variable in the established MEP model. The novelty of this research lies in the application of MEP to predict MWG and the formulation of a new mathematical model that links socioeconomic and environmental factors with MWG. The model can be used by waste management authorities to optimize waste collection, transportation, and disposal infrastructure for an effective circular economy and sustainable development. This model also aids in the development of effective waste management policies.