Application of Machine Learning for Prediction of Solid Waste Generation

Abstract

Effective solid waste management requires adaptive planning and accurate forecasting of waste generation and composition. Accurate predictions require a deep understanding of key influencing factors, including population, urbanization, income, and temporal effects, which impact waste trends. Traditional models, including regression and fixed-effects analysis, struggle to capture these complex, non-linear relationships. Recently, machine learning (ML) techniques have emerged as powerful tools, leveraging large datasets and advanced computational methods to improve predictive accuracy. Nonetheless, challenges like data limitations, regional variability, and scalability hinder their practical implementation. This review explores and summarizes the significance of the essential factors for predicting solid waste generation and emphasizes new opportunities for real-time waste monitoring by integrating ML with emerging technologies like the Internet of Things (IoT). Enhanced sustainable waste management needs advances in model generalisability, data quality, and policy integration.