Exploring Machine Learning and Deep Learning Approaches for Multi-Step Forecasting in Municipal Solid Waste Generation

Abstract

Municipal Solid Waste (MSW) management enact a significant role in protecting public health and the environment. The main objective of this paper is to explore the utility of using state-of-the-art machine learning and deep learning-based models for predicting future variations in MSW generation for a given geographical region, considering its past waste generation pattern. We consider nine different machine learning and deep-learning models to examine and evaluate their capability in forecasting the daily generated waste amount. In order to have a comprehensive evaluation, we explore the utility of two training and prediction paradigms, a single-model approach and a multi-model ensemble approach. Three Sri Lankan datasets from; Boralesgamuwa, Dehiwala, and Moratuwa, and open-source daily waste datasets from the city of Austin and Ballarat, are considered in this study. Our results show that Austin and Ballarat datasets got lower error percentage values of 8.03% and 8.3% for Linear Regression and Random Forest models respectively. In Sri Lankan datasets, Random Forest model outperformed other potential models in terms of MAPE by 28.02% to 36.89%. In addition, we provide an in-depth discussion on important considerations to make when choosing a model for predicting MSW generation to enhance the study.

Keywords

• Waste management
• time series
• machine learning
• deep learning