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

• Published in: IEEE access Vol. 10; p. 1
• Main Authors: Mudannayake, Oshan, Rathnayake, Disni, Herath, Jerome Dinal, Fernando, Dinuni K, Fernando, Mgnas
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Analytical models; Datasets; Deep learning; Evaluation; Forecasting; Machine learning; Mathematical models; Municipal solid waste; Municipal waste management; Predictive models; Public health; Solid modeling; Solid waste management; Solid wastes; time series; Time series analysis; Waste management; Waste materials
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3221941

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. In addition, we provide an in depth discussion on important considerations to make when choosing a model for predicting MSW generation.