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

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

Blog Article

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 MACASURE 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 MARINE COLLAGEN PWD enhance the study.