Comparative Evaluation of Machine Learning Models For Municipal Solid Waste Prediction With Feature Extension

Mwila Milandile; Muwanei Sinyinda

doi:10.25299/itjrd.2025.22391

Authors

Mwila Milandile Departement of Computer Science and Information Technology, Mulungushi University
Muwanei Sinyinda Departement of Computer Science and Information Technology, Mulungushi University

DOI:

https://doi.org/10.25299/itjrd.2025.22391

Keywords:

Ensemble Learning, prediction, Machine Learning, Feature Selection

Abstract

This paper explores the utilization of machine learning approaches in predicting municipal solid waste generation accurately based on two distinct prediction methods, a single-model approach and a multi-model ensemble approach while incorporating feature engineering. Furthermore, we compare the predictive performance of two approaches: the single-model method and the multi-model ensemble approach. The metrics Mean Absolute Percentage Error (MAPE) the Root Mean Square Error (RMSE), and the Mean Absolute Error (MAE) have been used to assess the performance of the models. The finfings indicate that multi-ensemble approach outperformed the single model method by obtaining lower MAPE, RMSE, and MAE. The ensemble model obtained a Mean Absolute Percentage Error (MAPE) of 37.38 %, a Root Mean Square Error (RMSE) of 7610.76, and a Mean Absolute Error (MAE) of 5760.89, while the single-model technique achieved a Mean Absolute Percentage Error (MAPE) of 42.58 %, a Root Mean Square Error (RMSE) of 8258.01 and MAE of 6470.14. These findings indicate that merging multiple models can result in a more resilient and accurate predicting system. The findings presented in this paper suggest that by integrating feature engineering and utilizing multiple models results into more accurate predictions leading to effective waste management practices.

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