An Explainable Model for Waste Cost Prediction: A Study on Linked Open Data in Italy

Lerina Aversano, Martina Iammarino, Antonella Madau, Debora Montano, Debora Montano, Chiara Verdone

2025

Abstract

Artificial intelligence and machine learning models are emerging as essential tools for optimizing municipal solid waste management and supporting policy decisions. However, transparency and interpretability of these models’ predictions continue to be major obstacles. Recent advances in Explainable Artificial Intelligence (XAI) techniques have made it possible to explain specific model decisions and guarantee that the outcomes are intelligible and useful. Using high-quality Italian open data in the form of Linked Open Data (LOD), this study investigates the benefits and viability of creating explainable models in italian municipalities. To achieve this, a method for using connected and open statistical data to create explainable models is provided. Additionally, a case study is presented, covering four years, in which waste management expenses are predicted and interpreted using connected data about Italian municipalities, categorizing them into three cost bands. CatBoost was selected as the predictive model’s algorithm, and the SHAP framework was used to guarantee the predictions’ transparency. Through transparent and accountable data management, this effort seeks to illustrate how cutting-edge technologies can enhance the sustainability of public programs.

Paper Citation

in Harvard Style

Aversano L., Iammarino M., Madau A., Montano D. and Verdone C. (2025). An Explainable Model for Waste Cost Prediction: A Study on Linked Open Data in Italy. In Proceedings of the 20th International Conference on Software Technologies - Volume 1: ICSOFT; ISBN 978-989-758-757-3, SciTePress, pages 446-453. DOI: 10.5220/0013650800003964

in Bibtex Style

@conference{icsoft25,
author={Lerina Aversano and Martina Iammarino and Antonella Madau and Debora Montano and Chiara Verdone},
title={An Explainable Model for Waste Cost Prediction: A Study on Linked Open Data in Italy},
booktitle={Proceedings of the 20th International Conference on Software Technologies - Volume 1: ICSOFT},
year={2025},
pages={446-453},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013650800003964},
isbn={978-989-758-757-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Conference on Software Technologies - Volume 1: ICSOFT
TI - An Explainable Model for Waste Cost Prediction: A Study on Linked Open Data in Italy
SN - 978-989-758-757-3
AU - Aversano L.
AU - Iammarino M.
AU - Madau A.
AU - Montano D.
AU - Verdone C.
PY - 2025
SP - 446
EP - 453
DO - 10.5220/0013650800003964
PB - SciTePress