Authors:
Lerina Aversano
1
;
Martina Iammarino
2
;
Antonella Madau
3
;
Debora Montano
4
;
1
and
Chiara Verdone
1
Affiliations:
1
Department of Agricultural Sciences, Food, Natural Resources and Engineering, University of Foggia, Foggia, Italy
;
2
Department of Information Science and Technology, Pegaso University, Naples, Italy
;
3
Department of Engineering, University of Sannio, Benevento, Italy
;
4
Department of Surgery, Medicine, Dentistry and Morphological Sciences with Interest in Transplantology, Oncology and Regenerative Medicine, University of Modena and Reggio Emilia, Italy
Keyword(s):
Open-Data, Machine Learning, Explainability, XAI, SHAP.
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 selecte
d 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.
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