Szczegóły publikacji
Opis bibliograficzny
Explainable AI for rule reduction in fuzzy models for air pollution measurement adjustment / Piotr A. KOWALSKI, Martina Casari, Laura Po // Environmental Modelling & Software ; ISSN 1364-8152 . — 2026 — vol. 195 art. no. 106734, s. 1–13. — Bibliogr. s. 13, Abstr. — Publikacja dostępna online od: 2025-10-11. — P. A. Kowalski - dod. afiliacja: Systems Research Institute, Polish Academy of Sciences, Warszawa, Poland
Autorzy (3)
- AGHKowalski Piotr Andrzej
- Casari Martina
- Po Laura
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 163987 |
|---|---|
| Data dodania do BaDAP | 2025-11-18 |
| Tekst źródłowy | URL |
| DOI | 10.1016/j.envsoft.2025.106734 |
| Rok publikacji | 2026 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Environmental Modelling & Software |
Abstract
Air quality monitoring using low-cost sensors has become increasingly important, yet their measurements are often inaccurate. Traditional adjustment methods face limitations in both applicability and explainability. This paper presents an explainable artificial intelligence approach for rule reduction in adaptive neuro-fuzzy inference systems, to improve the interpretability and efficiency of fuzzy models for fine particulate matter (PM2.5) measurement adjustment. We introduce two novel algorithms, the Binary Activation Method and the Weighted Activation Method, to assess and eliminate redundant rules while maintaining predictive performance, validating the approaches in multiple geographic locations. On average, rule pruning results in an increase in MAE of 0.2 on the training set and 0.1 on the test set. The simplified models retain strong correlation, with Pearson's correlation coefficients ranging from 0.73 to 0.96 in the test set. These results support the development of reliable and interpretable artificial intelligence systems for environmental monitoring.
