Exploring the Influencing Factors of Surface Ozone Variability by Explainable Machine Learning: A Case Study in the Basilicata Region (Southern Italy)
Exposure to high surface ozone (O<sub>3</sub>) concentrations, which is a major air pollutant and greenhouse gas, constitutes a significant public health concern, especially considering the potential adverse impact of climate change on future O<sub>3</sub> values. The impleme...
Saved in:
| Main Authors: | Roberta Valentina Gagliardi, Claudio Andenna |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Atmosphere |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4433/16/5/491 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Explainable artificial intelligence (XAI) for interpreting predictive models and key variables in flood susceptibility
by: Bahram Choubin, et al.
Published: (2025-09-01) -
Multilayer Concept Drift Detection Method Based on Model Explainability
by: Haolan Zhang, et al.
Published: (2024-01-01) -
Explainable machine learning-driven models for predicting Parkinson’s disease and its prognosis: obesity patterns associations and models development using NHANES 1999–2018 data
by: Jiaxin Fan, et al.
Published: (2025-07-01) -
Understanding the role of urban block morphology in innovation vitality through explainable machine learning
by: Yichen Ruan, et al.
Published: (2025-07-01) -
Explainable machine learning models for estimating daily dissolved oxygen concentration of the Tualatin River
by: Shuguang Li, et al.
Published: (2024-12-01)