Reviewing Explainable Artificial Intelligence Towards Better Air Quality Modelling

Thomas Tasioulis () and Kostas Karatzas ()
Additional contact information
Thomas Tasioulis: Aristotle University of Thessaloniki
Kostas Karatzas: Aristotle University of Thessaloniki

A chapter in Advances and New Trends in Environmental Informatics 2023, 2024, pp 3-19 from Springer

Abstract: Abstract The increasing complexity of machine learning models used in environmental studies necessitates robust tools for transparency and interpretability. This paper systematically explores the transformative potential of Explainable Artificial Intelligence (XAI) techniques within the field of air quality research. A range of XAI methodologies, including Permutation Feature Importance (PFI), Partial Dependence Plot (PDP), SHapley Additive exPlanations (SHAP), and Local Interpretable Model-Agnostic Explanations (LIME), have been effectively investigated to achieve robust, comprehensible outcomes in modeling air pollutant concentrations worldwide. The integration of advanced feature engineering, visual analytics, and methodologies like DeepLIFT and Layer-Wise Relevance Propagation further enhance the interpretability and reliability of deep learning models. Despite these advancements, a significant proportion of air quality research still overlooks the implementation of XAI techniques, resulting in biases and redundancies within datasets. This review highlights the pivotal role of XAI techniques in facing these challenges, thus promoting precision, transparency, and trust in complex models. Furthermore, it underscores the necessity for a continued commitment to the integration and development of XAI techniques, pushing the boundaries of our understanding and usability of Artificial Intelligence in environmental science. The comprehensive insights offered by XAI can significantly aid in decision-making processes and lead to transformative strides within the fields of Internet of Things and air quality research.

Keywords: XAI; Explainable modelling; Air quality (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:prochp:978-3-031-46902-2_1

Ordering information: This item can be ordered from
http://www.springer.com/9783031469022

DOI: 10.1007/978-3-031-46902-2_1

Access Statistics for this chapter

More chapters in Progress in IS from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().