Shapley additive explanations for NO2 forecasting
Abstract
In this paper, we address the problem of the interpretability of a machine learning model designed to predict air quality time series. When constructing a forecasting model, in addition to obtaining good accuracy, it is utterly important to understand why each prediction is made. Usually, interpreting the output of machine learning models is considered to be very difficult due to their complex “black box” architecture. However, we show how Shapley additive explanations can be used to interpret the outputs of a deep neural network designed to predict Nitrogen dioxide concentrations in Madrid. This method computes an estimation of the contribution of each feature for a particular prediction. Furthermore, we compare three explanatory methods to determine which one is more suitable for the air quality data and for the chosen machine learning model. A deeper insight into how the model behaves when predicting the pollution time series is obtained.
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Tree SHAP provides interpretable explanations suitable for regression and classification of models with tree structure applied to tabular data.
“The algorithm provides interpretable explanations suitable for regression and classification of models with tree structure applied to tabular data [22,39].”
From Explainable AI (XAI): Core Ideas, Techniques, and Solutions (2023) · cites this paper as [22]
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Explainable AI (XAI): Core Ideas, Techniques, and Solutions
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- Authors
- Vega García, M., & Aznarte, J. L.
- Affiliations
- Universidad Nacional de Educación a Distancia (ES)
- Corresponding author
- Aznarte, José L.
- ORCID
- Aznarte, José L.
- Venue
- Ecological Informatics
- Publisher
- Elsevier BV
- Published
- 2020-03
- Volume
- 56
- Pages
- 101039
- ISSN
- 1574-9541 (ISSN-L)
- OpenAlex
- W2996713777
- Cited by
- 346
- References
- 31
- Citations by year
- 2026: 49 · 2025: 81 · 2024: 73 · 2023: 55 · 2022: 39 · 2021: 32 · 2020: 17
- FWCI
- 10.37
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Ecological Informatics (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Environmental Science › Environmental Engineering › Air Quality Monitoring and Forecasting
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Air Quality and Health Impacts
- Keywords
- SHAP, explainable AI, air quality forecasting, nitrogen dioxide, deep neural network, feature attribution, model interpretability, black-box models, Madrid
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