Interpretable Machine Learning Approaches for Forecasting and Predicting Air Pollution: A Systematic Review

Interpretable Machine Learning Approaches for Forecasting and Predicting Air Pollution: A Systematic Review

Abstract Many studies use machine learning to predict atmospheric pollutant levels, prioritizing accuracy over interpretability. This systematic review will focus on reviewing studies that have utilized interpretable machine learning models to enhance interpretability while maintaining high accuracy...

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Bibliographic Details
Main Authors: Anass Houdou, Imad El Badisy, Kenza Khomsi, Sammila Andrade Abdala, Fayez Abdulla, Houda Najmi, Majdouline Obtel, Lahcen Belyamani, Azeddine Ibrahimi, Mohamed Khalis
Format: Article
Language:English
Published: Springer 2023-11-01
Series:Aerosol and Air Quality Research
Subjects:
Air quality prediction
Deep learning
Supervised learning
Online Access:https://doi.org/10.4209/aaqr.230151
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https://doi.org/10.4209/aaqr.230151

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