Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Air quality index prediction based on three-stage feature engineering, model matching, and optimized ensemble

Air quality index prediction based on three-stage feature engineering, model matching, and... A prompt and accurate prediction of air quality index (AQI) has become a necessity to tackle the mounting environmental threats. This paper proposes a feature-driven hybrid method for hourly, 3-step-ahead, and deterministic AQI prediction, which includes three modules. In Module 1, an “extract-merge-filter” procedure of feature engineering is created to capture the potential features from the AQI series. Ten feature sets are generated as candidates. In Module 2, six models including Light Gradient Boosting Machine, Extreme Gradient Boosting, Long Short-Term Memory, Convolutional Neural Network, Multilayer Perceptron, and Deep Neural Network are developed as base predictors and performed on the candidate features. In Module 3, predictors are first matched with their optimal features using a comprehensive metric, and then combined in an optimized ensemble using OPTUNA. A case study on the AQI data from four different Chinese cities is carried out to demonstrate the method. The experimental results show the following: (1) Feature engineering significantly boosts prediction performance and provides interpretable findings for practical use. (2) Customized input of features to the predictors is more effective than a fixed input and can rise the performance to a higher level. (3) OPTUNA is a promising tool for optimizing ensemble weights. The final ensemble model is superior to single machine learning models and has a good robustness. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Air Quality Atmosphere & Health Springer Journals

Air quality index prediction based on three-stage feature engineering, model matching, and optimized ensemble

Air Quality Atmosphere & Health , Volume 16 (9) – Sep 1, 2023

Loading next page...
 
/lp/springer-journals/air-quality-index-prediction-based-on-three-stage-feature-engineering-qQDOJfHV8T

References (65)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
1873-9318
eISSN
1873-9326
DOI
10.1007/s11869-023-01380-7
Publisher site
See Article on Publisher Site

Abstract

A prompt and accurate prediction of air quality index (AQI) has become a necessity to tackle the mounting environmental threats. This paper proposes a feature-driven hybrid method for hourly, 3-step-ahead, and deterministic AQI prediction, which includes three modules. In Module 1, an “extract-merge-filter” procedure of feature engineering is created to capture the potential features from the AQI series. Ten feature sets are generated as candidates. In Module 2, six models including Light Gradient Boosting Machine, Extreme Gradient Boosting, Long Short-Term Memory, Convolutional Neural Network, Multilayer Perceptron, and Deep Neural Network are developed as base predictors and performed on the candidate features. In Module 3, predictors are first matched with their optimal features using a comprehensive metric, and then combined in an optimized ensemble using OPTUNA. A case study on the AQI data from four different Chinese cities is carried out to demonstrate the method. The experimental results show the following: (1) Feature engineering significantly boosts prediction performance and provides interpretable findings for practical use. (2) Customized input of features to the predictors is more effective than a fixed input and can rise the performance to a higher level. (3) OPTUNA is a promising tool for optimizing ensemble weights. The final ensemble model is superior to single machine learning models and has a good robustness.

Journal

Air Quality Atmosphere & HealthSpringer Journals

Published: Sep 1, 2023

Keywords: AQI prediction; Feature engineering; Model matching; Optimized ensemble; OPTUNA

There are no references for this article.