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A Machine Learning Approach for Air Quality Prediction: Model Regularization and Optimization

1
Department of Computer Science, University of Iowa, Iowa City, IA 52242, USA
2
Department of Occupational and Environmental Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
3
Department of Management Sciences, University of Iowa, Iowa City, IA 52242, USA
*
Author to whom correspondence should be addressed.
Big Data Cogn. Comput. 2018, 2(1), 5; https://doi.org/10.3390/bdcc2010005
Received: 28 December 2017 / Revised: 16 February 2018 / Accepted: 19 February 2018 / Published: 24 February 2018
(This article belongs to the Special Issue Learning with Big Data: Scalable Algorithms and Novel Applications)
In this paper, we tackle air quality forecasting by using machine learning approaches to predict the hourly concentration of air pollutants (e.g., ozone, particle matter ( PM 2.5 ) and sulfur dioxide). Machine learning, as one of the most popular techniques, is able to efficiently train a model on big data by using large-scale optimization algorithms. Although there exist some works applying machine learning to air quality prediction, most of the prior studies are restricted to several-year data and simply train standard regression models (linear or nonlinear) to predict the hourly air pollution concentration. In this work, we propose refined models to predict the hourly air pollution concentration on the basis of meteorological data of previous days by formulating the prediction over 24 h as a multi-task learning (MTL) problem. This enables us to select a good model with different regularization techniques. We propose a useful regularization by enforcing the prediction models of consecutive hours to be close to each other and compare it with several typical regularizations for MTL, including standard Frobenius norm regularization, nuclear norm regularization, and 2 , 1 -norm regularization. Our experiments have showed that the proposed parameter-reducing formulations and consecutive-hour-related regularizations achieve better performance than existing standard regression models and existing regularizations. View Full-Text
Keywords: air pollutant prediction; multi-task learning; regularization; analytical solution air pollutant prediction; multi-task learning; regularization; analytical solution
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MDPI and ACS Style

Zhu, D.; Cai, C.; Yang, T.; Zhou, X. A Machine Learning Approach for Air Quality Prediction: Model Regularization and Optimization. Big Data Cogn. Comput. 2018, 2, 5. https://doi.org/10.3390/bdcc2010005

AMA Style

Zhu D, Cai C, Yang T, Zhou X. A Machine Learning Approach for Air Quality Prediction: Model Regularization and Optimization. Big Data and Cognitive Computing. 2018; 2(1):5. https://doi.org/10.3390/bdcc2010005

Chicago/Turabian Style

Zhu, Dixian; Cai, Changjie; Yang, Tianbao; Zhou, Xun. 2018. "A Machine Learning Approach for Air Quality Prediction: Model Regularization and Optimization" Big Data Cogn. Comput. 2, no. 1: 5. https://doi.org/10.3390/bdcc2010005

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