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A Machine Learning-Based Ensemble Framework for Forecasting PM 2.5 Concentrations in Puli, Taiwan.
- Source :
- Applied Sciences (2076-3417); Mar2022, Vol. 12 Issue 5, p2484, 21p
- Publication Year :
- 2022
-
Abstract
- Forecasting of PM<subscript>2.5</subscript> concentration is a global concern. Evidence has shown that the ambient PM<subscript>2.5</subscript> concentrations are harmful to human health, climate change, plant species mortality, etc. PM<subscript>2.5</subscript> concentrations are caused by natural and anthropogenic activities, and it is challenging to predict them due to many uncertain factors. Current research has focused on developing a new model while overlooking the fact that every single model for PM<subscript>2.5</subscript> prediction has its own strengths and weaknesses. This paper proposes an ensemble framework which combines four diverse learning models for PM<subscript>2.5</subscript> forecasting in Puli, Taiwan. It explores the synergy between parametric and non-parametric learning, and short-term and long-term learning. The feature set covers periodic, meteorological, and autoregression variables which are selected by a spiral validation process. The experimental dataset, spanning from 1 January 2008 to 31 December 2019, from Puli Township in central Taiwan, is used in this study. The experimental results show the proposed multi-model framework can synergize the advantages of the embedded models and obtain an improved forecasting result. Further, the benefit obtained by blending short-term learning with long-term learning is validated, in surpassing the performance obtained by using just single type of learning. Our multi-model framework compares favorably with deep-learning models on Puli dataset. It also shows high adaptivity, such that our multi-model framework is comparable to the leading methods for PM<subscript>2.5</subscript> forecasting in Delhi, India. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20763417
- Volume :
- 12
- Issue :
- 5
- Database :
- Complementary Index
- Journal :
- Applied Sciences (2076-3417)
- Publication Type :
- Academic Journal
- Accession number :
- 155705794
- Full Text :
- https://doi.org/10.3390/app12052484