PM2.5 Forecasting Model Using a Combination of Deep Learning and Statistical Feature Selection

PM2.5 Forecasting Model Using a Combination of Deep Learning and Statistical Feature Selection

This paper proposed a PM 2.5 forecasting model using Long Short-Term Model (LSTM) sequence to sequence combined with the statistical method. Correlation Analysis, XGBoost, and Chemical processed are used as the methods to select the essential features. The air pollution data is extracted from Taiwan...

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Bibliographic Details
Main Authors: Endah Kristiani, Ting-Yu Kuo, Chao-Tung Yang, Kai-Chih Pai, Chin-Yin Huang, Kieu Lan Phuong Nguyen
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Air pollution monitoring
LSTM seq2seq
PM2.5
XGBoost
feature selection
correlation analysis
Online Access:https://ieeexplore.ieee.org/document/9422751/

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