Air Quality Index Forecasting via Deep Dictionary Learning

Bin CHEN

doi:10.1587/transinf.2019EDP7296

Air Quality Index Forecasting via Deep Dictionary Learning

Bin CHEN

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Summary :

Air quality index (AQI) is a non-dimensional index for the description of air quality, and is widely used in air quality management schemes. A novel method for Air Quality Index Forecasting based on Deep Dictionary Learning (AQIF-DDL) and machine vision is proposed in this paper. A sky image is used as the input of the method, and the output is the forecasted AQI value. The deep dictionary learning is employed to automatically extract the sky image features and achieve the AQI forecasting. The idea of learning deeper dictionary levels stemmed from the deep learning is also included to increase the forecasting accuracy and stability. The proposed AQIF-DDL is compared with other deep learning based methods, such as deep belief network, stacked autoencoder and convolutional neural network. The experimental results indicate that the proposed method leads to good performance on AQI forecasting.

Publication: IEICE TRANSACTIONS on Information Vol.E103-D No.5 pp.1118-1125

Publication Date: 2020/05/01

Publicized: 2020/02/20

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2019EDP7296

Type of Manuscript: PAPER

Category: Image Recognition, Computer Vision

Authors

Bin CHEN
Jiaxing University

Keyword

air quality index forecasting, deep dictionary learning, greedy learning, representation learning paradigm

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Bin CHEN, "Air Quality Index Forecasting via Deep Dictionary Learning" in IEICE TRANSACTIONS on Information, vol. E103-D, no. 5, pp. 1118-1125, May 2020, doi: 10.1587/transinf.2019EDP7296.
Abstract: Air quality index (AQI) is a non-dimensional index for the description of air quality, and is widely used in air quality management schemes. A novel method for Air Quality Index Forecasting based on Deep Dictionary Learning (AQIF-DDL) and machine vision is proposed in this paper. A sky image is used as the input of the method, and the output is the forecasted AQI value. The deep dictionary learning is employed to automatically extract the sky image features and achieve the AQI forecasting. The idea of learning deeper dictionary levels stemmed from the deep learning is also included to increase the forecasting accuracy and stability. The proposed AQIF-DDL is compared with other deep learning based methods, such as deep belief network, stacked autoencoder and convolutional neural network. The experimental results indicate that the proposed method leads to good performance on AQI forecasting.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDP7296/_p

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@ARTICLE{e103-d_5_1118,
author={Bin CHEN, },
journal={IEICE TRANSACTIONS on Information},
title={Air Quality Index Forecasting via Deep Dictionary Learning},
year={2020},
volume={E103-D},
number={5},
pages={1118-1125},
abstract={Air quality index (AQI) is a non-dimensional index for the description of air quality, and is widely used in air quality management schemes. A novel method for Air Quality Index Forecasting based on Deep Dictionary Learning (AQIF-DDL) and machine vision is proposed in this paper. A sky image is used as the input of the method, and the output is the forecasted AQI value. The deep dictionary learning is employed to automatically extract the sky image features and achieve the AQI forecasting. The idea of learning deeper dictionary levels stemmed from the deep learning is also included to increase the forecasting accuracy and stability. The proposed AQIF-DDL is compared with other deep learning based methods, such as deep belief network, stacked autoencoder and convolutional neural network. The experimental results indicate that the proposed method leads to good performance on AQI forecasting.},
keywords={},
doi={10.1587/transinf.2019EDP7296},
ISSN={1745-1361},
month={May},}

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TY - JOUR
TI - Air Quality Index Forecasting via Deep Dictionary Learning
T2 - IEICE TRANSACTIONS on Information
SP - 1118
EP - 1125
AU - Bin CHEN
PY - 2020
DO - 10.1587/transinf.2019EDP7296
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2020
AB - Air quality index (AQI) is a non-dimensional index for the description of air quality, and is widely used in air quality management schemes. A novel method for Air Quality Index Forecasting based on Deep Dictionary Learning (AQIF-DDL) and machine vision is proposed in this paper. A sky image is used as the input of the method, and the output is the forecasted AQI value. The deep dictionary learning is employed to automatically extract the sky image features and achieve the AQI forecasting. The idea of learning deeper dictionary levels stemmed from the deep learning is also included to increase the forecasting accuracy and stability. The proposed AQIF-DDL is compared with other deep learning based methods, such as deep belief network, stacked autoencoder and convolutional neural network. The experimental results indicate that the proposed method leads to good performance on AQI forecasting.
ER -