Air Quality Index Forecasting via Deep Dictionary Learning

Bin CHEN  

IEICE TRANSACTIONS on Information and Systems   Vol.E103-D   No.5   pp.1118-1125
Publication Date: 2020/05/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2019EDP7296
Type of Manuscript: PAPER
Category: Image Recognition, Computer Vision
air quality index forecasting,  deep dictionary learning,  greedy learning,  representation learning paradigm,  

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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.