Real-time image-based air quality estimation by deep learning neural networks
學年 110
學期 1
出版（發表）日期 2022-01-24
作品名稱 Real-time image-based air quality estimation by deep learning neural networks
作品名稱（其他語言）
著者 Pu-Yun Kow; I-Wen Hsia; Li-Chiu Chang; Fi-John Chang
單位
出版者
著錄名稱、卷期、頁數 Journal of Environmental Management 307, 114560
摘要 Air quality profoundly impacts public health and environmental equity. Efficient and inexpensive air quality monitoring instruments could be greatly beneficial for human health and air pollution control. This study proposes an image-based deep learning model (CNN−RC) that integrates a convolutional neural network (CNN) and a regression classifier (RC) to estimate air quality at areas of interest through feature extraction from photos and feature classification into air quality levels. The models were trained and tested on datasets with different combinations of the current image, the baseline image, and HSV (hue, saturation, value) statistics for increasing model reliability and estimation accuracy. A total of 3549 hourly air quality datasets (including photos, PM2.5, PM10, and the air quality index (AQI)) collected at the Linyuan air quality monitoring station of Kaohsiung City in Taiwan constituted the case study. The main breakthrough of this study is to timely produce an accurate image-based estimation of several pollutants simultaneously by using only one single deep learning model. The test results show that estimation accuracy in terms of R2 for PM2.5, PM10, and AQI based on daytime (nighttime) images reaches 76% (83%), 84% (84%), and 76% (74%), respectively, which demonstrates the great capability of our method. The proposed model offers a promising solution for rapid and reliable multi-pollutant estimation and classification based solely on captured images. This readily scalable measurement approach could address major gaps between air quality data acquired from expensive instruments worldwide.
關鍵字
語言 en
ISSN
期刊性質 國外
收錄於 SCI
產學合作
通訊作者
審稿制度 否
國別 NLD
公開徵稿
出版型式 ,電子版
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/124321 )

SDGS 永續城市與社區,氣候行動