AQE-Net: A Deep Learning Model for Estimating Air Quality of Karachi City from Mobile Images
文献类型：期刊论文
| 作者 | Ahmed, Maqsood1; Shen, Yonglin2; Ahmed, Mansoor4; Xiao, Zemin1; Cheng, Ping2; Ali, Nafees3,5; Ghaffar, Abdul3; Ali, Sabir6 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2022-11-01 |
| 卷号 | 14期号:22页码:- |
| 关键词 | air quality index deep learning Karachi classification |
| 英文摘要 | Air quality has a significant influence on the environment and health. Instruments that efficiently and inexpensively detect air quality could be extremely valuable in detecting air quality indices. This study presents a robust deep learning model named AQE-Net, for estimating air quality from mobile images. The algorithm extracts features and patterns from scene photographs collected by the camera device and then classifies the images according to air quality index (AQI) levels. Additionally, an air quality dataset (KARACHI-AQI) of high-quality outdoor images was constructed to enable the model's training and assessment of performance. The sample data were collected from an air quality monitoring station in Karachi City, Pakistan, comprising 1001 hourly datasets, including photographs, PM2.5 levels, and the AQI. This study compares and examines traditional machine learning algorithms, e.g., a support vector machine (SVM), and deep learning models, such as VGG16, InceptionV3, and AQE-Net on the KHI-AQI dataset. The experimental findings demonstrate that, compared to other models, AQE-Net achieved more accurate categorization findings for air quality. AQE-Net achieved 70.1% accuracy, while SVM, VGG16, and InceptionV3 achieved 56.2% and 59.2% accuracy, respectively. In addition, MSE, MAE, and MAPE values were calculated for our model (1.278, 0.542, 0.310), which indicates the remarkable efficacy of our approach. The suggested method shows promise as a fast and accurate way to estimate and classify pollutants from only captured photographs. This flexible and scalable method of assessment has the potential to fill in significant gaps in the air quality data gathered from costly devices around the world. |
| 学科主题 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| 出版者 | MDPI |
| WOS记录号 | WOS:000887772000001 |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/35241]  |
| 专题 | 中科院武汉岩土力学所 |
| 作者单位 | 1.School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China 2.National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan 430074, China 3.State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China 4.School of Economics and Management, China University of Geosciences, Wuhan 430074, China 5.University of Chinese Academy of Sciences, Beijing 100049, China 6.Department of Computer Systems Engineering, Quaid-E-Awam University of Engineering, Science & Technology, Nawabshah 67230, Pakistan |
| 推荐引用方式 GB/T 7714 | Ahmed, Maqsood,Shen, Yonglin,Ahmed, Mansoor,et al. AQE-Net: A Deep Learning Model for Estimating Air Quality of Karachi City from Mobile Images[J]. REMOTE SENSING,2022,14(22):-. |
| APA | Ahmed, Maqsood.,Shen, Yonglin.,Ahmed, Mansoor.,Xiao, Zemin.,Cheng, Ping.,...&Ali, Sabir.(2022).AQE-Net: A Deep Learning Model for Estimating Air Quality of Karachi City from Mobile Images.REMOTE SENSING,14(22),-. |
| MLA | Ahmed, Maqsood,et al."AQE-Net: A Deep Learning Model for Estimating Air Quality of Karachi City from Mobile Images".REMOTE SENSING 14.22(2022):-. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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