Forecasting Fine-Grained Air Quality for Locations without Monitoring Stations Based on a Hybrid Predictor with Spatial-Temporal Attention Based Network

doi:10.3390/app12094268

Chia Nan University of Pharmacy & Science Institutional Repository > 藥理學院 > 保健營養系(所) > 期刊論文 > Item 310902800/34535

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標題:	Forecasting Fine-Grained Air Quality for Locations without Monitoring Stations Based on a Hybrid Predictor with Spatial-Temporal Attention Based Network
作者:	Hsieh, Hsun-Ping Wu, Su Ko, Ching-Chung Shei, Chris Yao, Zheng-Ting Chen, Yu-Wen
貢獻者:	National Cheng Kung University Chi Mei Hospital Department of Health and Nutrition, Chia Nan University of Pharmacy & Science National Sun Yat Sen University Swansea University Academia Sinica - Taiwan
關鍵字:	interpolation impact pm2.5
日期:	2022
上傳時間:	2023-12-11 13:56:34 (UTC+8)
出版者:	MDPI
摘要:	Air pollution in cities is a severe and worrying problem because it causes threats to economic development and health. Furthermore, with the development of industry and technology, rapid population growth, and the massive expansion of cities, the total amount of pollution emissions continue to increase. Hence, observing and predicting the air quality index (AQI), which measures fatal pollutants to humans, has become more and more critical in recent years. However, there are insufficient air quality monitoring stations for AQI observation because the construction and maintenance costs are too high. In addition, finding an available and suitable place for monitoring stations in cities with high population density is difficult. This study proposes a spatial-temporal model to predict the long-term AQI in a city without monitoring stations. Our model calculates the spatial-temporal correlation between station and region using an attention mechanism and leverages the distance information between all existing monitoring stations and target regions to enhance the effectiveness of the attention structure. Furthermore, we design a hybrid predictor that can effectively combine the time-dependent and time-independent predictors using the dynamic weighted sum. Finally, the experimental results show that the proposed model outperforms all the baseline models. In addition, the ablation study confirms the effectiveness of the proposed structures.
關聯:	Applied Sciences-Basel, v.12, n.9, Article 4268
显示于类别:	[保健營養系(所) ] 期刊論文

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