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

CNU IR > Pharmacy and Science > Dept. of Health and Nutrition (including master's program) > Periodical Articles > Item 310902800/34535

Please use this identifier to cite or link to this item: https://ir.cnu.edu.tw/handle/310902800/34535

Title:	Forecasting Fine-Grained Air Quality for Locations without Monitoring Stations Based on a Hybrid Predictor with Spatial-Temporal Attention Based Network
Authors:	Hsieh, Hsun-Ping Wu, Su Ko, Ching-Chung Shei, Chris Yao, Zheng-Ting Chen, Yu-Wen
Contributors:	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
Keywords:	interpolation impact pm2.5
Date:	2022
Issue Date:	2023-12-11 13:56:34 (UTC+8)
Publisher:	MDPI
Abstract:	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.
Relation:	Applied Sciences-Basel, v.12, n.9, Article 4268
Appears in Collections:	[Dept. of Health and Nutrition (including master's program)] Periodical Articles

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