TY - JOUR
T1 - Forecasting Fine-Grained Air Quality for Locations without Monitoring Stations Based on a Hybrid Predictor with Spatial-Temporal Attention Based Network
AU - Hsieh, Hsun Ping
AU - Wu, Su
AU - Ko, Ching Chung
AU - Shei, Chris
AU - Yao, Zheng Ting
AU - Chen, Yu Wen
N1 - Funding Information:
Funding: This research was funded by the Ministry of Science and Technology (MOST) of Taiwan under grants MOST 109-2636-E-006-025 and MOST 110-2636-E-006-011.
Funding Information:
Acknowledgments: This work was partially supported by Ministry of Science and Technology (MOST) of Taiwan under grants MOST 109-2636-E-006-025 and MOST 110-2636-E-006-011(MOST Young Scholar Fellowship).
Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2022/5/1
Y1 - 2022/5/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85129059450&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85129059450&partnerID=8YFLogxK
U2 - 10.3390/app12094268
DO - 10.3390/app12094268
M3 - Article
AN - SCOPUS:85129059450
VL - 12
JO - Applied Sciences (Switzerland)
JF - Applied Sciences (Switzerland)
SN - 2076-3417
IS - 9
M1 - 4268
ER -