The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Forecasting Fine-Grained Air Quality Based on Big Data
Abstract
In this paper, we forecast the reading of an air quality monitoring station over the next 48 hours, using a data-driven method that considers current meteorological data, weather forecasts, and air quality data of the station and that of other stations within a few hundred kilometers. Our predictive model is comprised of four major components: 1) a linear regression-based temporal predictor to model the local factors of air quality, 2) a neural network-based spatial predictor to model global factors, 3) a dynamic aggregator combining the predictions of the spatial and temporal predictors according to meteorological data, and 4) an inflection predictor to capture sudden changes in air quality. We evaluate our model with data from 43 cities in China, surpassing the results of multiple baseline methods. We have deployed a system with the Chinese Ministry of Environmental Protection, providing 48-hour fine-grained air quality forecasts for four major Chinese cities every hour. The forecast function is also enabled on Microsoft Bing Map and MS cloud platform Azure. Our technology is general and can be applied globally for other cities.
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Cites
Urban Computing: Concepts, Methodologies, and Applications
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Self-citationModelling air quality in street canyons: a review
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Self-citationReal-time air quality forecasting, part I: History, techniques, and current status
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Inferring gas consumption and pollution emission of vehicles throughout a city
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Self-citationZhang, Y., Bocquet, M., Mallet, V., Seigneur, C., & Baklanov, A. (2012) · Atmospheric Environment · doi:10.1016/j.atmosenv.2012.02.041 · Cited by 216
Inferring Air Quality for Station Location Recommendation Based on Urban Big Data
Hsieh, H.-P., Lin, S.-D., & Zheng, Y. (2015) · Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2783258.2783344 · Cited by 178
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A Cloud-Based Knowledge Discovery System for Monitoring Fine-Grained Air Quality
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Methodologies for Cross-Domain Data Fusion: An Overview
Zheng, Y. (2015) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2015.2465959 · Cited by 454
Self-citationA novel spatiotemporal convolutional long short-term neural network for air pollution prediction
Wen, C., Liu, S., Yao, X., Peng, L., Li, X., Hu, Y., & Chi, T. (2019) · Science of The Total Environment · doi:10.1016/j.scitotenv.2018.11.086 · Cited by 425
Urban big data fusion based on deep learning: An overview
Liu, J., Li, T., Xie, P., Du, S., Teng, F., & Yang, X. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.06.016 · Cited by 266
Self-citationA survey on data fusion in internet of things: Towards secure and privacy-preserving fusion
Ding, W., Jing, X., Yan, Z., & Yang, L. T. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.12.001 · Cited by 230
Metadata
- Authors
- Zheng, Y., Yi, X., Li, M., Li, R., Shan, Z., Chang, E., & Li, T.
- Venue
- Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
- Publisher
- ACM
- Published
- 2015-08-10
- Pages
- 2267-2276
- OpenAlex
- W1969865391
- Cited by
- 472
- References
- 9
- FWCI
- 103.65
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Zheng, Y., Yi, X., Li, M., Li, R., Shan, Z., Chang, E., & Li, T. (2015). Forecasting Fine-Grained Air Quality Based on Big Data. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. https://doi.org/10.1145/2783258.2788573
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
