Xiuwen Yi
Junbo Zhang
Yu Zheng
ABSTRACT
Accompanying the rapid urbanization, many developing countries are suffering from serious air pollution problem. The demand for predicting future air quality is becoming increasingly more important to government's policy-making and people's decision making. In this paper, we predict the air quality of next 48 hours for each monitoring station, considering air quality data, meteorology data, and weather forecast data. Based on the domain knowledge about air pollution, we propose a deep neural network (DNN)-based approach (entitled DeepAir), which consists of a spatial transformation component and a deep distributed fusion network. Considering air pollutants' spatial correlations, the former component converts the spatial sparse air quality data into a consistent input to simulate the pollutant sources. The latter network adopts a neural distributed architecture to fuse heterogeneous urban data for simultaneously capturing the factors affecting air quality, e.g. meteorological conditions. We deployed DeepAir in our AirPollutionPrediction system, providing fine-grained air quality forecasts for 300+ Chinese cities every hour. The experimental results on the data from three-year nine Chinese-city demonstrate the advantages of DeepAir beyond 10 baseline methods. Comparing with the previous online approach in AirPollutionPrediction system, we have 2.4%, 12.2%, 63.2% relative accuracy improvements on short-term, long-term and sudden changes prediction, respectively.
- Akimoto Hajime. 2003. Global air quality and pollution. Science 302.5651 (2003), 1716--1719.Google Scholar
- Kampa Marilena, and Elias Castanas. 2008. Human health effects of air pollution. Environmental pollution 151, 2 (2008), 362--367.Google Scholar
- Julie Yixuan Zhu, Yu Zheng, Xiuwen Yi, Victor O.K. Li. 2016. A Gaussian Bayesian Model to Identify Spatio-temporal Causalities for Air Pollution based on Urban Big Data. In INFOCOM WKSHPS. IEEE, 3--8.Google Scholar
- Yu Zheng, Furui Liu, and Hsun-Ping Hsieh. 2013. U-Air: When Urban Air Quality Inference Meets Big Data. In SIGKDD. ACM, 1436--1444. Google ScholarDigital Library
- Jianyi Lu, and Xin Cao. 2015. PM2.5 Pollution in major cities in China: pollution status, emission sources and control measures. Fresenius Environmental Bulletin 24, 4A (2014): 1338--1349.Google Scholar
- Elena Baralis, Tania Cerquitelli, Silvia Chiusano, Paolo Garza, and Mohammad Reza Kavoosifar. 2016. Analyzing air pollution on the urban environment. In MIPRO. IEEE, 1464--1469.Google Scholar
- Yu Zheng, Xiuwen Yi, Ming Li, Ruiyuan Li, Zhangqing Shan, Eric Chang, and Tianrui Li. 2015. Forecasting Fine-Grained Air Quality Based on Big Data. In SIGKDD. ACM, 2267--2276. Google ScholarDigital Library
- AirPollutionPrediction Website: http://airprediction.urban-computing.com.Google Scholar
- Jie Bao, Ruiyuan Li, Xiuwen Yi, Yu Zheng. 2016. Managing massive trajectories on the cloud. In ACM SIGSPATIAL. ACM. 41. Google ScholarDigital Library
- Xiuwen Yi, Yu Zheng, Junbo Zhang, Tianrui Li. 2015. ST-MVL: Filling Missing Values in Geo-sensory Time Series Data. In IJCAI. 2704--2710. Google ScholarDigital Library
- George Y. Lu, David W. Wong. An adaptive inverse-distance weighting spatial interpolation technique. Computers &Geosciences, 34,9 (2008), 1044--1055. Google ScholarDigital Library
- Tobler, Waldo R.. 1970. A computer movie simulating urban growth in the Detroit region. Economic Geography 46, 2 (1970), 234--240.Google ScholarCross Ref
- Junbo Zhang, Yu Zheng, and Dekang Qi. 2017. Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction. In AAAI, 1655--1661.Google Scholar
- Sepp Hochreiter, and Jurgen Schmidhuber. 1997. Long short-term memory. Neural Computation 9, 8 (1997), 1735--1780. Google ScholarDigital Library
- Junbo Zhang, Yu Zheng, Dekang Qi, Ruiyuan Li, and Xiuwen Yi. 2016. DNN-Based Prediction Model for Spatio-Temporal Data. In SIGSPATIAL GIS. ACM, 92. Google ScholarDigital Library
- Junbo Zhang, Yu Zheng, Dekang Qi, Ruiyuan Li, Xiuwen Yi, Tianrui Li. 2018. Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual Networks. Artificial Intelligence, 259, (2018), 147--166,Google Scholar
- Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction. arXiv preprint arXiv:1703.04247 (2017).Google Scholar
- Djork-Arne Clevert, Thomas Unterthiner, and Sepp Hochreiter. 2015. Fast and accurate deep network learning by exponential linear units (ELUs). arXiv preprint arXiv, 1511.07289 (2015).Google Scholar
- Kingma Diederik, and Ba Jimmy. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv, 1412.6980.Google Scholar
- Srivastava Nitish, Geoffrey E. Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. Dropout: a simple way to prevent neural networks from overfitting. Journal of machine learning research 15, 1 (2014): 1929--1958. Google ScholarDigital Library
- Dong Wang, Junbo Zhang, Wei Cao, Jian Li, Yu Zheng. 2018. When Will You Arrive? Estimating Travel Time Based on Deep Neural Networks. In AAAI.Google Scholar
- Dong Wang, Wei Cao, Jian Li, Jieping Ye. 2017. DeepSD: Supply-Demand Prediction for Online Car-Hailing Services Using Deep Neural Networks. In ICDE. IEEE, 243--254.Google Scholar
- Yang Zhang, Bocquet Marc, Mallet Vivien, Seigneur Christian and Baklanov Alexander. 2012 a. Real-time Air Quality Forecasting, Part I: History, techniques, and current status, Atmospheric Environment, 60, (2012), 632--655.Google Scholar
- Yang Zhang, Bocquet Marc, Mallet Vivien, Seigneur Christian and Baklanov Alexander. 2012 b. Real-time Air Quality Forecasting, Part II: State of the science, current research needs, and future prospects, Atmospheric Environment, 60 (2012), 656--676.Google ScholarCross Ref
- Xuan Song, Kanasugi Hiroshi, and Shibasaki Ryosuke. 2016. DeepTransport: Prediction and simulation of human mobility and transport mode at a citywide level. In AAAI. 2618--2624. Google ScholarDigital Library
- Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, Zhenhui Li. 2018. Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction. In AAAI.Google Scholar
- Yuxuan Liang, Songyu Ke, Junbo Zhang, Xiuwen Yi, Yu Zheng. 2018. Multi-level Attention Networks for Geo-sensory Time Series Prediction. In IJCAI.Google Scholar
- Deep Distributed Fusion Network for Air Quality Prediction
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