ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4/W2, 15-22, 2017
https://doi.org/10.5194/isprs-annals-IV-4-W2-15-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
 
19 Oct 2017
A Spatiotemporal Prediction Framework for Air Pollution Based on Deep RNN
J. Fan1,2, Q. Li1,2, J. Hou1,2, X. Feng1, H. Karimian1, and S. Lin2 1Institute of Remote Sensing and GIS, Peking University, 100871 Beijing, China
2Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, China
Keywords: air pollution, missing value, RNN, LSTM, deep learning Abstract. Time series data in practical applications always contain missing values due to sensor malfunction, network failure, outliers etc. In order to handle missing values in time series, as well as the lack of considering temporal properties in machine learning models, we propose a spatiotemporal prediction framework based on missing value processing algorithms and deep recurrent neural network (DRNN). By using missing tag and missing interval to represent time series patterns, we implement three different missing value fixing algorithms, which are further incorporated into deep neural network that consists of LSTM (Long Short-term Memory) layers and fully connected layers. Real-world air quality and meteorological datasets (Jingjinji area, China) are used for model training and testing. Deep feed forward neural networks (DFNN) and gradient boosting decision trees (GBDT) are trained as baseline models against the proposed DRNN. Performances of three missing value fixing algorithms, as well as different machine learning models are evaluated and analysed. Experiments show that the proposed DRNN framework outperforms both DFNN and GBDT, therefore validating the capacity of the proposed framework. Our results also provides useful insights for better understanding of different strategies that handle missing values.
Conference paper (PDF, 1165 KB)


Citation: Fan, J., Li, Q., Hou, J., Feng, X., Karimian, H., and Lin, S.: A Spatiotemporal Prediction Framework for Air Pollution Based on Deep RNN, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4/W2, 15-22, https://doi.org/10.5194/isprs-annals-IV-4-W2-15-2017, 2017.

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