Deep Multi-Task Learning Based Urban Air Quality Index Modelling

Chen, Ling; Ding, Yifang; Lyu, Dandan; Liu, Xiaoze; Long, Hanyu

doi:10.1145/3314389

Cited by 33 publications

(33 citation statements)

References 17 publications

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“…As shown in Figure 3, the network graph G aa � (A ∪ A, β aa ) between prediction regions represents the distance relationship of each region, where A represents the region to be predicted, β aa represents the set of edges e ij between any two regions, and the weight w ij represents the distance between the two areas. On the right side of Figure 4, the network graph G ap � (A ∪ P, β ap ) between the area and the POIs represents the distribution of POIs in the prediction area, where P represents the collection of POI categories, and the categories p 1 ∼p 10 are, respectively, expressed as transportation spots, factories, parks, stores, eating and drinking establishments, stadiums, schools, real estate, entertainment establishments, and other establishments [9]. β ap represents the set of edges e ij between the region and the POI category, and its weight w ij represents the number of POIs containing category p j in the prediction area i. e network graph G ar � (A ∪ R, β ar ) between the area and the road network in the left part of the figure represents the distribution of road segments in the prediction area, where R represents the set of road segment categories, β ar represents the set of edges e ij between the region and the road segment categories, and its weight w ij represents the total length of the roads of category r j included in the prediction area i.…”

Section: Feature Extraction Of Nonsequential Information For Auxiliary Predictionmentioning

confidence: 99%

“…For example, if the environment around the predicted area is good, then its air quality will also be good and will change nonlinearly with time. In addition, nonsequential information such as the POI and road network [8,9] also affects the prediction of air quality. For example, the air quality near a park is much better than the air quality near a factory.…”

Section: Introductionmentioning

confidence: 99%

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Air Quality Prediction Based on a Spatiotemporal Attention Mechanism

Zou

Zhao

Duan

et al. 2021

Mobile Information Systems

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With the rapid development of the Internet of Things and Big Data, smart cities have received increasing attention. Predicting air quality accurately and efficiently is an important part of building a smart city. However, air quality prediction is very challenging because it is affected by many complex factors, such as dynamic spatial correlation between air quality detection sensors, dynamic temporal correlation, and external factors (such as road networks and points of interest). Therefore, this paper proposes a long short-term memory (LSTM) air quality prediction model based on a spatiotemporal attention mechanism (STA-LSTM). The model uses an encoder-decoder structure to model spatiotemporal features. A spatial attention mechanism is introduced in the encoder to capture the relative influence of surrounding sites on the prediction area. A temporal attention mechanism is introduced in the decoder to capture the time dependence of air quality. In addition, for spatial data such as point of interest (POI) and road networks, this paper uses the LINE graph embedding method to obtain a low-dimensional vector representation of spatial data to obtain abundant spatial features. This paper evaluates STA-LSTM on the Beijing dataset, and the root mean square error (RMSE) and R-squared ( R 2 ) indicators are used to compare with six benchmarks. The experimental results show that the model proposed in this paper can achieve better performance than the performances of other benchmarks.

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Section: Feature Extraction Of Nonsequential Information For Auxiliary Predictionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Air Quality Prediction Based on a Spatiotemporal Attention Mechanism

Zou

Zhao

Duan

et al. 2021

Mobile Information Systems

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“…Lin et al [23] tried to represent the spatial correlation in a graph with automatically selected important geographic feature types that largely affect PM 2.5 concentrations and uses those important geographic features to compute the adjacency graph for the model. To conquer the challenge of lacking training samples, Chen et al [3] proposed a multi-task based approach to learn the representations of the relevant spatial and sequential data, as well as to build the correlation between air quality and these representations.…”

Section: Related Workmentioning

confidence: 99%

TIP-Air: Tracking Pollution Transfer for Accurate Air Quality Prediction

Cheng

Saukh

Thiele

2021

Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of The

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Air quality is of vital importance to human health. Accurately predicting air quality, especially its sudden changes, is highly valuable for citizens and governments to make personal and local decisions, design intelligent policies and control pollution at minimal cost. However, none of the existing methods achieves sufficient prediction accuracy for time intervals of sudden pollution change due to inability of existing models to take into account pollution propagation between different areas caused by air mass movement. For the first time, we consider pollution transfer in the context of shortterm air quality prediction and propose to use air flow trajectory data, widely used in environmental sciences, to represent pollution transfer patterns between different locations. By learning trajectory representations, measurement location embedding vectors, and interrelationships between local weather at relevant locations, we propose a new attention based seq2seq model to track pollution propagation for accurate air quality prediction. We evaluate our model on datasets from Beijing area and compare the results to several state-of-the-art baselines. Experiments show that the proposed approach can successfully capture pollution transfer patterns between different sites in the area. Our model outperforms all the baselines and decreases prediction errors by 9.6% to 22.4%. The method allows interpreting prediction results visually and analytically.

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“…One typical approach is to use convolutional layers over a spatial map and to use recurrent layers over a time sequence. Lan et al [7] and Chen et al [3] adopted convolutional layers on the morphological layout of a city and LSTM to estimate travel time and predict urban air quality index. Recently convolutional graph networks have drawn more attention in helping discover non-linearity correlation between nodes and edges in a graph [5,17].…”

Section: Related Workmentioning

confidence: 99%

Mobility Irregularity Detection with Smart Transit Card Data

Wang

Yao

Liu

et al. 2020

Advances in Knowledge Discovery and Data Mining

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Identifying patterns and detecting irregularities regarding individual mobility in public transport system is crucial for transport planning and law enforcement applications (e.g., fraudulent behavior). In this context, most of recent approaches exploit similarity learning through comparing spatial-temporal patterns between normal and irregular records. However, they are limited in utilizing passenger-level information. First, all passenger transits are fused in a certain region at a timestamp whereas each passenger has own repetitive stops and time slots. Second, these differences in passenger profile result in high intraclass variance of normal records and blur the decision boundaries. To tackle these problems, we propose a modelling framework to extract passenger-level spatial-temporal profile and present a personalised similarity learning for irregular behavior detection. Specifically, a route-tostop embedding is proposed to extract spatial correlations between transit stops and routes. Then attentive fusion is adopted to uncover spatial repetitive and time invariant patterns. Finally, a personalised similarity function is learned to evaluate the historical and recent mobility patterns. Experimental results on a large-scale dataset demonstrate that our model outperforms the state-of-the-art methods on recall, F1 score and accuracy. Raw features and the extracted patterns are visualized and illustrate the learned deviation between the normal and the irregular records.

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Deep Multi-Task Learning Based Urban Air Quality Index Modelling

Cited by 33 publications

References 17 publications

Air Quality Prediction Based on a Spatiotemporal Attention Mechanism

Air Quality Prediction Based on a Spatiotemporal Attention Mechanism

TIP-Air: Tracking Pollution Transfer for Accurate Air Quality Prediction

Mobility Irregularity Detection with Smart Transit Card Data

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