scholarly journals Air Quality Prediction Based on a Spatiotemporal Attention Mechanism

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xiangyu Zou ◽  
Jinjin Zhao ◽  
Duan Zhao ◽  
Bin Sun ◽  
Yongxin He ◽  
...  
Keyword(s):  
Air Quality ◽  
Spatial Data ◽  
Short Term Memory ◽  
Smart Cities ◽  
Line Graph ◽  
Rapid Development ◽  
Road Networks ◽  
Attention Mechanism ◽  
Quality Prediction ◽  
Air Quality Prediction

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.

scholarly journals Air Quality Prediction in Smart Cities Using Machine Learning Technologies based on Sensor Data: A Review

2020 ◽  
Vol 10 (7) ◽  
pp. 2401 ◽  
Author(s):  
Ditsuhi Iskandaryan ◽  
Francisco Ramos ◽  
Sergio Trilles
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Air Quality ◽  
Smart Cities ◽  
Learning Technologies ◽  
Sensor Data ◽  
Quality Prediction ◽  
Air Quality Prediction ◽  
Air Pollution Prediction ◽  
Pollution Prediction

The influence of machine learning technologies is rapidly increasing and penetrating almost in every field, and air pollution prediction is not being excluded from those fields. This paper covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities. Using the most popular databases and executing the corresponding filtration, the most relevant papers were selected. After thorough reviewing those papers, the main features were extracted, which served as a base to link and compare them to each other. As a result, we can conclude that: (1) instead of using simple machine learning techniques, currently, the authors apply advanced and sophisticated techniques, (2) China was the leading country in terms of a case study, (3) Particulate matter with diameter equal to 2.5 micrometers was the main prediction target, (4) in 41% of the publications the authors carried out the prediction for the next day, (5) 66% of the studies used data had an hourly rate, (6) 49% of the papers used open data and since 2016 it had a tendency to increase, and (7) for efficient air quality prediction it is important to consider the external factors such as weather conditions, spatial characteristics, and temporal features.

scholarly journals Research on Urban Air Quality Prediction Based on Ensemble Learning of XGBoost

2020 ◽  
Vol 165 ◽  
pp. 02014
Author(s):  
Haotian Jing ◽  
Yingchun Wang
Keyword(s):  
Air Quality ◽  
Rapid Development ◽  
Absolute Error ◽  
Urban Air Quality ◽  
Urban Air ◽  
Quality Prediction ◽  
Prediction Ability ◽  
Air Quality Prediction ◽  
Pollutant Concentrations ◽  
Air Quality Assessment

In recent years, with the rapid development of China’s economy and the continuous improvement of people’s quality of life, air pollution caused by a large amount of energy consumption has become increasingly serious. Air quality index (AQI) has become an important basis to measure air quality. At present, the research on air quality assessment and prediction methods has become increasingly active at home and abroad, which is of great significance to guide people’s production and life. In this paper, Taking Shijiazhuang, Hebei Province as an example and using the XGBoost model of the machine learning ensemble algorithm, regression fitting was performed on the six pollutant concentrations that currently mainly affect air quality, and the hourly prediction of AQI was achieved.The trained model has lower mean absolute error (MAE) and higher correlation coefficient (R-square), which improves the prediction ability of urban air quality prediction, provides a new idea for urban air quality prediction, and has a broad application prospect in the future urban air quality prediction.

scholarly journals Prediction of Ambient PM2.5 Concentrations Using a Correlation Filtered Spatial-Temporal Long Short-Term Memory Model

2019 ◽  
Vol 10 (1) ◽  
pp. 14 ◽  
Author(s):  
Yuexiong Ding ◽  
Zheng Li ◽  
Chengdian Zhang ◽  
Jun Ma
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Quality Prediction ◽  
Short Term ◽  
Concentration Data ◽  
Term Memory ◽  
Air Quality Prediction ◽  
Long Short Term Memory

Due to the increasingly serious air pollution problem, air quality prediction has been an important approach for air pollution control and prevention. Many prediction methods have been proposed in recent years to improve the prediction accuracy. However, most of the existing methods either did not consider the spatial relationships between monitoring stations or overlooked the strength of the correlation. Excluding the spatial correlation or including too much weak spatial inputs could influence the modeling and reduce the prediction accuracy. To overcome the limitation, this paper proposes a correlation filtered spatial-temporal long short-term memory (CFST-LSTM) model for air quality prediction. The model is designed based on the original LSTM model and is equipped with a spatial-temporal filter (STF) layer. This layer not only takes into account the spatial influence between stations, but also can extract highly correlated sequential data and drop weaker ones. To evaluate the proposed CFST-LSTM model, hourly PM2.5 concentration data of California are collected and preprocessed. Several experiments are conducted. The experimental results show that the CFST-LSTM model can effectively improve the prediction accuracy and has great generalization.

scholarly journals Features Exploration from Datasets Vision in Air Quality Prediction Domain

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 312
Author(s):  
Ditsuhi Iskandaryan ◽  
Francisco Ramos ◽  
Sergio Trilles
Keyword(s):  
Air Quality ◽  
Spatial Data ◽  
Research Work ◽  
Open Data ◽  
World Population ◽  
Quality Prediction ◽  
Temporal Data ◽  
Air Quality Prediction ◽  
The World ◽  
Monitoring And Forecasting

Air pollution and its consequences are negatively impacting on the world population and the environment, which converts the monitoring and forecasting air quality techniques as essential tools to combat this problem. To predict air quality with maximum accuracy, along with the implemented models and the quantity of the data, it is crucial also to consider the dataset types. This study selected a set of research works in the field of air quality prediction and is concentrated on the exploration of the datasets utilised in them. The most significant findings of this research work are: (1) meteorological datasets were used in 94.6% of the papers leaving behind the rest of the datasets with a big difference, which is complemented with others, such as temporal data, spatial data, and so on; (2) the usage of various datasets combinations has been commenced since 2009; and (3) the utilisation of open data have been started since 2012, 32.3% of the studies used open data, and 63.4% of the studies did not provide the data.

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