Artificial intelligence-enabled context-aware air quality prediction for smart cities

2020 ◽  
Vol 271 ◽  
pp. 121941 ◽  
Author(s):  
Daniel Schürholz ◽  
Sylvain Kubler ◽  
Arkady Zaslavsky
Keyword(s):  
Artificial Intelligence ◽  
Air Quality ◽  
Smart Cities ◽  
Quality Prediction ◽  
Context Aware ◽  
Air Quality Prediction

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 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.

Development of Hybrid Artificial Neural Network and Decision Tree Algorithm for Air Quality Prediction

2021 ◽  
Author(s):  
S Gunasekar ◽  
G Joselin Retna Kumar ◽  
K Vijayakumar ◽  
G Pius Agbulu
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Decision Tree ◽  
Weather Forecasting ◽  
Smart Cities ◽  
Preventive Measure ◽  
Quality Prediction ◽  
Tree Model ◽  
Air Quality Prediction ◽  
Sensitivity Score

Abstract With the drastic development of smart cities and technology, various pollutions occurred in an environment such as air, water and noise. More specifically, air pollution has risen due to the population and transportation and it also direct impact on human health. To suggest a useful preventive measure, proper prediction of air quality is needed. Today machine learning (ML) algorithms support intelligent data analysis and are applied in various fields like medical, Market basket analysis, Finance and weather forecasting etc. This work is used to analyze the performance of air quality prediction by using various ML algorithms. In addition, a hybrid neural network and decision tree model is proposed for accurate forecasting. Experiential results show that the proposed hybrid model achieves higher accuracy than other methods. The proposed model attained an accuracy of 99.88%, with a sensitivity score of 99% and a specificity score of 99.88%.

scholarly journals Air Quality Prediction using Artificial Intelligence

2020 ◽  
Vol 9 (5) ◽  
pp. 1417-1420
Keyword(s):  
Artificial Intelligence ◽  
Air Quality ◽  
Urban Areas ◽  
Specific Area ◽  
Air Pollutant ◽  
Support Vector ◽  
Quality Prediction ◽  
Air Quality Prediction ◽  
Pollutant Concentrations ◽  
Spatio Temporal

Due to the critical impacts of air pollution, prediction and monitoring of air quality in urban areas are essential tasks. However, because of the dynamic nature and high Spatio-temporal variability, the prediction of the air pollutant concentrations is a complex Spatio-temporal problem. The data is collected in specific area such as climate condition and vehicular pollutant occurring in the peak hours. the predication process is used to compare the algorithm artificial neural network and support vector machine process. This paper presents a survey on Air quality prediction using artificial intelligence

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