Investigating China’s Urban Air Quality Using
Big Data, Information Theory,
and Machine Learning

Chen, Sheng; Kan, Guangyuan; Li, Jiren; Ke, Liang; Hong, Yang

doi:10.15244/pjoes/75159

Cited by 24 publications

(13 citation statements)

References 23 publications

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“…In terms of MAE (mean absolute error) and RMSE (root mean square error) values, research found that the support vector machine outperformed other machine-learning approaches. Chen et al [34] used physical sensors to investigate the air pollution of 16 large metropolitan areas. They identified the substances that impacted the air quality then established a link between them.…”

Section: Iot/sensor Innovation and Machine Learning For Air-quality Predictionmentioning

confidence: 99%

“…The works by Han et al [31], Chen et al [34] Xi et al [20], and Raj et al [21] for the prediction of air quality are limited since they rely on traditional machine learning with individual sensor readings. Unlike that work, we proposed a low-cost solution for the detection of different chemicals at city level using time-series data transformation into images.…”

Section: Iot/sensor Innovation and Machine Learning For Air-quality Predictionmentioning

confidence: 99%

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Event-Driven Deep Learning for Edge Intelligence (EDL-EI)

Shah

Tariq

Lee

et al. 2021

Sensors

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Edge intelligence (EI) has received a lot of interest because it can reduce latency, increase efficiency, and preserve privacy. More significantly, as the Internet of Things (IoT) has proliferated, billions of portable and embedded devices have been interconnected, producing zillions of gigabytes on edge networks. Thus, there is an immediate need to push AI (artificial intelligence) breakthroughs within edge networks to achieve the full promise of edge data analytics. EI solutions have supported digital technology workloads and applications from the infrastructure level to edge networks; however, there are still many challenges with the heterogeneity of computational capabilities and the spread of information sources. We propose a novel event-driven deep-learning framework, called EDL-EI (event-driven deep learning for edge intelligence), via the design of a novel event model by defining events using correlation analysis with multiple sensors in real-world settings and incorporating multi-sensor fusion techniques, a transformation method for sensor streams into images, and lightweight 2-dimensional convolutional neural network (CNN) models. To demonstrate the feasibility of the EDL-EI framework, we presented an IoT-based prototype system that we developed with multiple sensors and edge devices. To verify the proposed framework, we have a case study of air-quality scenarios based on the benchmark data provided by the USA Environmental Protection Agency for the most polluted cities in South Korea and China. We have obtained outstanding predictive accuracy (97.65% and 97.19%) from two deep-learning models on the cities’ air-quality patterns. Furthermore, the air-quality changes from 2019 to 2020 have been analyzed to check the effects of the COVID-19 pandemic lockdown.

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Section: Iot/sensor Innovation and Machine Learning For Air-quality Predictionmentioning

confidence: 99%

Section: Iot/sensor Innovation and Machine Learning For Air-quality Predictionmentioning

confidence: 99%

Event-Driven Deep Learning for Edge Intelligence (EDL-EI)

Shah

Tariq

Lee

et al. 2021

Sensors

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“…They have mentioned that real time decisionmaking can be performed, but did not mention the prediction accuracy. Another Apache Spark based AQI prediction system using Random Forest, implemented using the Spark distributed on multiple clusters is given in [31]. However, while Random Forests can be used for classification of data, the method is not used for real-time analysis of time series data.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Recently, in China, air pollutant data of different cities has been analyzed using an ensemble Neural Network technique for 16 cities in China [31]. Although, accuracy of predictive model is improved, the processing time is not discussed.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative Analysis of Machine Learning Techniques for Predicting Air Quality in Smart Cities

et al. 2019

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Dealing with air pollution presents a major environmental challenge in smart city environments. Real-time monitoring of pollution data enables local authorities to analyze the current traffic situation of the city and make decisions accordingly. Deployment of the Internet of Things-based sensors has considerably changed the dynamics of predicting air quality. Existing research has used different machine learning tools for pollution prediction; however, comparative analysis of these techniques is required to have a better understanding of their processing time for multiple datasets. In this paper, we have performed pollution prediction using four advanced regression techniques and present a comparative study to determine the best model for accurately predicting air quality with reference to data size and processing time. We have conducted experiments using Apache Spark and performed pollution estimation using multiple datasets. The Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) have been used as evaluation criteria for the comparison of these regression models. Furthermore, the processing time of each technique through standalone learning and through fitting the hyperparameter tuning on Apache Spark has also been calculated to find the best-fit model in terms of processing time and lowest error rate.INDEX TERMS IoT, smart city, air quality index (AQI), data mining, Apache Spark.

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“…In the forecasting model category, there are two articles that use NN, and meteorology and pollutants as predicting features. Chen et al [46] aim to build a model that forecasts AQI one day ahead by using an Ensemble Neural Network that processes selected factors using PEK-based machine learning for 16 main cities in China (three years of data). First a selection of the best predictors (PM 2.5 , PM 10 , and SO 2 ) is performed, based on Partial Mutual Information (PMI), which measures the degree of predictability of the output variable knowing the input variable, and then the daily AQI value is predicted, through PEK-based machine learning, by using the previous day's meteorological and pollution conditions.…”

Section: Category 1: Identifying Relevant Predictors and Understandinmentioning

confidence: 99%

Machine Learning Approaches for Outdoor Air Quality Modelling: A Systematic Review

2018

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Current studies show that traditional deterministic models tend to struggle to capture the non-linear relationship between the concentration of air pollutants and their sources of emission and dispersion. To tackle such a limitation, the most promising approach is to use statistical models based on machine learning techniques. Nevertheless, it is puzzling why a certain algorithm is chosen over another for a given task. This systematic review intends to clarify this question by providing the reader with a comprehensive description of the principles underlying these algorithms and how they are applied to enhance prediction accuracy. A rigorous search that conforms to the PRISMA guideline is performed and results in the selection of the 46 most relevant journal papers in the area. Through a factorial analysis method these studies are synthetized and linked to each other. The main findings of this literature review show that: (i) machine learning is mainly applied in Eurasian and North American continents and (ii) estimation problems tend to implement Ensemble Learning and Regressions, whereas forecasting make use of Neural Networks and Support Vector Machines. The next challenges of this approach are to improve the prediction of pollution peaks and contaminants recently put in the spotlights (e.g., nanoparticles).

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Investigating China’s Urban Air Quality Using Big Data, Information Theory, and Machine Learning

Cited by 24 publications

References 23 publications

Event-Driven Deep Learning for Edge Intelligence (EDL-EI)

Event-Driven Deep Learning for Edge Intelligence (EDL-EI)

Comparative Analysis of Machine Learning Techniques for Predicting Air Quality in Smart Cities

Machine Learning Approaches for Outdoor Air Quality Modelling: A Systematic Review

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