Developing an early-warning system for air quality prediction and assessment of cities in China

Wang, Jianzhou; Zhang, Xiaobo; Zhang, Guo; Lu, Hanqing

doi:10.1016/j.eswa.2017.04.059

Cited by 63 publications

(28 citation statements)

References 39 publications

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“…The forecasting of air quality became popular, many methods have been purposed for forecasting air quality such as hidden Markov model [15], first-order and one-variable grey model [16], developed support vector machine [17], fuzzy time series model [18][19] [25], Solar Radiation [26], and Fuzzy-AHP [27].…”

Section: Related Workmentioning

confidence: 99%

“…Compared with forecasting, determination of air pollution has recently become an important issue due to https://doi.org/10.1051/e3sconf/2018730 , 0 (2018) E3S Web of Conferences 73 ICENIS 2018 50 50 26 26 its significance, and several new methodologies have been developed for the determination of air quality, such as Fuzzy-AHP [27], artificial neural networks [28], and real-time measurement [29]. Based on the literature on the determination of air quality mentioned above, real-time measurement became an important issue since air pollution parameters difficult detected by a human body.…”

Section: Related Workmentioning

confidence: 99%

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Web Based Information System of Carbon Monoxide Pollution

2018

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In Indonesia, carbon monoxide is one of which type of gas used as a parameter in the air pollution. Unfortunately, reporting and monitoring air pollution in Indonesia is regulated in government rules and reported once a day. The value of carbon monoxide concentration always change but the published information is out of date. Without real-time information, people cannot avoid the danger of monoxide pollution toxicity effect. This paper purpose the solution by publishes the real-time information from the carbon monoxide sensor data acquisition. This Research uses the rule-based method to calculate carbon monoxide pollution from data acquisition. Results of experiments show that information generated by the system in accordance with the manual calculation results of carbon monoxide pollution. This research contributed to providing information on real-time carbon monoxide measurement to the public, so the danger of carbon monoxide poisoning can be avoided.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Web Based Information System of Carbon Monoxide Pollution

2018

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“…Consequently, these proxies are unable to evaluate and assess the estimation uncertainty. For instance, if there is a sudden increase of pollutant concentration estimation, it is not possible to assess its confidence interval [26,27]. Therefore, additional measurements and validation may be required to confirm the estimation.…”

Section: Introductionmentioning

confidence: 99%

Mutual Information Input Selector and Probabilistic Machine Learning Utilisation for Air Pollution Proxies

et al. 2019

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An air pollutant proxy is a mathematical model that estimates an unobserved air pollutant using other measured variables. The proxy is advantageous to fill missing data in a research campaign or to substitute a real measurement for minimising the cost as well as the operators involved (i.e., virtual sensor). In this paper, we present a generic concept of pollutant proxy development based on an optimised data-driven approach. We propose a mutual information concept to determine the interdependence of different variables and thus select the most correlated inputs. The most relevant variables are selected to be the best proxy inputs, where several metrics and data loss are also involved for guidance. The input selection method determines the used data for training pollutant proxies based on a probabilistic machine learning method. In particular, we use a Bayesian neural network that naturally prevents overfitting and provides confidence intervals around its output prediction. In this way, the prediction uncertainty could be assessed and evaluated. In order to demonstrate the effectiveness of our approach, we test it on an extensive air pollution database to estimate ozone concentration.

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“…Commonly used machine learning algorithms include multiple linear regression (MLR), random forest (RF) [23], support vector regression (SVR) [24], artificial neural networks (ANN) [25], and so forth. Previous studies have found that machine learning methods achieve excellent performance due to the nonlinear relationships within data, meaning that these methods are better suited to parameter statistic models and need less training time than dispersion models [26][27][28]. Deep learning algorithms, as a relative newcomer, have obtained outstanding prediction or detection performances in various application domains such as speech recognition, natural language processing, and computer vision [29].…”

Section: Introductionmentioning

confidence: 99%

Deep Random Subspace Learning: A Spatial-Temporal Modeling Approach for Air Quality Prediction

Sun

2019

Atmosphere

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Decrease in air quality is one of the most crucial threats to human health. There is an imperative and necessary need for more accurate air quality prediction. To meet this need, we propose a novel long short-term memory-based deep random subspace learning (LSTM-DRSL) framework for air quality forecasting. Specifically, we incorporate real-time pollutant emission data into the model input. We also design a spatial-temporal analysis approach to make good use of these data. The prediction model is developed by combining random subspace learning with a deep learning algorithm in order to improve the prediction accuracy. Empirical analyses based on multiple datasets over China from January 2015 to September 2017 are performed to demonstrate the efficacy of the proposed framework for hourly pollutant concentration prediction at an urban-agglomeration scale. The empirical results indicate that our framework is a viable method for air quality prediction. With consideration of the regional scale, the LSTM-DRSL framework performs better at a relatively large regional scale (around 200–300 km). In addition, the quality of predictions is higher in industrial areas. From a temporal point of view, the LSTM-DRSL framework is more suitable for hourly predictions.

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Developing an early-warning system for air quality prediction and assessment of cities in China

Cited by 63 publications

References 39 publications

Web Based Information System of Carbon Monoxide Pollution

Web Based Information System of Carbon Monoxide Pollution

Mutual Information Input Selector and Probabilistic Machine Learning Utilisation for Air Pollution Proxies

Deep Random Subspace Learning: A Spatial-Temporal Modeling Approach for Air Quality Prediction

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