Long-term effects of outdoor air pollution on mortality and morbidity–prediction using nonlinear autoregressive and artificial neural networks models

Khojasteh, Davood Namdar; Taghizadeh–Mehrjardi, Ruhollah; Asumadu-Sakyi, Akwasi Bonsu; Fehresti-Sani, Masoud

doi:10.1016/j.apr.2020.10.007

Cited by 23 publications

(10 citation statements)

References 4 publications

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“…( 17), where 𝑦(𝑡) is the output value at the current moment, y(t − 1), 𝑦(𝑡 − 2), ⋯ , 𝑦(𝑡 − 𝑑) are the output value at the historical moment, and 𝑑 is the delay order. NAR neural network consists of input layer, hidden layer and output layer [38]. For the selection of the number of neurons in the hidden layer and the order of input delay, we also use grid optimization to optimize in [5,15]× [1,5].…”

Section: Pca-rvm-nar Model Constructionmentioning

confidence: 99%

Calibration of miniature air quality detector monitoring data with PCA–RVM–NAR combination model

Liu

Zhang²

2022

Preprint

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The development of miniature air quality detectors makes it possible for humans to monitor air quality in real time and grid. However, the accuracy of measuring pollutants by miniature air quality detectors needs to be improved. In this paper, the PCA-RVM-NAR combined model is proposed to calibrate the measurement accuracy of the miniature air quality detector. First, correlation analysis is used to find out the main factors affecting pollutant concentrations. Second, principal component analysis is used to reduce the dimensionality of these main factors and extract their main information. Thirdly, taking the extracted principal components as independent variables and the observed values of pollutant concentrations as dependent variables, a PCA-RVM model is established by the relevance vector machine. Finally, the nonlinear autoregressive neural network is used to correct the error and finally complete the establishment of the PCA-RVM-NAR model. Root mean square error, goodness of fit, mean absolute error and relative mean absolute percent error are used to compare the calibration effect of PCA-RVM-NAR model and other commonly used models such as support vector machine, multilayer perceptron neural network and nonlinear autoregressive models with exogenous input. The results show that, no matter which pollutant, the PCA-RVM-NAR model achieves better calibration results than other models in the four indicators. Using this model to correct the data of the miniature air quality detector can improve its accuracy by 77.8%~93.9%.

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Section: Pca-rvm-nar Model Constructionmentioning

confidence: 99%

Calibration of miniature air quality detector monitoring data with PCA–RVM–NAR combination model

Liu

Zhang²

2022

Preprint

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show abstract

“…( 17 ), where y ( t ) is the output value at the current moment, are the output value at the historical moment, and is the delay order. NAR neural network consists of input layer, hidden layer and output layer 38 . For the selection of the number of neurons in the hidden layer and the order of input delay, we also use grid optimization to optimize in [5, 15] × [1, 5].…”

Section: Establishment Of Sensor Calibration Modelmentioning

confidence: 99%

Calibration of miniature air quality detector monitoring data with PCA–RVM–NAR combination model

Liu

Zhang²

2022

Sci Rep

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The development of miniature air quality detectors makes it possible for humans to monitor air quality in real time and grid. However, the accuracy of measuring pollutants by miniature air quality detectors needs to be improved. In this paper, the PCA–RVM–NAR combined model is proposed to calibrate the measurement accuracy of the miniature air quality detector. First, correlation analysis is used to find out the main factors affecting pollutant concentrations. Second, principal component analysis is used to reduce the dimensionality of these main factors and extract their main information. Thirdly, taking the extracted principal components as independent variables and the observed values of pollutant concentrations as dependent variables, a PCA–RVM model is established by the relevance vector machine. Finally, the nonlinear autoregressive neural network is used to correct the error and finally complete the establishment of the PCA–RVM–NAR model. Root mean square error, goodness of fit, mean absolute error and relative mean absolute percent error are used to compare the calibration effect of PCA–RVM–NAR model and other commonly used models such as multiple linear regression model, support vector machine, multilayer perceptron neural network and nonlinear autoregressive models with exogenous input. The results show that, no matter which pollutant, the PCA–RVM–NAR model achieves better calibration results than other models in the four indicators. Using this model to correct the data of the miniature air quality detector can improve its accuracy by 77.8–93.9%.

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“…Among all the kinds of air pollution, the impacts of PM 2.5 , PM 10 , and nitrides and sulfides on human health have become a research focus worldwide. Many studies have shown that the population mortality rate and the incidence of various human respiratory and circulatory system diseases are closely related to short-term exposure to air pollution [13][14][15][16][17][18][19][20]. Burkart et al [21] adopted bivariate response surface models (BRSMs) and generalized additive models (GAMs) to assess the increased mortality risk from high temperatures and air pollution.…”

Section: Introductionmentioning

confidence: 99%

Risk Assessment and Prediction of Air Pollution Disasters in Four Chinese Regions

Deng

Xie

et al. 2022

Sustainability

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Evaluating the regional trends of air pollution disaster risk in areas of heavy industry and economically developed cities is vital for regional sustainable development. Until now, previous studies have mainly adopted a traditional weighted comprehensive evaluation method to analyze the air pollution disaster risk. This research has integrated principal component analysis (PCA), a genetic algorithm (GA) and a backpropagation (BP) neural network to evaluate the regional disaster risk. Hazard risk, hazard-laden environment sensitivity, hazard-bearing body vulnerability and disaster resilience were used to measure the degree of disaster risk. The main findings were: (1) the air pollution disaster risk index of Liaoning Province, Beijing, Shanghai and Guangdong Province increased year by year from 2010 to 2019; (2) the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) of each regional air pollution disaster risk index in 2019, as predicted by the PCA-GA-BP neural network, were 0.607, 0.317 and 20.3%, respectively; (3) the predicted results were more accurate than those using a PCA-BP neural network, GA-BP neural network, traditional BP neural network, support vector regression (SVR) or extreme gradient boosting (XGBoost), which verified that machine learning could be used as a method of air pollution disaster risk assessment to a considerable extent.

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Long-term effects of outdoor air pollution on mortality and morbidity–prediction using nonlinear autoregressive and artificial neural networks models

Cited by 23 publications

References 4 publications

Calibration of miniature air quality detector monitoring data with PCA–RVM–NAR combination model

Calibration of miniature air quality detector monitoring data with PCA–RVM–NAR combination model

Calibration of miniature air quality detector monitoring data with PCA–RVM–NAR combination model

Risk Assessment and Prediction of Air Pollution Disasters in Four Chinese Regions

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