Application of combined model of stepwise regression analysis and artificial neural network in data calibration of miniature air quality detector

AbstractIn this paper, six types of air pollutant concentrations are taken as the research object, and the data monitored by the micro air quality detector are calibrated by the national control point measurement data. We use correlation analysis to find out the main factors affecting air quality, and then build a stepwise regression model for six types of pollutants based on 8 months of data. Taking the stepwise regression fitting value and the data monitored by the miniature air quality detector as input variables, combined with the multilayer perceptron neural network, the SRA-MLP model was obtained to correct the pollutant data. We compared the stepwise regression model, the standard multilayer perceptron neural network and the SRA-MLP model by three indicators. Whether it is root mean square error, average absolute error or average relative error, SRA-MLP model is the best model. Using the SRA-MLP model to correct the data can increase the accuracy of the self-built point data by 42.5% to 86.5%. The SRA-MLP model has excellent prediction effects on both the training set and the test set, indicating that it has good generalization ability. This model plays a positive role in scientific arrangement and promotion of miniature air quality detectors. It can be applied not only to air quality monitoring, but also to the monitoring of other environmental indicators.

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A data calibration method for micro air quality detectors based on a LASSO regression and NARX neural network combined model

Scientific Reports ◽

10.1038/s41598-021-00804-7 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Bing Liu ◽

Yueqiang Jin ◽

Dezhi Xu ◽

Yishu Wang ◽

Chaoyang Li

Keyword(s):

Neural Network ◽

Air Quality ◽

Influencing Factors ◽

Control Point ◽

Measurement Data ◽

Pollutant Concentration ◽

Lasso Regression ◽

Narx Model ◽

Narx Neural Network ◽

Better Than

AbstractStudies have shown that there is a certain correlation between air pollution and various human diseases, especially lung diseases, so it is very meaningful to monitor the concentration of pollutants in the air. Compared with the national air quality monitoring station (national control point), the micro air quality detector has the advantage that it can monitor the concentration of pollutants in real time and grid, but its measurement accuracy needs to be improved. This paper proposes a model combining the least absolute selection and shrinkage operator (LASSO) regression and nonlinear autoregressive models with exogenous inputs (NARX) to calibrate the data measured by the micro air quality detector. Before establishing the LASSO-NARX model, correlation analysis is used to test whether the correlation between the concentration of air pollutants and its influencing factors is significant, and to find out the main factors that affect the concentration of pollutants. Due to the multicollinearity between various influencing factors, LASSO regression is used to further screen the influencing factors and give the quantitative relationship between the pollutant concentration and various influencing factors. In order to improve the prediction accuracy of pollutant concentration, the predicted value of each pollutant concentration in the LASSO regression model and the measurement data of the micro air quality detector are used as input variables, and the LASSO-NARX model is constructed using the NARX neural network. Several indicators such as goodness of fit, root mean square error, mean absolute error and relative mean absolute percent error are used to compare various air quality models. The results show that the prediction results of the LASSO-NARX model are not only better than the LASSO model alone and the NARX model alone, but also better than the commonly used multilayer perceptron and radial basis function neural network. Using this model to calibrate the measurement data of the micro air quality detector can increase the accuracy by 61.3–91.7%.

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A Data Calibration Method of Micro Air Quality Detector Based on LASSO Regression and NARX Neural Network Combined Model

10.21203/rs.3.rs-861855/v1 ◽

2021 ◽

Author(s):

Bing Liu ◽

Yueqiang Jin ◽

Dezhi Xu ◽

Yishu Wang ◽

Chaoyang Li

Keyword(s):

Neural Network ◽

Air Quality ◽

Influencing Factors ◽

Control Point ◽

Measurement Data ◽

Lasso Regression ◽

National Control ◽

Narx Model ◽

Narx Neural Network ◽

Better Than

Abstract Studies have shown that there is a certain correlation between air pollution and various human diseases, especially lung diseases, so it is very meaningful to monitor the concentration of pollutants in the air. Compared with the national air quality monitoring station (national control point), the micro air quality detector has the advantage that it can monitor the concentration of pollutants in real time and grid, but its measurement accuracy needs to be improved. In this paper, the measurement data of the micro air quality detector is calibrated with the help of the LASSO regression and NARX neural network combination (LASSO-NARX) model using the data measured by the national control point. First, correlation analysis is used to test whether the correlation between the concentration of air pollutants and its influencing factors is significant. Second, LASSO regression is used to give the quantitative relationship between pollutant concentration and various influencing factors. Third, the predicted value of each pollutant concentration in the LASSO regression model and the measurement data of the micro air quality detector are used as input variables, and the LASSO-NARX model is constructed using the NARX neural network. Finally, several indicators such as Root Mean Square Error, goodness of fit, Mean Absolute Error and Relative Mean Absolute Percent Error are used to compare various air quality models. The results show that the prediction results of the LASSO-NARX model are not only better than the LASSO model alone and the NARX model alone, but also better than the commonly used multilayer perceptron and radial basis function neural network. The LASSO-NARX model performed equally well on the training set and test set, indicating that the model has excellent generalization capabilities. Using this model to calibrate the measurement data of the micro air quality detector can increase the accuracy by 61.3% to 91.7%.

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A Layered Recurrent Neural Network for Imputing Air Pollutants Missing Data and Prediction of NO 2, O 3, PM 10, and PM 2.5

10.5772/intechopen.93678 ◽

2020 ◽

Author(s):

Hamza Turabieh ◽

Alaa Sheta ◽

Malik Braik ◽

Elvira Kovač-Andrić

Keyword(s):

Neural Network ◽

Air Pollution ◽

Air Quality ◽

Recurrent Neural Network ◽

Missing Values ◽

Prediction Models ◽

Air Pollutant ◽

Forecasting Models ◽

Monitoring Strategies ◽

Artificial Neural Network Ann

To fulfill the national air quality standards, many countries have created emissions monitoring strategies on air quality. Nowadays, policymakers and air quality executives depend on scientific computation and prediction models to monitor that cause air pollution, especially in industrial cities. Air pollution is considered one of the primary problems that could cause many human health problems such as asthma, damage to lungs, and even death. In this study, we present investigated development forecasting models for air pollutant attributes including Particulate Matters (PM2.5, PM10), ground-level Ozone (O3), and Nitrogen Oxides (NO2). The dataset used was collected from Dubrovnik city, which is located in the east of Croatia. The collected data has missing values. Therefore, we suggested the use of a Layered Recurrent Neural Network (L-RNN) to impute the missing value(s) of air pollutant attributes then build forecasting models. We adopted four regression models to forecast air pollutant attributes, which are: Multiple Linear Regression (MLR), Decision Tree Regression (DTR), Artificial Neural Network (ANN) and L-RNN. The obtained results show that the proposed method enhances the overall performance of other forecasting models.

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Comparing Activation Functions in Modeling Shoreline Variation Using Multilayer Perceptron Neural Network

Water ◽

10.3390/w12051281 ◽

2020 ◽

Vol 12 (5) ◽

pp. 1281

Author(s):

Je-Chian Chen ◽

Yu-Min Wang

Keyword(s):

Neural Network ◽

Multilayer Perceptron ◽

Activation Function ◽

Aerial Survey ◽

Activation Functions ◽

Shoreline Changes ◽

Extensive Evaluation ◽

Aerial Vehicle ◽

Sine Functions ◽

Mlp Model

The study has modeled shoreline changes by using a multilayer perceptron (MLP) neural network with the data collected from five beaches in southern Taiwan. The data included aerial survey maps of the Forestry Bureau for years 1982, 2002, and 2006, which served as predictors, while the unmanned aerial vehicle (UAV) surveyed data of 2019 served as the respondent. The MLP was configured using five different activation functions with the aim of evaluating their significance. These functions were Identity, Tahn, Logistic, Exponential, and Sine Functions. The results have shown that the performance of an MLP model may be affected by the choice of an activation function. Logistic and the Tahn activation functions outperformed the other models, with Logistic performing best in three beaches and Tahn having the rest. These findings suggest that the application of machine learning to shoreline changes should be accompanied by an extensive evaluation of the different activation functions.

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A DBN-Based Deep Neural Network Model with Multitask Learning for Online Air Quality Prediction

Journal of Control Science and Engineering ◽

10.1155/2019/5304535 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 5

Author(s):

Jiangeng Li ◽

Xingyang Shao ◽

Rihui Sun

Keyword(s):

Neural Network ◽

Air Quality ◽

Network Model ◽

Neural Network Model ◽

Prediction Accuracy ◽

Deep Neural Network ◽

Multitask Learning ◽

Air Pollutant ◽

Quality Prediction ◽

Air Quality Prediction

To avoid the adverse effects of severe air pollution on human health, we need accurate real-time air quality prediction. In this paper, for the purpose of improve prediction accuracy of air pollutant concentration, a deep neural network model with multitask learning (MTL-DBN-DNN), pretrained by a deep belief network (DBN), is proposed for forecasting of nonlinear systems and tested on the forecast of air quality time series. MTL-DBN-DNN model can solve several related prediction tasks at the same time by using shared information contained in the training data of different tasks. In the model, DBN is used to learn feature representations. Each unit in the output layer is connected to only a subset of units in the last hidden layer of DBN. Such connection effectively avoids the problem that fully connected networks need to juggle the learning of each task while being trained, so that the trained networks cannot get optimal prediction accuracy for each task. The sliding window is used to take the recent data to dynamically adjust the parameters of the MTL-DBN-DNN model. The MTL-DBN-DNN model is evaluated with a dataset from Microsoft Research. Comparison with multiple baseline models shows that the proposed MTL-DBN-DNN achieve state-of-art performance on air pollutant concentration forecasting.

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Evaluating Visibility Range on Air Pollution using NARX Neural Network

Journal of Environmental Treatment Techniques ◽

10.47277/jett/9(2)547 ◽

2021 ◽

Vol 9 (2) ◽

pp. 540-547

Keyword(s):

Neural Network ◽

Air Pollution ◽

Air Quality ◽

Air Pollutant ◽

Restriction Factors ◽

Network Efficiency ◽

Independent Variables ◽

Visibility Range ◽

Least Area ◽

Narx Neural Network

Evaluating air visibility range is considered as one of the apparent criteria of air quality. Haze air as a conclusion of air pollution causes unpleasant breathing, psychological effects, and visibility restriction. In this study, NARX neural network applied to determine air visibility restriction factors. Data of air quality control stations of Baghshomal, Rastebazar, and Abresan in Tabriz City, Iran used which include PM2.5, PM10, NO2, SO2, O3, and CO for the duration of four years from 2013 to 2017 that considered as independent variables. NARX neural network created to find each pollutant relation to visibility restriction and networks used for simulation to analysis network results in conspectuses condition. The results showed that PM10 pollutant has the most influence on-air visibility with R=0.9 in the train, R=0.728 in the test, and R=0.75 in validation process. Also error results of the PM10 obtained as MSE=0.054. Moreover, simulation results demonstrated the least area integral between curves according to ascending order for six pollutant factors and verified PM10 accuracy in NARX network simulation. The total result as study conclusion verified NARX neural network efficiency to evaluate air visibility range while using air pollutant parameters.

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Assessment of the Variability of Air Pollutant Concentrations at Industrial, Traffic and Urban Background Stations in Krakow (Poland) Using Statistical Methods

Sustainability ◽

10.3390/su13105623 ◽

2021 ◽

Vol 13 (10) ◽

pp. 5623

Author(s):

Robert Oleniacz ◽

Tomasz Gorzelnik

Keyword(s):

Air Quality ◽

Statistical Methods ◽

Fine Particulate Matter ◽

Measurement Data ◽

Road Transport ◽

Air Pollutant ◽

Rank Test ◽

Urban Background ◽

Pollutant Concentrations ◽

Dependent Samples

In cities with an extensive air quality monitoring (AQM) system, the results of pollutant concentration measurements obtained in this system can be used not only for current assessments of air pollution, but also for analyzes aimed at better identification of factors influencing the air quality and for tracking trends in changes taking place in this regard. This can be achieved with the use of statistical methods that allow for the assessment of the variability of measurement data observed at stations of various types and for the determination of possible interdependencies between these data. In this article, an analysis of this type was carried out for traffic, urban background and industrial AQM stations in Krakow (Southern Poland) operating in the years 2017–2018 with the use of, i.a., cluster analyzes, as well as dependent samples t-test and Wilcoxon signed-rank test, taking into account the concentrations of air pollutants such as fine particulate matter (PM10), nitrogen dioxide (NO2), benzene (C6H6) and sulfur dioxide (SO2). On the basis of the conducted analyzes, similarities and differences were shown between the data observed at individual types of stations, and the possibilities of using them to identify the causes of the observed changes and the effects of remedial actions to improve air quality undertaken recently and planned in the future were indicated. It was found that the air concentrations of some substances measured at these stations can be used to assess the emission abatement effects in road transport (NO2, PM10 or C6H6), residential heating (PM10 or SO2), and selective industrial plants (SO2, NO2 or C6H6).

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Nexus between green bonds, financial and environmental indicators

Economics and Business Letters ◽

10.17811/ebl.10.3.2021.191-199 ◽

2021 ◽

Vol 10 (3) ◽

pp. 191-199

Author(s):

Ngo Thai Hung

Keyword(s):

Neural Network ◽

Stock Market ◽

Granger Causality ◽

Multilayer Perceptron ◽

Co2 Emission ◽

Clean Energy ◽

Environmental Indicators ◽

Energy Price ◽

Causal Direction ◽

Bidirectional Relationship

This study uses a novel perspective to examine the causal connectedness between green bonds and other conventional assets, including clean energy, price of CO2 emission allowances, Bitcoin, and the S&P 500 stock market covering from January 2013 to March 2019. We apply the Multilayer Perceptron Neural Network Non-linear Granger causality and Transfer Entropy to detect possible changes in the causal direction between green bonds and other considered variables. We find a bidirectional relationship between green bonds, S&P 500, and Bitcoin markets, while green bonds have a unidirectional connection with the price of CO2 emission allowances.

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A NEURAL NETWORK NOISE PREDICTION MODEL FOR TEHRAN URBAN ROADS

Journal of Environmental Engineering and Landscape Management ◽

10.3846/16486897.2017.1356327 ◽

2018 ◽

Vol 26 (2) ◽

pp. 88-97 ◽

Cited By ~ 6

Author(s):

Ali Mansourkhaki ◽

Mohammadjavad Berangi ◽

Majid Haghiri ◽

Mohammadreza Haghani

Keyword(s):

Neural Network ◽

Regression Model ◽

Network Model ◽

Neural Network Model ◽

Goodness Of Fit ◽

Measurement Data ◽

Reflection Factor ◽

Road Traffic Noise ◽

The Neural Network ◽

Marquardt Algorithm

Over the last decades, the number of motor vehicles has increased dramatically in Iran, where different traffic characteristics and urban structures are notable. In the present study, a multilayer perceptron neural network model trained with the Levenberg-Marquardt algorithm was used for predicting the equivalent sound level (LAeq) originating from traffic. Fifty-one samples were collected from different areas of Tehran. Input parameters consisted of total traffic volume per hour, average speed of vehicles, percentage of each category of vehicles, road gradient, density of buildings around the road section and a new parameter named “Building Reflection Factor”. These data were randomly used with 80, 10 and 10 percentiles respectively for training, validation and testing of the Artificial Neural Network (ANN). Results yielded by the ANN model were compared with field measurement data, a proposed regression model and some classical well-known models. Our study indicated that the prediction error of the neural network model was much less than that of the regression model and other classical models. Moreover, a statistical t-test was applied for evaluating the goodness-of-fit of the proposed model and proved that the neural network model is highly efficient in estimating road traffic noise levels.

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Study of Trends in Concentrations of Basic Air Pollutants in the Malopolska Province

Ecological Chemistry and Engineering S ◽

10.2478/eces-2020-0035 ◽

2020 ◽

Vol 27 (4) ◽

pp. 567-578

Author(s):

Mariusz Filak ◽

Szymon Hoffman

Keyword(s):

Air Quality ◽

Environmental Protection ◽

Tropospheric Ozone ◽

Air Pollutants ◽

Linear Equations ◽

Measurement Data ◽

Automatic Monitoring ◽

Air Pollutant ◽

Long Term Measurement

Abstract The purpose of the paper was to analyse the trends observed at air monitoring stations in the Malopolska Province - one of the most polluted regions in Poland. The study was carried out on the basis of long-term measurement data registered at five selected stations of automatic monitoring of air quality in the Malopolska Province. Trends evaluation was made on the basis of mean annual concentrations, taken from the database of the Chief Inspectorate for Environmental Protection in Poland. Separately for each basic air pollutant, such as SO2, NO2, NOx, CO, PM10 and O3, trend lines and their linear equations were determined to illustrate the direction of changes in concentrations. The obtained equations of the trend lines indicate the threat to the environment in the Malopolska Province. Based on the results obtained it can be concluded that for recent years there has been observed the concentration decrease of main air pollutants, except of tropospheric ozone.

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