An efficient correlation based adaptive LASSO regression method for air quality index prediction

Sethi, Jasleen Kaur; Mittal, Mamta

doi:10.1007/s12145-021-00618-1

Cited by 30 publications

(11 citation statements)

References 29 publications

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“…The results of the simulations were operated by Python scripts. We compared four different simulation models, including Random Forest (a supervised and powerful machine learning algorithm based on decision trees, which can be used for both classification and regression) （ Gariazzo et al, 2020 ）, K-nearest Neighbors (the most widely used algorithm for both classification and regression based on the idea that similar things are near together) ( Aini and Mustafa, 2020 ), Linear Regression (the mostly used algorithm to find the relationships between dependent and independent variables) ( Yuchi et al, 2019 ), and Lasso Regression (improved from linear regression using the regularization way to reduce overfit) ( Sethi and Mittal, 2021 ). The dependent variables (air pollutants) and independent variables (meteorological indicators, traffic data, holidays, etc.)…”

Section: Methodsmentioning

confidence: 99%

Investigation of COVID-19-related lockdowns on the air pollution changes in augsburg in 2020, Germany

Cao

Liu

Hadiatullah

et al. 2022

Atmospheric Pollution Research

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Section: Methodsmentioning

confidence: 99%

Investigation of COVID-19-related lockdowns on the air pollution changes in augsburg in 2020, Germany

Cao

Liu

Hadiatullah

et al. 2022

Atmospheric Pollution Research

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“…With the increase of , the LASSO can compress the coefficients of unimportant variables to 0, thus realizing variable selection. The larger the value of k, the more parameters are compressed to 0, and the smaller the model complexity, which solves the problem of poor model interpretability 14 , 35 , 36 . …”

Section: Establishment Of Sensor Calibration Modelmentioning

confidence: 99%

“…For the multicollinearity problem that may exist in the construction of multiple regression model, least absolute selection and shrinkage operator (LASSO) regression is one of the methods often used to solve it. Sethi et al proposed an adaptive LASSO regression method based on correlation, successfully identified the important factors affecting the air quality index, and completed the forecast of air quality in Delhi 14 . It is difficult for multiple linear regression models to detect the complex and potentially non-linear relationship between predictor variables and response variables, so machine learning algorithms such as artificial neural networks 15 – 18 , support vector machines 19 – 22 , random forest 23 – 26 and extreme gradient boosting 27 – 29 have become the mainstream of pollutant concentration prediction.…”

Section: Introductionmentioning

confidence: 99%

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

Liu

Jin

et al. 2021

Sci Rep

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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. 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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“…PM 2.5, PM 10 and O3 attributes present in the data set will be used in computation of Air Quality. The authors of [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] have compared the machine learning algorithms for predicting the Air Quality in different areas and stated that neural network algorithms were superior to other machine learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Air Quality Index (AQI) using Neural Approach

Singh

Chaturvedi

2023

Preprint

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Air pollution has become a serious environmental issue. It is responsible for hundreds of fatalities each year and it poses a serious threat to human health and environment. It leads to global warming, greenhouse effect and it also causes respiratory problems like asthma, lung cancer etc. It is important to predict the quality of the air to regulate air pollution. In this paper, the neural approach is used in predicting the Air quality.

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An efficient correlation based adaptive LASSO regression method for air quality index prediction

Cited by 30 publications

References 29 publications

Investigation of COVID-19-related lockdowns on the air pollution changes in augsburg in 2020, Germany

Investigation of COVID-19-related lockdowns on the air pollution changes in augsburg in 2020, Germany

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

Prediction of Air Quality Index (AQI) using Neural Approach

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