Prediction Model of Air Pollutant Levels Using Linear Model with Component Analysis

Syafei, Arie Dipareza; Fujiwara, Akimasa; Zhang, Junyi

doi:10.7763/ijesd.2015.v6.648

Cited by 18 publications

(5 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The typical processes of ML-like data preparation, data exploration, feature selection, training and testing samples, model evaluation, and improvement of the model were followed in this study. Several supervised classifiers: Stepwise regression, Quadratic Support Vector Machine (SVM), Rational quadratic Gaussian Process Regression (GPR), Ensemble Bagged Trees, Random Forest, Linear Regression, and Gradient Boosting were applied (Suárez Sánchez et al, 2011;Syafei et al, 2015;Choudhary et al, 2017;Kamińska, 2018). After executing these classifiers, the key results were summarized by comparing the Mean squared error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and goodness of fit (R 2 ).…”

Section: Machine Learning (Ml) For Predictionmentioning

confidence: 99%

Probable nexus between Methane and Air Pollution in Bangladesh using Machine Learning and Geographically Weighted Regression Modeling

Islam

Bhuiyan

2023

Jour. Hypers. Rem. Sensg.

View full text Add to dashboard Cite

This paper investigates the probable nexus between methane (CH4) and air pollutants, a public health hazard in Bangladesh. The hypothesis considers that the concentration of CH4 is dependent on the ten air pollutants found in the five districts in Dhaka Division, a major urban and industrial area in Bangladesh. These pollutants are: Particular matters (PM2.5), Nitrogen dioxide (NO2), Nitrogen oxide (NOx), Aerosol optical thickness (AOT), Sulfur dioxide (SO2), Carbon monoxide (CO), Ozone (O3), Black carbon (BC), Formaldehyde (HCHO) and Dust. The study applies Machine Learning (ML) technique and Geographically Weighted Regression (GWR) Modeling. Temporal CH4 datasets from the Sentinel-5P sensor are classified to estimate the annual CH4 concentration during 2019-2021.Seven supervised classifiers of ML coupled with the GWR model are used to predict the statistical and spatial relationships. CH4 increases gradually during 2018-2021 in Dhaka, Gazipur, and Munshiganj Districts. It relates differently with various air pollutants, e.g., positively with BC, Dust, NO2, PM2.5, O3, and AOT, and negatively with NOx, CO, HCHO, and SO2.This study results that Rational quadratic (RMSE-0.001, MAE-0.001, R2-0.96), Random Forest (RMSE-0.004, MAE-0.003, R2-0.91), and Stepwise regression (RMSE-0.002, MAE-0.002, R2-0.87) are the suitable method in ML. The highest goodness-of-fit (R2) of 82%-96% is found in Dhaka and Narshingdi Districts. The key findings may help formulate the appropriate action plan to mitigate ongoing and future air pollution in Bangladesh. In addition, the methodology of the research may be applicable elsewhere nationally and internationally for air pollution research.

show abstract

Section: Machine Learning (Ml) For Predictionmentioning

confidence: 99%

Probable nexus between Methane and Air Pollution in Bangladesh using Machine Learning and Geographically Weighted Regression Modeling

Islam

Bhuiyan

2023

Jour. Hypers. Rem. Sensg.

View full text Add to dashboard Cite

show abstract

“…Even though these methods have their own approach, the goal is similar is to build components that are statistically independent with each other. In regression analysis, this is particularly very useful and become good input as predictors in a regression model since they optimize spatial patterns and remove complexity due to multicollinearity (Syafei et al, 2015). Hence, the use of PLSR method for regression problems began in the early 80's.…”

Section: Partial Least Squares Regressionmentioning

confidence: 99%

The Comparison Of Partial Least Squares Regression, Principal Component Regression And Ridge Regression With Multiple Linear Regression For Predicting Pm10 Concentration Level Based On Meteorological Parameters

Polat¹,

Günay²

2021

Journal of Data Science

View full text Add to dashboard Cite

Air pollution shows itself as a serious problem in big cities in Turkey, especially for winter seasons. Particulate atmospheric pollution in urban areas is considered to have significant impact on human health. Therefore, the ability to make accurate predictions of particulate ambient concentrations is important to improve public awareness and air quality management. Ambient PM10 (i.e particulate diameter less than 10um in size) pollution has negative impacts on human health and it is influenced by meteorological conditions. In this study, partial least squares regression, principal component regression, ridge regression and multiple linear regression methods are compared in modeling and predicting daily mean PM10 concentrations on the base of various meteorological parameters obtained for the city of Ankara, in Turkey. The analysed period is February 2007. The results show that while multiple linear regression and ridge regression yield somewhat better results for fitting to this dataset, principal component regression and partial least squares regression are better than both of them in terms of prediction of PM10 values for future datasets. In addition, partial least squares regression is the remarkable method in terms of predictive ability as it has a close performance with principal component regression even with less number of factors.

show abstract

“…At first, the prediction model is built using empirical assumptions, such as those used in air pollution dispersion models. Predictions of air quality are made using machine learning techniques like linear regression [3] and ANNs [4]. But such methods are imperfect due to a wide variety of factors, including weather, pollutants, and traffic patterns.…”

Section: Introductionmentioning

confidence: 99%

Air Quality Prediction using Voronai-Based Spatial Temporal Sequence Similarity with Conjugate Gradient Enabled Sparse Autoencoder Deep Learning

P. Shree Nandhini

2023

IJRITCC

View full text Add to dashboard Cite

Air Quality Prediction (AQP) remains a difficult task because of multidimensional nonlinear spatiotemporal features. To solve this issue, an Improved Sparse Autoencoder with Deep Learning (ISAE-DL) and Enriched ISAE-DL (EISAE-DL) models were developed with the combination of concentric circle-based clustering, Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) followed by the ISAE for AQP. In EISAE-DL, concentric circle-based clustering uses Manhattan distance to efficiently split the locations into four regions using its center and cluster the spatially and temporally similar candidate locations. But it was considered a fixed structure and may struggle to find variations in several data points. Also, it accommodate clusters with regular and circular patterns, whereas irregular and non-circular cluster patterns were not handled. Similarly, the ANN inference was often offended or ignored because of complex meteorological characteristics. Hence, this paper proposes a Voronoi-based spatial-temporal sequence similarity with the Conjugate gradient-enabled SAE-DL (VCSAE-DL) model for effective AQP. First, a Voronoi clustering is performed by creating the Voronoi diagram for analogous candidate location clustering. Then, the resultant clusters of location data along with the PM2.5 and other meteorological data are given to the Improved ANN (IANN), and the target stations are given to the LSTM to capture the spatiotemporal relationship features and temporal features, respectively. Also, CNN is used to extract relationships between terrain and air quality features. These extracted features are fused in the merge layer and transferred to the ISAE for final prediction of air quality. Finally, the test outcomes demonstrate that the VCSAE-DL achieves better prediction performance compared to the existing AQP models.

show abstract

Prediction Model of Air Pollutant Levels Using Linear Model with Component Analysis

Cited by 18 publications

References 14 publications

Probable nexus between Methane and Air Pollution in Bangladesh using Machine Learning and Geographically Weighted Regression Modeling

Probable nexus between Methane and Air Pollution in Bangladesh using Machine Learning and Geographically Weighted Regression Modeling

The Comparison Of Partial Least Squares Regression, Principal Component Regression And Ridge Regression With Multiple Linear Regression For Predicting Pm10 Concentration Level Based On Meteorological Parameters

Air Quality Prediction using Voronai-Based Spatial Temporal Sequence Similarity with Conjugate Gradient Enabled Sparse Autoencoder Deep Learning

Contact Info

Product

Resources

About