Prediction of PM10 pollution using principal component regression and hybrid artificial neural network model
รหัสดีโอไอ
Creator Sateesh N Hosamane
Title Prediction of PM10 pollution using principal component regression and hybrid artificial neural network model
Publisher Research and Development Office, Prince of Songkla University
Publication Year 2022
Journal Title Songklanakarin Journal of Science an Technology (SJST)
Journal Vol. 44
Journal No. 5
Page no. 1256-1263
Keyword air pollution, PM10, principal component, neural network, prediction
URL Website https://sjst.psu.ac.th/
ISSN 0125-3395
Abstract Air pollution, especially particulate matter (PM) pollution, has a significant impact on India. PM pollution is due toroadside dust, fossil fuel use, vehicular population, and industrial emissions. PM10 forecasting model development is essentialbecause it permits the experts and the citizens to take appropriate actions to restrict their exposure and execute protectivemeasures to improve air quality. This study aimed to develop a specialized computational intelligence methodology that usesprincipal component (PC) based artificial neural networks (ANN). The model was used to forecast PM10 in ambient air usingmeteorological data. This application is demonstrated for monitoring data from the urban area of Belagavi city of Karnatakastate, India. Principal component analysis (PCA) was applied to understand the interactions between PM10 concentration andmeteorological data. The analysis found that the PCANN model is better than the principal component regression (PCR) model,based on using various performance indexes (MAE, MSE, MAPE, RMSE, R, and R2). The PM10 predictive model performancewas satisfactory, with a MAPE of 0.069. The overall predictive capability of PM10 was 89.59% in terms of R.
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