This paper is published in Volume-5, Issue-5, 2019
Area
Environmental Engineering
Author
Satya Sundar Patra
Org/Univ
Indian Institutes of Technology, Chennai, Tamil Nadu, India
Pub. Date
27 September, 2019
Paper ID
V5I5-1185
Publisher
Keywords
Indoor PM2.5, PM2.5 Prediction, Support Vector Regression (SVR), Low-cost sensor

Citationsacebook

IEEE
Satya Sundar Patra. Prediction of Indoor PM2.5 concentrations using Support Vector Regression, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Satya Sundar Patra (2019). Prediction of Indoor PM2.5 concentrations using Support Vector Regression. International Journal of Advance Research, Ideas and Innovations in Technology, 5(5) www.IJARIIT.com.

MLA
Satya Sundar Patra. "Prediction of Indoor PM2.5 concentrations using Support Vector Regression." International Journal of Advance Research, Ideas and Innovations in Technology 5.5 (2019). www.IJARIIT.com.

Abstract

Studies have found that exposure to elevated levels of PM2.5 concentrations, both in the long term and short term, has adverse health effects. The risk of exposure in the indoor microenvironment is higher compared to that of outdoor because people spend a significant amount of their time indoors. Therefore to mitigate the health risks, it becomes essential to maintain indoor PM2.5 levels. To do so, keeping an eye on the future expected concentrations is equally important as monitoring the current PM2.5 levels. This paper talks about the development of one such model that enables us to predict the indoor PM2.5 concentrations using Support Vector Regression. Upon development the model showed a very high value of R-square (0.98) and while testing it yielded a very low error (3.78%).
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