Modeling airborne indoor and outdoor particulate matter using genetic programming
作者： Rama Rao Karri , Mahmoud Mohammadyan , Mahboobeh Ghoochani , Reza Ali Mohammadpour , Yusri Yusup
DOI: 10.1016/J.SCS.2018.08.015
关键词: Atmospheric sciences 、 Empirical modelling 、 Particulates 、 University campus 、 Fitness measure 、 Environmental science 、 Genetic programming 、 Indoor air quality 、 Mean squared error
摘要: Abstract Airborne particulate matter (PM) is considered to be an essential indicator of outdoor and indoor air quality. In this study, PM1, PM2.5, PM10 concentrations were monitored at different locations within the Tehran University campus. It found that 10% PM2.5 higher than 36.11, 52.48 92.13 μg/m3 for indoors respectively. Genetic programming (GP) based methodology implemented identify influence PM on established significant empirical models. The best GP model identified fitness measure root mean square error. was observed models are perfectly able mimic behavioural trends concentrations. predictions very similar measured values their variation less ± 8%. This analysis confirms performance data driven modeling approach predict relationship between its concentration.
sci-hub.se 参考文章(39)
Vladan Babovic, Maarten Keijzer, Rainfall runoff modelling based on genetic programming Hydrology Research. ,vol. 33, pp. 331- 346 ,(2002) , 10.2166/NH.2002.0012
Marina Riga, Fani A. Tzima, Kostas Karatzas, Pericles A. Mitkas, Development and Evaluation of Data Mining Models for Air Quality Prediction in Athens, Greece Proceedings of the Fourth International ICSC Symposium on Information Technologies in Environmental Engineering. pp. 331- 344 ,(2009) , 10.1007/978-3-540-88351-7_25
Hiram Levy, Larry W. Horowitz, M. Daniel Schwarzkopf, Yi Ming, Jean-Christophe Golaz, Vaishali Naik, V. Ramaswamy, The roles of aerosol direct and indirect effects in past and future climate change Journal of Geophysical Research: Atmospheres. ,vol. 118, pp. 4521- 4532 ,(2013) , 10.1002/JGRD.50192
W. Gerald Teague, Charlene W. Bayer, Outdoor air pollution. Asthma and other concerns. Pediatric Clinics of North America. ,vol. 48, pp. 1167- 1183 ,(2001) , 10.1016/S0031-3955(05)70367-9
Daya Shankar Pandey, Indranil Pan, Saptarshi Das, James J. Leahy, Witold Kwapinski, Multi-gene genetic programming based predictive models for municipal solid waste gasification in a fluidized bed gasifier. Bioresource Technology. ,vol. 179, pp. 524- 533 ,(2015) , 10.1016/J.BIORTECH.2014.12.048
KAMBAN PARASURAMAN, AMIN ELSHORBAGY, SEAN K. CAREY, Modelling the dynamics of the evapotranspiration process using genetic programming Hydrological Sciences Journal-journal Des Sciences Hydrologiques. ,vol. 52, pp. 563- 578 ,(2007) , 10.1623/HYSJ.52.3.563
R. Fernández-Camacho, I. Brito Cabeza, J. Aroba, F. Gómez-Bravo, S. Rodríguez, J. de la Rosa, Assessment of ultrafine particles and noise measurements using fuzzy logic and data mining techniques Science of The Total Environment. ,vol. 512, pp. 103- 113 ,(2015) , 10.1016/J.SCITOTENV.2015.01.036
H. Orouji, O. Bozorg Haddad, E. Fallah-Mehdipour, M. A. Mariño, Modeling of Water Quality Parameters Using Data-Driven Models Journal of Environmental Engineering. ,vol. 139, pp. 947- 957 ,(2013) , 10.1061/(ASCE)EE.1943-7870.0000706
WILLIAM F MCDONNELL, NAOMI NISHINO-ISHIKAWA, FLOYD F PETERSEN, LIE HONG CHEN, DAVID E ABBEY, Relationships of mortality with the fine and coarse fractions of long-term ambient PM10 concentrations in nonsmokers. Journal of Exposure Science and Environmental Epidemiology. ,vol. 10, pp. 427- 436 ,(2000) , 10.1038/SJ.JEA.7500095