Modeling airborne indoor and outdoor particulate matter using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@Article{KARRI:2018:SCS,

• author = "Rama Rao Karri and Behzad Heibati and Yusri Yusup and Mohd Rafatullah and Mahmoud Mohammadyan and J. N. Sahu",
• title = "Modeling airborne indoor and outdoor particulate matter using genetic programming",
• journal = "Sustainable Cities and Society",
• volume = "43",
• pages = "395--405",
• year = "2018",
• keywords = "genetic algorithms, genetic programming, Air quality, Airborne particles, Particulate matter, Modeling",
• ISSN = "2210-6707",
• DOI = "doi:10.1016/j.scs.2018.08.015",
• URL = "http://www.sciencedirect.com/science/article/pii/S2210670718301331",
• abstract = "Airborne particulate matter (PM) is considered to be an essential indicator of outdoor and indoor air quality. In this study, indoor and outdoor PM1, PM2.5, PM10 concentrations were monitored at different locations within the Tehran University campus. It is found that 10percent of PM1, PM2.5 and PM10 concentrations were higher than 36.11, 52.48 and 92.13ag/m3 for indoors respectively. Genetic programming (GP) based methodology is implemented to identify the influence of outdoor PM on the indoor PM and established significant empirical models. The best GP model is identified based on fitness measure and root mean square error. It was observed that the GP based models are perfectly able to mimic the behavioural trends of outdoor particulate matter for PM1, PM2.5 and PM10 concentrations. The model predictions are very similar to the measured values and their variation was less than pm 8percent. This analysis confirms the performance of GP based data driven modeling approach to predict the relationship between the outdoor particulate matter and its influence on the indoor particulate matter concentration",

}

Genetic Programming entries for Rama Rao Karri Behzad Heibati Yusri Yusup Mohd Rafatullah Mahmoud Mohammadyan J N Sahu

Citations