A Genetic Programming Based Pollutant Concentration Predictor Design for Urban Pollution Monitoring Based on Multi-Sensor Electronic Nose

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

@InProceedings{Ari:2021:ICIT,

• author = "Davut Ari and Baris Baykant Alagoz",
• title = "A Genetic Programming Based Pollutant Concentration Predictor Design for Urban Pollution Monitoring Based on Multi-Sensor Electronic Nose",
• booktitle = "2021 International Conference on Information Technology (ICIT)",
• year = "2021",
• pages = "168--172",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/ICIT52682.2021.9491122",
• month = jul,
• abstract = "An important part of air pollution control is the pollution monitoring. Since industrial spectrometers are expensive equipment, the number of observation points to monitor air pollution over an urban area can be limited. The low-cost multi-sensors network can spread over areas and form a wide-area electronic nose to estimate pollutant concentration distributions. However, the collected multisensor data should be analysed to correctly estimate pollutant concentrations. This study demonstrates implementation of genetic programming (GP) to obtain prediction models that can estimate CO and NO2 concentrations from multisensor electronic nose data. For this purpose, to function as an electronic nose, a regression model from a training data set is obtained by using a tree-based GP algorithm. In order to improve performance of the GP based prediction models, data normalization is performed and prediction performance enhancements are demonstrated via statistical performance analyses on a test data set.",
• notes = "Also known as \cite{9491122}",

}

Genetic Programming entries for Davut Ari Baris Baykant Alagoz

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