Evolving Compact Prediction Model for PM2.5 level of Chiang Mai Using Multiobjective Multigene Symbolic Regression

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@InProceedings{Unachak:2021:JCSSE,

• author = "Prakarn Unachak and Prayat Puangjaktha",
• title = "Evolving Compact Prediction Model for PM2.5 level of Chiang Mai Using Multiobjective Multigene Symbolic Regression",
• booktitle = "2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE)",
• year = "2021",
• abstract = "In recent years, fine particulate matter (PM2.5) has caused economic and health-related adversities to people of Northern Thailand. An accurate predictive model would allow residents to take precautions for their safeties. Also, a human-readable predictive model can lead to better understandings of the issues. In this paper, we use multigene symbolic regression, a genetic programming (GP) approach, to create predictive models for PM2.5 levels in the next 3 hours. This approach creates mathematical models consists of multiple simpler trees for equivalent expressiveness to conventional GP. We also used Non-dominated Sorting Genetic Algorithm-II (NSGA-II), a multiobjective optimization technique, to ensure accurate yet compact models. Using pollutants and meteorological data from Yupparaj Wittayalai monitoring station, combined with satellite-based fire hotspots data from Fire Information of Resource Management System (FIRMS), our approach has created compact human-readable models with better or comparable accuracies to benchmark approaches, as well as identifies possible nonlinear relationships in the dataset.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/JCSSE53117.2021.9493833",
• ISSN = "2642-6579",
• month = jun,
• notes = "Also known as \cite{9493833}",

}

Genetic Programming entries for Prakarn Unachak Prayat Puangjaktha

Citations