Knowledge-Based Dynamic Systems Modeling: A Case Study on Modeling River Water Quality

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

@InProceedings{Park:2021:ICDE,

• author = "Namyong Park and MinHyeok Kim and Nguyen Xuan Hoai and R. I. {Bob McKay} and Dong-Kyun Kim",
• title = "Knowledge-Based Dynamic Systems Modeling: A Case Study on Modeling River Water Quality",
• booktitle = "2021 IEEE 37th International Conference on Data Engineering (ICDE)",
• year = "2021",
• pages = "2231--2236",
• abstract = "Modeling real-world phenomena is a focus of many science and engineering efforts, from ecological modeling to financial forecasting. Building an accurate model for complex and dynamic systems improves understanding of underlying processes and leads to resource efficiency. Knowledge-driven modeling builds a model based on human expertise, yet is often suboptimal. At the opposite extreme, data-driven modeling learns a model directly from data, requiring extensive data and potentially generating overfitting. We focus on an intermediate approach, model revision, in which prior knowledge and data are combined to achieve the best of both worlds. We propose a genetic model revision framework based on tree-adjoining grammar (TAG) guided genetic programming (GP), using the TAG formalism and GP operators in an effective mechanism making data-driven revisions while incorporating prior knowledge. Our framework is designed to address the high computational cost of evolutionary modeling of complex systems. Via a case study on the challenging problem of river water quality modeling, we show that the framework efficiently learns an interpretable model, with higher modeling accuracy than existing methods.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/ICDE51399.2021.00229",
• ISSN = "2375-026X",
• month = apr,
• notes = "Also known as \cite{9458939}",

}

Genetic Programming entries for Namyong Park MinHyeok Kim Nguyen Xuan Hoai R I (Bob) McKay Dong-Kyun Kim

Citations