Accurate and Interpretable Radar Quantitative Precipitation Estimation with Symbolic Regression
Created by W.Langdon from
gp-bibliography.bib Revision:1.8344
- @InProceedings{Zhang:2024:BigData,
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author = "Olivia Zhang and Brianna Grissom and Julian Pulido and
Kenia Munoz-Ordaz and Jonathan He and Mostafa Cham and
Haotong Jing and Weikang Qian and Yixin Wen and
Jianwu Wang",
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title = "Accurate and Interpretable Radar Quantitative
Precipitation Estimation with Symbolic Regression",
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booktitle = "2024 IEEE International Conference on Big Data",
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year = "2024",
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pages = "2254--2263",
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month = dec,
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keywords = "genetic algorithms, genetic programming, Accuracy,
Rain, Knowledge based systems, Estimation, Radar,
Mathematical models, Floods, Water resources,
Monitoring, quantitative precipitation estimation,
polarimetric radar, symbolic regression,
knowledge-based loss terms",
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ISSN = "2573-2978",
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DOI = "
doi:10.1109/BigData62323.2024.10825069",
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abstract = "Accurate quantitative precipitation estimation (QPE)
is essential for managing water resources, monitoring
flash floods, creating hydrological models, and more.
Traditional methods of obtaining precipitation data
from rain gauges and radars have limitations such as
sparse coverage and inaccurate estimates for different
precipitation types and intensities. Symbolic
regression, a machine learning method that generates
mathematical equations fitting the data, presents a
unique approach to estimating precipitation that is
both accurate and interpretable. Using WSR-88D
dual-polarimetric radar data from Oklahoma and Florida
over three dates, we tested symbolic regression models
involving genetic programming and deep learning,
symbolic regression on separate clusters of the data,
and the incorporation of knowledge-based loss terms
into the loss function. We found that symbolic
regression is both accurate in estimating rainfall and
interpretable through learnt equations. Accuracy and
simplicity of the learnt equations can be slightly
improved by clustering the data based on select radar
variables and by adjusting the loss function with
knowledge-based loss terms. This research provides
insights into improving QPE accuracy through
interpretable symbolic regression methods.",
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notes = "Also known as \cite{10825069}",
- }
Genetic Programming entries for
Olivia Zhang
Brianna Grissom
Julian Pulido
Kenia Munoz-Ordaz
Jonathan He
Mostafa Cham
Haotong Jing
Weikang Qian
Berry Wen
Jianwu Wang
Citations