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,

• 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",
• title = "Accurate and Interpretable Radar Quantitative Precipitation Estimation with Symbolic Regression",
• booktitle = "2024 IEEE International Conference on Big Data",
• year = "2024",
• pages = "2254--2263",
• month = dec,
• 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",
• ISSN = "2573-2978",
• DOI = "doi:10.1109/BigData62323.2024.10825069",
• 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.",
• 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