- author = "Mostafa Cham and Olivia Zhang and Haotong Jing and Weikang Qian and Yixin Wen and Jianwu Wang",
- title = "{NS-QPE}: A Neuro-Symbolic Approach Towards Accurate and Interpretable Quantitative Precipitation Estimation Using Polarimetric Radar Data",
- year = "2025",
- booktitle = "2025 International Conference on Advanced Machine Learning and Data Science (AMLDS)",
- pages = "784--792",
- address = "Tokyo, Japan",
- month = "19-21 " # jul,
- publisher = "IEEE",
- keywords = "genetic algorithms, genetic programming, Training, Meteorological radar, Accuracy, Sensitivity, Estimation, Predictive models, Mathematical models, Radar polarimetry, Data models, Calibration, Neuro-Symbolic AI, Hybrid Modeling, Neural Networks, Symbolic regression, Model Interpretability, Meteorological Forecasting",
- isbn13 = "979-8-3315-2178-3",
-
DOI = "
10.1109/AMLDS63918.2025.11159351",
-
code_url = "
https://github.com/big-data-lab-umbc/NS-QPE",
- abstract = "...the hybrid model not only maintains RMSE scores comparable to the purely neural network model but also enhances interpretability through the integration of symbolic knowledge ...",
-
notes = "Also known as \cite{11159351} Is this GP ?
Department of Information Systems, University of Maryland, Baltimore, USA",
