Two-dimensional convolutional neural network outperforms other machine learning architectures for water depth surrogate modeling
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @Article{YAN:2023:jhydrol,
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author = "Xiaohui Yan and Abdolmajid Mohammadian and
Ruigui Ao and Jianwei Liu and Na Yang",
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title = "Two-dimensional convolutional neural network
outperforms other machine learning architectures for
water depth surrogate modeling",
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journal = "Journal of Hydrology",
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volume = "616",
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pages = "128812",
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year = "2023",
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ISSN = "0022-1694",
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DOI = "doi:10.1016/j.jhydrol.2022.128812",
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URL = "https://www.sciencedirect.com/science/article/pii/S0022169422013828",
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keywords = "genetic algorithms, genetic programming, Deep
learning, Convolutional neural network, Water depth,
Spatial distribution, Surrogate modeling",
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abstract = "Rapid prediction of spatially distributed hydrological
variables, such as water depths in rivers, is an
important but challenging task. This study proposes a
novel matrix-based deep learning approach for
predicting spatial distribution of water depths in
rivers. The proposed approach was constructed based on
a two-dimensional (2D) convolutional neural network
(CNN) with a new architecture that was specifically
designed for providing spatial distribution maps. A
numerical dataset was established based on a field
cruise and two-dimensional hydraulic modeling for
different scenarios, and numerical experiments were
designed to predict spatial distribution of water
depths for different scenarios using the adaptive
neuro-fuzzy inference system (ANFIS), support vector
machine (SVM), genetic programming (GP), multi-gene
genetic programming (MGGP), one-dimensional CNN
(1D-CNN), and the proposed CNN algorithms. The results
showed that the proposed CNN approach captured both the
large-scale and small-scale spatial patterns remarkably
well, and it outperformed the other approaches. This
study shows that the 2D CNN algorithm is better than
the classical machine learning (ML) algorithms for
inundation modeling. The proposed approach is thus a
promising tool for providing rapid predictions of
spatial distribution of water depths in river systems
and can potentially be leveraged to predict other
spatially distributed hydrological variables",
- }
Genetic Programming entries for
Xiaohui Yan
Abdolmajid Mohammadian
Ruigui Ao
Jianwei Liu
Na Yang
Citations