Modeling spatial distribution of flow depth in fluvial systems using a hybrid two-dimensional hydraulic-multigene genetic programming approach
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- @Article{YAN:2021:JH,
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author = "Xiaohui Yan and Abdolmajid Mohammadian and
Ali Khelifa",
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title = "Modeling spatial distribution of flow depth in fluvial
systems using a hybrid two-dimensional
hydraulic-multigene genetic programming approach",
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journal = "Journal of Hydrology",
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volume = "600",
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pages = "126517",
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year = "2021",
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ISSN = "0022-1694",
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DOI = "doi:10.1016/j.jhydrol.2021.126517",
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URL = "https://www.sciencedirect.com/science/article/pii/S0022169421005643",
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keywords = "genetic algorithms, genetic programming, Spatial
distribution, Flow depth, 2D hydraulic, Multigene
genetic programming, Ottawa River",
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abstract = "Modeling spatial distribution of flow depth in fluvial
systems is crucial for flow mitigation, river
rehabilitation, and design of water resources
infrastructure. Flow depth in fluvial systems can be
typically estimated using hydrological or physics-based
hydraulic models. However, hydrological models may not
be able to provide satisfactory predictions for
catchments with limited data because they normally
ignored the strict conservation of momentum.
Traditional fully physics-based hydraulic models are
often very computationally expensive, limiting their
wide usage in practical applications. In this study, a
novel method, based on a hybrid two-dimensional (2D)
hydraulic-multigene genetic programming (MGGP)
approach, is proposed and employed to model the spatial
distribution of flow depth in fluvial systems. A 2D
hydraulic model was constructed using the TELEMAC-2D
software and validated against field measurements. The
validated model was then assumed to reflect the real
physical processes and used to carry out additional
computations to obtain spatial distribution of flow
depth under different discharge scenarios, which
provided a sufficient synthetic dataset for training
machine learning models based on the MGGP technique.
The study area (a segment of the Ottawa River near the
island named Ile Kettle) was divided into 34
sub-regions to further reduce the computational costs
of the training processes and the complexity of the
evolved models. The numerical data were distributed to
the corresponding sub-regions, and an MGGP-based model
was trained for each sub-region. These models are
compact explicit arithmetic equations that can be
readily transferable and can immediately output the
flow depth at any point in the corresponding sub-region
as functions of the flow rate, longitudinal, and
transversal coordinates. The best MGGP model for each
sub-region amongst all the generated models was
identified using the Pareto optimization approach. The
results showed that the best MGGP models satisfactorily
reproduced the training data and predicted the testing
data (the root mean square errors were 0.303 m and
0.306 m, respectively), demonstrating the predictive
capability of the approach. A comparison between MGGP
and single-gene genetic programming (SGGP) approaches
and confidence analysis were also reported, which
demonstrated the good performance of the proposed
approach. Furthermore, it took about 53 min for the
hydraulic model to complete each simulation, but it
took only about 0.56 s using the final model; the total
size of the hydraulic output files for 12 different
sizes was 432, 948 KB, but the total size of the script
file for the final model was only about 46 KB.
Therefore, the present study found that the hybrid 2D
hydraulic-MGGP approach was satisfactorily accurate,
fast to run, and easy to use, and thus, it is a
promising tool for modeling spatial distribution of
flow depth in fluvial systems",
- }
Genetic Programming entries for
Xiaohui Yan
Abdolmajid Mohammadian
Ali Khelifa
Citations