Incorporating a machine learning technique to improve open-channel flow computations

 The objective of this study is to employ support
vector machine as a machine learning technique to improve
flow discharge predictions in compound open channels.
Accurate estimation of channel conveyance is a major step
in prediction of the flow discharge in open-channel flow
computations (e.g., river flood simulations, design of
canals, and water surface profile computation). Common
methods to estimate the conveyance are highly simplified
and are a main source of uncertainty in compound channels,
since popular river/canal models still incorporate 1-D
hydrodynamic formulations. Further, the reliability of
using a specific method (e.g., vertical divided channel
method, the coherence method) over other methods for
different applications involving various geometric and
hydraulic conditions is questionable. Using available
experimental and field data, a novel method was developed,
based on SVM, to compute channel conveyance. The
data included 394 flow rating curves from 30 different
laboratory and natural compound channel sections which
were used for the training, and verification of the SVM
method. The data were limited to straight compound
channels. The performance of SVM was compared with
those from other commonly used methods, such as the
vertical divided channel method, the coherence method and
the Shiono and Knight model. Additionally, SVM estimations
were compared with available data for River Main
and River Severn, UK. Results indicated that SVM outperforms
traditional methods for both laboratory and field
data. It is concluded that the proposed SVM approach
could be applied as a reliable technique for the prediction
of flow discharge in straight compound channels. The
proposed SVM can be potentially incorporated into 1-D
river hydrodynamic models in future studies