Resumen
Estimating Manning?s roughness coefficient (??)
(
n
)
is one of the essential factors in predicting the discharge in a stream. Present research work is focused on prediction of Manning?s ??
n
in meandering compound channels by using the Group Method of Data Handling Neural Network (GMDH-NN) approach. The width ratio (??)
(
a
)
, relative depth (??)
(
ß
)
, sinuosity (??)
(
s
)
, Channel bed slope (????)
(
S
o
)
, and meander belt width ratio (??)
(
?
)
are specified as input parameters for the development of the model. The performance of GMDH-NN is evaluated with two different machine learning techniques, namely the support vector regression (SVR) and multivariate adaptive regression spline (MARS) with various statistical measures. Results indicate that the proposed GMDH-NN model predicts the Manning?s ??
n
satisfactorily as compared to the MARS and SVR model. This GMDH-NN approach can be useful for practical implementation as the prediction of Manning?s coefficient and subsequently discharge through Manning?s equation in the compound meandering channels are found to be quite adequate.