Effects of Model Structural Complexity and Data Pre-Processing on Artificial Neural Network (ANN) Forecast Performance for Hydrological Process Modelling
The choice of a particular
Artificial Neural Network (ANN) structure is a seemingly difficult task; worthy
of relevance is that there is no systematic way for establishing a suitable
architecture. In view of this, the study looked at the effects of ANN
structural complexity and data pre-processing regime on its forecast performance.
To address this aim, two ANN structural configurations: 1) Single-hidden
layer, and 2) Double-hidden layer feed-forward backpropagation network were employed. Results obtained revealed generally
that: a) ANN comprised of double hidden layers tends to be less robust and
converges with less accuracy than its single-hidden layer counterpart under
identical situations; b) for a univariate time series, phase-space
reconstruction using embedding dimension which is based on dynamical systems
theory is an effective way for determining the appropriate number of ANN input
neurons, and c) data pre-processing via the scaling approach excessively limits
the output range of the transfer function. In specific terms considering
extreme flow prediction capability on the basis of effective correlation:
Percent maximum and minimum correlation coefficient (Rmax% and Rmin%), on the average for one-day ahead forecast during
the training and validation phases respectively for the adopted network
structures: 8 7 5 (i.e., 8 input
nodes, 7 nodes in the hidden layer,
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