TITLE:
Effects of Model Structural Complexity and Data Pre-Processing on Artificial Neural Network (ANN) Forecast Performance for Hydrological Process Modelling
AUTHORS:
Martins Yusuf Otache, John Jiya Musa, Ibrahim Abayomi Kuti, Mustapha Mohammed, Lydia Ezekiel Pam
KEYWORDS:
Streamflow, Neural Network, Phase-Space, Optimisation, Algorithm
JOURNAL NAME:
Open Journal of Modern Hydrology,
Vol.11 No.1,
January
29,
2021
ABSTRACT: 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 back propagation 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, and 5 output nodes in the output layer), 8 5 2 5 (8 nodes in the input layer, 5 nodes in the first hidden layer, 2
nodes in the second hidden layer, and 5 nodes in the output layer), and 8
4 3 5 (8 nodes in the input layer, 4 nodes in the first hidden layer, 3 nodes
in the second hidden layer, and 5 nodes in the output layer) gave: 101.2, 99.4; 100.2, 218.3; 93.7, 95.0 in all
instances irrespective of the training algorithm (i.e., pooled). On the other hand, in terms of percent of correct
event prediction, the respective performances of the models for both low and
high flows during the training and validation phases, respectively were: 0.78,
0.96: 0.65, 0.87; 0.76, 0.93: 0.61, 0.83; and 0.79, 0.96: 0.65, 0.87.
Thus, it suffices to note that on the basis of coherence or regularity of
prediction consistency, the ANN model: 8 4 3 5 performed better. This
implies that though the adoption of large hidden layers vis-à-vis corresponding
large neuronal signatures could be counter-productive because of network
over-fitting, however, it may provide additional representational power. Based
on the findings, it is imperative to note that ANN model is by no means a
substitute for conceptual watershed modelling,
therefore, exogenous variables should be incorporated in streamflow modelling and forecasting exercise because of their
hydrologic evolutions.