%0 Journal Article %T Effects of Model Structural Complexity and Data Pre-Processing on Artificial Neural Network (ANN) Forecast Performance for Hydrological Process Modelling %A Martins Yusuf Otache %A John Jiya Musa %A Ibrahim Abayomi Kuti %A Mustapha Mohammed %A Lydia Ezekiel Pam %J Open Journal of Modern Hydrology %P 1-18 %@ 2163-0496 %D 2021 %I Scientific Research Publishing %R 10.4236/ojmh.2021.111001 %X 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 (R_max% and R_min%), 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, %K Streamflow %K Neural Network %K Phase-Space %K Optimisation %K Algorithm %U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=108006