Effects of Model Structural Complexity and Data Pre-Processing on Artificial Neural Network (ANN) Forecast Performance for Hydrological Process Modelling

作     者：Martins Yusuf Otache John Jiya Musa Ibrahim Abayomi Kuti Mustapha Mohammed Lydia Ezekiel Pam Martins Yusuf Otache;John Jiya Musa;Ibrahim Abayomi Kuti;Mustapha Mohammed;Lydia Ezekiel Pam

作者机构：Department of Agricultural & Bioresources Engineering Federal University of Technology Minna Nigeria Raw Materials Research and Development Council Abuja Nigeria 

出 版 物：《Open Journal of Modern Hydrology》 (现代水文学期刊（英文）)

年 卷 期：2021年第11卷第1期

页      面：1-18页

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

主　　题：Streamflow Neural Network Phase-Space Optimisation Algorithm 

摘      要：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 Rspan