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Application of Parametric-Based Framework for Regionalisation of Flow Duration Curves

Journal of Geoscience and Environment Protection 2016年第5期4卷 89-99页

作者： Martins Yusuf Otache Muhammad Abdullahi Tyabo Iyanda Murtala Animashaun lydia pam ezekiel Department of Agricultural & Bioresources Engineering Federal University of Technology Minna Nigeria Department of Technical Education Niger State College of Education Minna Nigeria

It is common knowledge that the end user of stream flow data may necessarily not have any prior knowledge of the quality control measures applied in their generation, therefore, conclusions drawn most often times may not be effective as desired. Thus, this study is an attempt at providing an independent quality construct to boost the confidence in the use of stream flow data by developing regional flow duration curves for selected ungauged stations of the upper Niger River Basin, Nigeria. Toward this end, stream flow data for seven gauging stations cover some sub basins in the Basin were obtained;precisely, monthly stream flow data covering a range of eleven to fifty-three years period. The flow duration curves from the gauging stations were fitted with three probability distribution models;i.e., logarithmic, power and exponential regression models. For the regionalisation, parameterisation was carried out in terms of the drainage area alone to allow for simplicity of models. Results obtained showed that the exponential regression model, in terms of Coefficient of Determination (R2) had the best fit. Though the regionalised model was simple, measurable agreement was obtained during the calibration and validation phases. However, considering the length of data used and probable variability in the stream flow regime, it is not possible to objectively generalise on the quality of the results. Against this backdrop, it suffices to take into cognisance the need to use an ensemble of catchment characteristics in the development of the flow duration curves and the overall regional models;this is important considering the implications of anthropogenic activities and hydro-climatic variations.

关键词： Regionalisation Parametric Models Anthropogenic Hydro-Climatic Variations

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

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Open Journal of Modern Hydrology 2021年第1期11卷 1-18页

作者： Martins Yusuf Otache John Jiya Musa Ibrahim Abayomi Kuti Mustapha Mohammed lydia ezekiel pam Department of Agricultural & Bioresources Engineering Federal University of Technology Minna Nigeria Raw Materials Research and Development Council Abuja Nigeria

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 R

关键词： Streamflow Neural Network Phase-Space Optimisation Algorithm

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