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Assessment of a conceptual hydrological model and artificial neural networks for daily outflows forecasting |
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| Authors: | M Rezaeianzadeh A Stein H Tabari H Abghari N Jalalkamali E Z Hosseinipour V P Singh |
| Institution: | 1. School of Forestry and Wildlife Sciences, Auburn University, 602 Duncan Drive, Auburn, AL, 36849, USA 2. Faculty of Geo-Information Science and Earth Observation (ITC), Twente University, Enschede, The Netherlands 3. Department of Water Engineering, Ayatollah Amoli Branch, Islamic Azad University, Amol, Iran 4. Faculty of Natural Resources, Urmia University, Urmia, Iran 5. Department of Water Engineering, Kerman Branch, Islamic Azad University, Kerman, Iran 6. Advanced Planning Section, Ventura County Watershed Protection District, Ventura, CA, USA 7. Department of Biological and Agricultural Engineering, Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX, 77843-2117, USA |
| Abstract: | Artificial neural networks (ANNs) are used by hydrologists and engineers to forecast flows at the outlet of a watershed. They are employed in particular where hydrological data are limited. Despite these developments, practitioners still prefer conventional hydrological models. This study applied the standard conceptual HEC-HMS’s soil moisture accounting (SMA) algorithm and the multi layer perceptron (MLP) for forecasting daily outflows at the outlet of Khosrow Shirin watershed in Iran. The MLP optimized with the scaled conjugate gradient] used the logistic and tangent sigmoid activation functions resulting into 12 ANNs. The R 2 and RMSE values for the best trained MPLs using the tangent and logistic sigmoid transfer function were 0.87, 1.875 m3 s?1 and 0.81, 2.297 m3 s?1, respectively. The results showed that MLPs optimized with the tangent sigmoid predicted peak flows and annual flood volumes more accurately than the HEC-HMS model with the SMA algorithm, with R 2 and RMSE values equal to 0.87, 0.84 and 1.875 and 2.1 m3 s?1, respectively. Also, an MLP is easier to develop due to using a simple trial and error procedure. Practitioners of hydrologic modeling and flood flow forecasting may consider this study as an example of the capability of the ANN for real world flow forecasting. |
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