Estimation of discharge in rivers by different artificial neural network algorithms: case of the Algerian Coastal basin
River discharge estimation is fundamental for a large number of engineering applications. The rating curve of a hydrometric station, which permits the establishment of a relationship between water level h and flow rate Q at a given cross-section, is the methodology most frequently used for continuous river flow measurements. To properly develop rating curves, discharges must be measured at all representative stages, using at least 10 to 12 points covering the range of low to high flows (Kennedy 2001). In the Algerian rivers, the bed is unstable, it is constantly in motion and characterized by a strong rate of sediments transported (Salhi et al. 2013), so the stage–discharge relationship is re-callipered more than four times per year (ANRH, 2009). Whenever, the river crosssection changes, the old data and rating curve become useless and discharge calculations must wait until enough new data are collected to establish a new empirical rating. Consequently, long series of levels are frequently available without the corresponding discharge values. During the transition period when the change is occurring, the discharge is calculated from field measurements of velocity and cross-section gauging (WMO 2008). These require much time and effort and are usually not done in flood conditions because of the dangers and the difficulty in activating the measurement team in due time, which is very frequent in Algerian rivers. In these cases, water level–runoff models can be used as an alternative solution. This study compared three artificial neural networks (ANN) algorithms to calibrate a water level–runoff model from two hydrometrics stations in the Algerian Coastal basin. These algorithms, the Levenberg-Marquard (ANN_LM), scaled conjugate gradient (ANN_SCG), and resilient back-propagation (ANN_RP), were applied to the tangent sigmoid transfer function. The input vector consisted of level [H(t)], and antecedents levels [H(t−1), H(t−2) and H(t−3)]. The algorithms were trained and validated by cross-validation using the limnigrammes provided and the realized gauging.
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Estimation of discharge in rivers by different artificial neural network algorithms: case of the Algerian Coastal basin
Semantic Scholar · Environmental Science · 2015
Abstract
River discharge estimation is fundamental for a large number of engineering applications. The rating curve of a hydrometric station, which permits the establishment of a relationship between water level h and flow rate Q at a given cross-section, is the methodology most frequently used for continuous river flow measurements. To properly develop rating curves, discharges must be measured at all representative stages, using at least 10 to 12 points covering the range of low to high flows (Kennedy 2001). In the Algerian rivers, the bed is unstable, it is constantly in motion and characterized by a strong rate of sediments transported (Salhi et al. 2013), so the stage–discharge relationship is re-callipered more than four times per year (ANRH, 2009). Whenever, the river crosssection changes, the old data and rating curve become useless and discharge calculations must wait until enough new data are collected to establish a new empirical rating. Consequently, long series of levels are frequently available without the corresponding discharge values. During the transition period when the change is occurring, the discharge is calculated from field measurements of velocity and cross-section gauging (WMO 2008). These require much time and effort and are usually not done in flood conditions because of the dangers and the difficulty in activating the measurement team in due time, which is very frequent in Algerian rivers. In these cases, water level–runoff models can be used as an alternative solution. This study compared three artificial neural networks (ANN) algorithms to calibrate a water level–runoff model from two hydrometrics stations in the Algerian Coastal basin. These algorithms, the Levenberg-Marquard (ANN_LM), scaled conjugate gradient (ANN_SCG), and resilient back-propagation (ANN_RP), were applied to the tangent sigmoid transfer function. The input vector consisted of level [H(t)], and antecedents levels [H(t−1), H(t−2) and H(t−3)]. The algorithms were trained and validated by cross-validation using the limnigrammes provided and the realized gauging.