Abstract
In this study, potential of Least Square-Support Vector Regression (LS-SVR) approach is utilized to model the daily variation of river flow. Inherent complexity, unavailability of reasonably long data set and heterogeneous catchment response are the couple of issues that hinder the generalization of relationship between previous and forthcoming river flow magnitudes. The problem complexity may get enhanced with the influence of upstream dam releases. These issues are investigated by exploiting the capability of LS-SVR–an approach that considers Structural Risk Minimization (SRM) against the Empirical Risk Minimization (ERM)–used by other learning approaches, such as, Artificial Neural Network (ANN). This study is conducted in upper Narmada river basin in India having Bargi dam in its catchment, constructed in 1989. The river gauging station–Sandia is located few hundred kilometer downstream of Bargi dam. The model development is carried out with pre-construction flow regime and its performance is checked for both pre- and post-construction of the dam for any perceivable difference. It is found that the performances are similar for both the flow regimes, which indicates that the releases from the dam at daily scale for this gauging site may be ignored. In order to investigate the temporal horizon over which the prediction performance may be relied upon, a multistep-ahead prediction is carried out and the model performance is found to be reasonably good up to 5-day-ahead predictions though the performance is decreasing with the increase in lead-time. Skills of both LS-SVR and ANN are reported and it is found that the former performs better than the latter for all the lead-times in general, and shorter lead times in particular.
Highlights
River flow is an important component in hydrological cycle, which is directly available to the community
The results presented Support Vector Machine (SVM) as a compelling alternative to traditional Artificial Neural Networks (ANN) to conduct climate impact studies [10,11] downscaled monthly precipitation to basin scale using SVMs and reported the results to be encouraging in their accuracy while showing large promise for further applications
As discussed in case of Least Square-Support Vector Regression (LS-SVR) earlier, the peak river flow values are not captured with reasonable accuracy in case of ANN as well
Summary
River flow is an important component in hydrological cycle, which is directly available to the community. River flow plays an important role in establishing some of the critical interactions that occur between physical, ecological, social or economic processes. Accurate or at least reasonably reliable prediction of river flow is an important foundation for preventing flood, reducing natural disasters, and the optimum management of water resource. Constructions of major and minor dams are essential in order to effective use of available water resources. This is more crucial for the monsoon dominated countries, where most of the annual rainfall occurs during couple of months, and rest of the months are mostly no-rainfall months. Existence of dams adds to the complexity in the river flow modelling, which is influenced by the releases from upstream reservoirs as well as the influence of the inputs from the catchment area between immediate downstream of the dam and the gauging site
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