Keywords: sampling, data-driven models, flow predictions.

The inundation and flooding of a landscape is a serious problem which needs to be solved. This paper presents the application of a data-driven model for stream flow prediction, which can be one of the tools for the preventive protection of a population and its property. The authors have applied support vector machine methodology (SVMs) to the flow predictions in this paper. SVMs are gaining popularity as a result of various attractive features, which equip SVMs with a greater ability to generalize the main goal in data-driven modelling. From a practical point of view, perhaps the most serious problem with SVMs is their high algorithmic complexity and the extensive memory requirements of the quadratic programming required (which is part of solving a problem with an SVM model) for large-scale tasks. In this situation the pre-processing of the data, namely sampling methods are useful for reducing software-hardware requirements. The pre-processing methods investigated were applied to the Hron River watershed in Slovakia using hydrological and meteorological daily data of various variables, which are the predictants in SVM flow prediction models. Special sampling was used, bearing in mind the nuances of hydrological modelling. A compromise exists between the size of the training dataset and the degree of accuracy, when the degree of accuracy is not lowered and the computation time is significantly decreased. The authors demonstrate in the paper how such a compromise could be found. A model trained with a reduced dataset of 50% achieved the same degree of accuracy as the model with all the data, but with an execution time four times shorter.

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