Abstract
Metodologija za implementaciju hidrološkog modela otjecanja na malim slivovima
Highlights
Development of a hydrological prediction model requires a number of technologies and areas of expertise that normally include several elements, such as the long-term monitoring and collection of existing data, data analysis, and model development, validation and evaluation [1]
The model development is described in detail in the doctoral thesis [6], while only a short description and results are presented in this paper
The lower part contributes more significantly to the surface runoff compared to the upper part
Summary
Development of a hydrological prediction model requires a number of technologies and areas of expertise that normally include several elements, such as the long-term monitoring and collection of existing data, data analysis, and model development, validation and evaluation [1]. Hydrological prediction models are usually prepared for specific large catchments, and they cannot be used anywhere else. Such models cannot be applied in, for example, small catchments, where the solution-finding process and prediction time are more sensitive. The focus is placed on the development of an accurate model development methodology using parametric (data-driven) models, such as the artificial neural networks (ANN), in order to develop a hydrological rainfall-runoff prediction model for small catchments. A novel, simple and appealing solution to complex hydrological processes is derived form a large number of existing studies and ANN-based hydrological rainfall-runoff models. The lack of hydrological prediction model implementation in small catchments, and the absence of an accurate model development methodology, is established using the developed model
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