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EN
The overall objective of the ongoing work is to develop the computational environment HY DRO-PATH as a flexible tool for forecasting runoff from catchment areas for various hydrometeorological conditions while taking into account the information available on a real-time basis. Ensuring the model’s operational reliability and reducing the uncertainty of generated forecasts is accomplished through the adjustment of both the internal structure of the model and the spatial representation of the computational grid to the physiographical, hydrological and climatological characteristics of a given basin. The research focused on the development of methods for selecting the optimal model structure and parameters by analysing the results obtained for different model structures. This is achieved through the computational environment, in which it is possible to implement different types of hydrological rainfall-runoff models. These models have a unified system of data input, parameter optimisation rules, and procedures for result generation. The developed elements of the computational environment correspond to generation potential of models with a given structure and complexity. Furthermore, within the framework of HY DRO-PATH the following components were developed: an application programming interface (API), a data assimilation module, a module for computational representation of a real object, and a module for the estimation and optimisation of model parameters. The developed computational environment was applied to prepare a version of TOPO-Flex and perform hydrological validation of the model’s results. The hydrological validation was performed for selected flood events in the Bystrzyca Dusznicka subbasin of the Nysa Kłodzka River.
EN
There is always a question as to how sound are assessments of evolutionary algorithms capabilities based on the tests made in the specific adaptive landscapes. We present some results of numerical experiments concerning the ability of phenotypic evolution to cross saddles between adaptive peaks of different shapes. Asexual reproduction, proportional selection and normally distributed mutations are used. Some related comments on the benchmarking of evolutionary algorithms are also given.
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