Knowledge of hydrological processes and water balance elements are important for climate adaptive water management as well as for introducing mitigation measures aiming to improve surface water quality. Mathematical models have the potential to estimate changes in hydrological processes under changing climatic or land use conditions. These models, indeed, need careful calibration and testing before being applied in decision making. The aim of this study was to compare the capability of five different hydrological models to predict the runoff and the soil water balance elements of a small catchment in Norway. The models were harmonised and calibrated against the same data set. In overall, a good agreement between the measured and simulated runoff was obtained for the different models when integrating the results over a week or longer periods. Model simulations indicate that forest appears to be very important for the water balance in the catchment, and that there is a lack of information on land use specific water balance elements. We concluded that joint application of hydrological models serves as a good background for ensemble modelling of water transport processes within a catchment and can highlight the uncertainty of models forecast.
Multi-agent system is a system of autonomous, intelligent but resource-bounded agents. Particular agents have to be able to make decisions on their own, based on the activities of other agents and events within the system as well as in its environment. To this end agents make use of their own internal knowledge base which serves them as a memory. In this paper we focus on the design and management of such a knowledge base. After a brief description of some classical fundamental approaches to the knowledge base management, we propose an improvement based on the application of statistical methods. We focus in particular on the optimization of the process., Multi-agent systém je systém autonomních, inteligentních, ale zdrojově omezených agentů. Konkrétní agenti musí být schopni sami rozhodovat na základě činností jiných agentů a událostí v systému i v jeho prostředí. K tomuto účelu agenti využívají vlastní interní znalostní základnu, která jim slouží jako paměť. V tomto příspěvku se zaměřujeme na návrh a správu takové znalostní báze. Po stručném popisu některých klasických základních přístupů k řízení znalostní báze navrhujeme zlepšení založené na aplikaci statistických metod. Zaměřujeme se především na optimalizaci procesu., and Michal Košinár ; Ondřej Kohut