To contemplate quantitative and qualitative water features by neural networks method

M. Neruda and R. Neruda
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M. Neruda: Faculty of Environmental Studies, University J.E. Purkynì in Ústí nad Labem, Czech Republic 2 Institute of Computer Science, Academy of Sciences of the Czech Republic, Prague, Czech Republic
R. Neruda: Faculty of Environmental Studies, University J.E. Purkynì in Ústí nad Labem, Czech Republic 2 Institute of Computer Science, Academy of Sciences of the Czech Republic, Prague, Czech Republic

Plant, Soil and Environment, 2002, vol. 48, issue 7, 322-326

Abstract: An application deals with calibration of neural model and Fourier series model for Ploučnice catchment. This approach has an advantage, that the network choice is independent of other example's parameters. Each networks, and their variants (different units and hidden layer number) can be connected in as a black box and tested independently. A Stuttgart neural simulator SNNS and a multiagent hybrid system Bang2 developed in Institute of Computer Science, AS CR have been used for testing. A perceptron network has been constructed, which was trained by back propagation method improved with a momentum term. The network is capable of an accurate forecast of the next day runoff based on the runoff and rainfall values from previous day.

Keywords: rainfall-runoff models; Ploučnice river catchment; applications of artificial neural networks; water quality (search for similar items in EconPapers)
Date: 2002
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Persistent link: https://EconPapers.repec.org/RePEc:caa:jnlpse:v:48:y:2002:i:7:id:4375-pse

DOI: 10.17221/4375-PSE

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