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To contemplate quantitative and qualitative water features by neural networks method

Author

Listed:
  • 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)

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.

Suggested Citation

  • M. Neruda & R. Neruda, 2002. "To contemplate quantitative and qualitative water features by neural networks method," Plant, Soil and Environment, Czech Academy of Agricultural Sciences, vol. 48(7), pages 322-326.
    • Handle: RePEc:caa:jnlpse:v:48:y:2002:i:7:id:4375-pse
    DOI: 10.17221/4375-PSE
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