To contemplate quantitative and qualitative water features by neural networks method

https://doi.org/10.17221/4375-PSECitation:Neruda M., Neruda R. (2002): To contemplate quantitative and qualitative water features by neural networks method. Plant Soil Environ., 48: 322-326.
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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.
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