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By the analyze of chaos for runoff series, combing the reconstruction phase space theory and BP neural network to develop the BP neural network model based reconstruction phase space, and forecast the runoff series mensal in Xiaoqing river hydrological station of Jinan, the result shows that the model has a very good forecast accuracy and value.
By the main component analysis, and maximum Lyapunov index method, this paper analyses chaotic character of ground water level time series. On this basis, combining the reconstruction phase space of chaos theory with BP neural network to set up a BP neural network model based on chaos theory. This paper forecasts ground water level of the Heihu Spring in Jinan by the model. The result shows that the...
Based on analysis of former matter element extension evaluating model, author points out the former model's limitation that the model can not be used when observed data are bigger than the section field value in evaluation of water quality. Combining synthesis weights with approach degree, a new matter element extension evaluating model based on synthesis weights and approach degree is established...
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