¹College of Urban Construction, Hebei University of Engineering, Handan, 056038, China
²College of Water Conservancy and Hydropower, Hebei University of Engineering, Handan, 056038, China
³State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, China

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Abstract—Because of Boundary types and geologic conditions, which possess random and obscure characteristics, groundwater heads vary with the conditions. The prediction of groundwater level is one of the main work of hydraulic government, which is predicted based on the history data and the relative influence factors. Therefore, prediction precision depends on the accuracy of history data. Data mining has provided a new method for analyzing massive, complex and noisy data. According to the complexity and ambiguity of groundwater system, a new integration of grey with neural network model is built to forecast groundwater heads, which were used to judge whether future groundwater heads were extraordinarily over the history range or not. This method overcomes the disadvantages which the grey method only predict the linear trend. The methods were used to analyze the random characteristics of groundwater heads in anyang city. The results indicate that the method is reliable, and reasonable.

Index Terms—grey degree, groundwater level, neural network, Anyang city

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Cite: Changjun Zhu, Qinghua Luan, Zhenchun Hao, Qin Ju, "Integration of Grey with Neural Network Model and Its Application in Data Mining," Journal of Software vol. 6, no. 4, pp. 716-723, 2011.