SOM-RBF神经网络模型在地下水位预测中的应用应用

doi:10.13278/j.cnki.jjuese.201501204

Abstract

Abstract:

As the hidden units of radial basis function network (RBF) were optimized by the theory of self-organizing map (SOM), the groundwater levels forecasting error range, due to its structural problems, could be reduced. With the two-dimensional feature map and clustering results of SOM, the number of hidden units, the position and the width of the radial basis centers can be easily determined. The SOM-RBFN model can be established. The accuracy of the model was verified by predicting groundwater level at Erdao Town in Fengman District of Jilin City based on observed groundwater level from 2000 to 2009.In addition, dynamic data of groundwater level for five years (2005-2009), seven years (2003-2009), ten years (2000-2009), are used as study samples and make forecast one by one, which can examine that if the sample size could influence the forecast result. The results prove that SOM-RBFN model can be used in groundwater levels dynamic forecasting, because the averages of RMSE and CE are 0.43 and 0.52, respectively, which are the relatively good outcomes. And, the averages of RMSE and CE of RBF7 are 0.38 and 0.68, whose results are better than RBF5 and RBF10. Therefore, it can be known that the amount of data cannot directly influence the accuracy of results.

Key words: groundwater level prediction, self-organizing map, radial basis function, neural networks

CLC Number:

P641

Liu Bo, Xiao Changlai, Liang Xiujuan. Application of Combining SOM and RBF Neural Network Model for Groundwater Levels Prediction[J].Journal of Jilin University(Earth Science Edition), 2015, 45(1): 225-231.

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Application of Combining SOM and RBF Neural Network Model for Groundwater Levels Prediction

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