Groundwater Level Prediction Using SOM-RBFN Multisite Model

Abstract

In this paper, a groundwater level forecasting model is proposed by combining the theory of self-organizing map (SOM) and radial basis function network (RBFN). The proposed model is referred to as SOM-RBFN model. Recently, RBFN has been applied in time series forecasting. Traditionally, the number of hidden units and the positioning of the radial basis centers are crucial problems for establishing RBFN. The proposed model can decide the number of RBFN’s hidden units with using the two-dimensional feature map which is constructed by SOM, and then it can determine the positioning of the radial basis centers easily. The proposed model is applied to actual groundwater level data in southern Taiwan from 1997 to 2003. It is found that the multisite model can predict the 1 month ahead groundwater level more precisely than the single-site model. Moreover, it is also found that the four-site model is more competent in predicting groundwater level as compared to the single-site model and six-site model. Therefore, too much information cannot improve the generalization ability of the model. For groundwater level prediction, the SOM-RBFN multisite model is recommended as an alternative to the other methods because it has a clear principle and a simple structure. In addition, it can produce more reasonable forecasts.