By considering the nonlinearity

spatial and temporal heterogeneity and dynamic uncertainty of soil moisture process

Elman recurrent neural network model is applied to the prediction of soil moisture in Linyi and Pingyi stations. Results show that the model achieves a high accuracy of soil moisture simulation and the simulated values agree well with observed values in the whole process. The mean absolute errors of prediction precision for Linyi and Pingyi stations are 1. 08%and 1.07%

and the mean relative errors

10.2% and 11.0%

respectively. Elman recurrent neural network model can be used to find some evolutional characters and regular patterns from complex soil moisture system by taking advantage of its nonlinearity

non-append-age and self-adaption capacity. Therefore

the model provides a simple and efficient method which provides high accuracy and reliable precision for soil moisture simulation. In order to further evaluate the superiority of this method

a longer series of data

more investigations in other regions and more comprehensive influence factors are needed to deepen theoretical study and analysis because of limited samples and the highly dynamic uncertainty of soil moisture process.