Simulation and Prediction for Groundwater Dynamics Based on RBF Neural Network

DOI: 10.4236/jwarp.2012.47063   PDF   HTML     4,201 Downloads   6,352 Views   Citations


Based on MATLAB, a new model-BRF network model is founded to be used in groundwater dynamic simulation and prediction. It is systematically studied about the training sample set, testing sample set, the pretreatment of the original data, neural network construction, training, testing and evaluating the entire process. A favorable result is achieved by applying the model to simulate and predict groundwater dynamics, which shows this new method is precise and scientific.

Share and Cite:

Z. Fei, D. Luo and B. Li, "Simulation and Prediction for Groundwater Dynamics Based on RBF Neural Network," Journal of Water Resource and Protection, Vol. 4 No. 7, 2012, pp. 540-544. doi: 10.4236/jwarp.2012.47063.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] S. Cong, “The Function Analysis and Application Study of Radial Basics Function Network,” Computer Engineering and Applications, Vol. 38, No. 3, 2002, pp. 85-87.
[2] H. Demuth and M. Beale, “Neural Network Toolbox User’s Guide,” The MathWorks Inc., Natick, 1997, pp. 420-467.
[3] P. D. Wasserman, “Advanced Methods in Neural Computing,” Van Norstrand Reinhold, New York, 1993, pp. 334-366.
[4] S.-Y. Zheng, Z.-B. Li and X.-A. Li, “Artificial Neural Network Method for Forecast of Underground Water Level,” Northwest Water Lou Shuntian, Shi Yang, Systems Analysis and Design Based on MATLAB-Artificial Neural Network, XiDian University Publishing Company, Xian, 1998, pp. 210-256.
[5] D. Xu and Z. Wu, “Systems Analysis and Design Based on MATLAB6.x-Artificial Neural Network,” XiDian University Publishing Company, Xi’an, 2002, pp. 125-168.

comments powered by Disqus

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.