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R-RTRL Based on Recurrent Neural Network with K-Fold Cross-Validation for Multi-step-ahead Prediction Landslide Displacement

  • Jiejie ChenEmail author
  • Ping Jiang
  • Zhigang Zeng
  • Boshan Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10878)

Abstract

The reinforced real-time recurrent learning (R-RTRL) algorithm with K-fold cross-validation for recurrent neural networks (RNNs) are applied to forecast multi-step-ahead landslide displacement (K-R-RTRL). The proposed novel method is implemented to make two-and four-ahead forecasts in Liangshuijing landslide monitoring point ZJG24 in Three Gorges Reservoir area. Based on comparison purpose, two comparative neural networks are performed, one is RTRL, the other is back propagation through time neural network (BPTT). The proposed algorithm K-R-RTRL gets superior performance to comparative networks in the final numerical and experimental results.

Keywords

R-RTRL K-fold cross-validation RNNs K-R-RTRL BPTT 

Notes

Acknowledgements

The work is supported by the Natural Science Foundation of China under Grant 61603129, the Natural Science Foundation of Hubei Province under Grant 2016CFC734.

References

  1. 1.
    Kilburn, C.R.J., Petley, D.N.: Forecasting giant, catastrophic slope collapse: lessons from Vajont, Northern Italy. Geomorphology 54, 21–32 (2005)CrossRefGoogle Scholar
  2. 2.
    Babu, G.L.S., Bijoy, A.C.: Appraisal of Bishop’s method of slope stability analysis. Slope Stab. Eng. 1–2, 249–252 (1999)Google Scholar
  3. 3.
    Lian, C., Zeng, Z.G., Yao, W., Tang, H.M.: Displacement prediction model of landslide based on a modified ensemble empirical mode decomposition and extreme learning machine. Nat. Hazards 66, 759–771 (2013)CrossRefGoogle Scholar
  4. 4.
    Chang, F.J., Hwang, Y.Y.: A self-organization algorithm for real-time flood forecast. Hydrol. Process. 13(2), 123–138 (1999)CrossRefGoogle Scholar
  5. 5.
    Chang, F.J., Chang, L.C., Huang, H.L.: Real-time recurrent learning neural network for stream-flow forecasting. Hydrol. Process. 16, 2577–2588 (2002)CrossRefGoogle Scholar
  6. 6.
    Chang, L.C., Chang, F.J., Chiang, Y.M.: A two step-ahead recurrent neural network for stream-flow forecasting. Hydrol. Process. 18, 81–92 (2004)CrossRefGoogle Scholar
  7. 7.
    Chang, L.C., Chen, P.A., Chang, F.J.: Reinforced two step-ahead weight adjustment technique for online training of recurrent neural networks. IEEE Trans. Neural Netw. Learn. Syst. 23(8), 1269–1278 (2012)CrossRefGoogle Scholar
  8. 8.
    Chen, P.A., Chang, L.C., Chang, F.J.: Reinforced recurrent neural networks for multi-step-ahead flood forecasts. J. Hydrol. 23(8), 71–79 (2013)CrossRefGoogle Scholar
  9. 9.
    Wiens, T.S., Dale, B.C., Boyce, M.S., Kershaw, G.P.: Three way k-fold cross-validation of resource selection functions. Ecol. Model. 212(3–4), 244–255 (2008)CrossRefGoogle Scholar
  10. 10.
    Kraskov, A., Stogbauer, H., Grassberger, P.: Estimating mutual information. Phys. Rev. E 69(066138), 1–16 (2004)MathSciNetGoogle Scholar
  11. 11.
    Smith, T.F., Waterman, M.S.: Identification of common molecular subsequences. J. Mol. Biol. 147, 195–197 (1981)CrossRefGoogle Scholar
  12. 12.
    May, P., Ehrlich, H.-C., Steinke, T.: ZIB structure prediction pipeline: composing a complex biological workflow through web services. In: Nagel, W.E., Walter, W.V., Lehner, W. (eds.) Euro-Par 2006. LNCS, vol. 4128, pp. 1148–1158. Springer, Heidelberg (2006).  https://doi.org/10.1007/11823285_121CrossRefGoogle Scholar
  13. 13.
    Foster, I., Kesselman, C.: The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann, San Francisco (1999)Google Scholar
  14. 14.
    Czajkowski, K., Fitzgerald, S., Foster, I., Kesselman, C.: Grid information services for distributed resource sharing. In: 10th IEEE International Symposium on High Performance Distributed Computing, pp. 181–184, New York (2001)Google Scholar
  15. 15.
    Foster, I., Kesselman, C., Nick, J., Tuecke, S.: The physiology of the grid: an open grid services architecture for distributed systems integration. Technical report, Global Grid Forum (2002)Google Scholar
  16. 16.
    National Center for Biotechnology Information. http://www.ncbi.nlm.nih.gov

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Jiejie Chen
    • 1
    Email author
  • Ping Jiang
    • 2
  • Zhigang Zeng
    • 3
  • Boshan Chen
    • 4
  1. 1.College of Computer Science and TechnologyHubei Normal UniversityHuangshiChina
  2. 2.Computer SchoolHubei PolyTechnic UniversityHuangshiChina
  3. 3.School of AutomationHuazhong University of Science and TechnologyWuhanChina
  4. 4.College of Mathematics and StatisticsHubei Normal UniversityHuangshiChina

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