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)


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.


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



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


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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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