Computer Science ›› 2019, Vol. 46 ›› Issue (2): 223-229.doi: 10.11896/j.issn.1002-137X.2019.02.034

• Artificial Intelligence • Previous Articles     Next Articles

Spatio-Temporal Integrated Forecasting Algorithm for Dam Deformation

MAO Ying-chi, CAO Hai, HE Jin-feng   

  1. College of Computer and Information,Hohai University,Nanjing 211100,China
  • Received:2018-07-31 Online:2019-02-25 Published:2019-02-25

Abstract: The analysis of the spatial-temporal evolution of dam deformation is conducive for managersto master the overall deformation of the dam’s space.The existing predictive research on dam deformation can be divided into two parts.The first part is only making time series prediction for instrument part with distribution deformation,and the se-cond part is using a method of spatial interpolation at the current moment to obtain unknown point’s value of deformation.Both of these cannot use the historical deformation time series data to predict the deformation of the undistributed instrument.To solve this problem,combining the advantages of traditional spatial-temporal prediction model(STK) and neural network modelssuch such asBP and Gated Recurrent Unit (GRU),this paper constructed a spatio-temporal sequence prediction algorithm named BP-STK-GRU.The main steps are described as follows.Firstly,GRU optimizes the historical time series of individual measuring points.Secondly,BP fits the overall trend of spatio-temporal data at measuring points of the next moment.Thirdly,STK fits the stable parts of BP prediction results.Lastly,the spatial residual value and the overall BP space prediction are combined to get the deformation of the undistributed instrument.The experimental results show that the method is effective,and it has good performance in predicting the stability and accuracy of the deformation value of the unknown point.

Key words: BP network, Dam deformation, Gated recurrent unit, Spatial interpolation, Spatio-Temporal prediction

CLC Number: 

  • TP311
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