Dam Safety Monitoring Model Based on Neural Network and Time Series

Abstract:

The deformation monitoring data of the dam has the typical characters of instability and nonlinearity after being completed and impounding water. To solve the problems, this paper introduces the time series model and BP neural network model to analysis the dam monitoring data. Firstly, time series model was applied to fit and predict and then used the BP neural network model to correct the nonlinear part of residuals. Finally, we can get a series of fitting and predictive value of the monitoring data by combining of above both models. Taking the certain radial displacement value of a measuring point of a certain dam as an example, ARIMA-BP model was established to analyze the data. The result shows: fitting and predictive accuracy of ARIMA-BP model is relatively high and closed to the measured value.

• Periodical: Applied Mechanics and Materials (Volumes 631-632)
• Pages: 543-547
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.631-632.543
• Online since: September 2014
• Authors: Long Long Feng*, Xing Li
• Keywords: Accuracy, ARIMA-Bp Model, Fitting, Prediction, Safety Monitoring

References

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