Elman neural network dynamic prediction based on landslide monitoring data

LI Xunchang; YE Junwen; LI Ge; LI Jun

doi:10.3969/j.issn.1001-1986.2018.03.019

Volume 46 Issue 3

Jun. 2018

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COAL GEOLOGY & EXPLORATION > 2018 > 46(3): 113-120,126. > DOI: 10.3969/j.issn.1001-1986.2018.03.019

LI Xunchang, YE Junwen, LI Ge, LI Jun. Elman neural network dynamic prediction based on landslide monitoring data[J]. COAL GEOLOGY & EXPLORATION, 2018, 46(3): 113-120,126. DOI: 10.3969/j.issn.1001-1986.2018.03.019

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Elman neural network dynamic prediction based on landslide monitoring data

1. School of Geology Engineering and Geomatics, Chang'an University Xi'an 710054, China;
2. Shaanxi Nuclear Industry Engineering Investigation Institute Limited Company, Xi'an 710054, China

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The Basic Scientific Research Operating Expenses of the Central University(310826172203,310826161018)

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Received Date: November 13, 2017
Published Date: June 24, 2018

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Abstract

Abstract

The landslide is a very frequent geological disaster in China, and its monitoring curve of the accumulative displacement has complex nonlinear property. Researchers have established many prediction models, however, the accuracy of these prediction models is not satisfactory. Based on the Elman neural network which can approximate any arbitrary nonlinear function by arbitrary precision, this paper takes the equation for sigmoid as the kernel function, and uses the method of trial when choosing hidden layer, and through the "3δ" method and normalized engineering instance of landslides to accumulate displacement data, and then Elman neural network dynamic prediction model is established. The model made dynamic prediction to the multiple monitoring data and the results show that the goodness of fit between the model prediction results and the measured data is quite high, and the average error is 1.78%, which means that the prediction accuracy is relatively high, which can verify the Elman neural network can play a role in the prediction of landslide disasters.
- Elman neural network,
- 3δ method,
- dynamic prediction,
- kernel function

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References

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