Prediction model of rain water depth on road surface

JI Tian-jian; HUANG Xiao-ming; LIU Qing-quan; TANG Guo-qi

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Article Navigation > Journal of Traffic and Transportation Engineering > 2004 > 4(3): 1-3

JI Tian-jian, HUANG Xiao-ming, LIU Qing-quan, TANG Guo-qi. Prediction model of rain water depth on road surface[J]. Journal of Traffic and Transportation Engineering, 2004, 4(3): 1-3.

Citation:

JI Tian-jian, HUANG Xiao-ming, LIU Qing-quan, TANG Guo-qi. Prediction model of rain water depth on road surface[J]. Journal of Traffic and Transportation Engineering, 2004, 4(3): 1-3.

JI Tian-jian, HUANG Xiao-ming, LIU Qing-quan, TANG Guo-qi. Prediction model of rain water depth on road surface[J]. Journal of Traffic and Transportation Engineering, 2004, 4(3): 1-3.

Citation:

JI Tian-jian, HUANG Xiao-ming, LIU Qing-quan, TANG Guo-qi. Prediction model of rain water depth on road surface[J]. Journal of Traffic and Transportation Engineering, 2004, 4(3): 1-3.

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Prediction model of rain water depth on road surface

1.
School of Transportation, Southeast University, Nanjing 210096, China
2.
Research Institute of Highway, Ministry of Communications, Beijing 100088, China

More Information

Author Bio:
JI Tian-jian (1970-), male, doctoral student, 86-25-83791654, jitianjian@sohu.com
Received Date: 2003-10-17
Publish Date: 2004-09-25

Abstract

Abstract

Rainfall and slope rough degree have great effect on rain water depth on road surface, and their relation is highly nonlinear, it can not be described by simple models. This paper used artificial neural network to establish the prediction model of water film thickness. Input layers of the model were rainfall intensity, gradient, slope length and contexture depth of road, output layer was rain water depth, there were 6 neural cells in hidden layer. The weights and thresholds of the model were attained through training neural network with testing data.Applied results show that the relative coefficient of predictive values and measure values is 0.98, error is 3.08. The result indicates that the predictive model is reasonable.
- highway engineering,
- overland flow,
- artificial neural network,
- rain water depth on road surface

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References(8)

References

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