Cusp Catastrophe Intelligence Combinatorial Prediction of Land Subsidence

Chong Hao Huang; Yu Song Lu; Guo Bao; Zhen Rong Lin

doi:10.4028/www.scientific.net/AMM.155-156.606

Paper Titles

Research the Influence of Comprehensive Materials Multimedia Technology on the Exercise Interest of Students
p.585

Research on the Guidance Methods of Sports Training Based on Data Mining Technology
p.590

Study on Chip Morphology of Electric Hot Minipore Drilling
p.596

Research on Virtual Hobbing Simulation and Study of Tooth Surface Accuracy of Involute Helical Gears
p.601

Cusp Catastrophe Intelligence Combinatorial Prediction of Land Subsidence
p.606

Process Design of Tacking and Welding for Turbine Blades and Positioning Plates
p.611

Flow Patterns Transition Criteria from Bubbly to Slug Flow during Flow Boiling in Confined Vertical Narrow Rectangular Channels
p.616

The Embedded Audio Coding of Algebraic Quantization Based on the Transform Coefficients
p.621

Discussion of the Modern Electronic Technology Application and Future Development Trend on Automobile
p.627

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 155-156Cusp Catastrophe Intelligence Combinatorial...

Cusp Catastrophe Intelligence Combinatorial Prediction of Land Subsidence

Abstract:

Seeking for a scientific and reasonable predict model is the key point to ensure accurate and reliable prediction results for the land subsidence. In this paper, the research of subsidence area pointed out by picks the process of land subsidence which causes by the water reservoir that conforms to the characteristic of nonlinear dynamics, thus, a new model is established to solve the combinatorial weighted coefficients by using the predict precision, and a simple method is introduced to solve the grey model and neural network combination forecast model. Based on many examples, the new cusp catastrophe land subsidence model is proved to be very effective and much more accurate.

You might also be interested in these eBooks

Mechanical Engineering and Green Manufacturing II

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 155-156)

Pages:

606-610

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.155-156.606

Citation:

Cite this paper

Online since:

February 2012

Authors:

Chong Hao Huang, Yu Song Lu, Guo Bao, Zhen Rong Lin

Keywords:

Cusp Catastrophe, Grey-Neural Network, Predict Precision, Prediction of Land Subsidence, Weighted Coefficient

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] J. Deng. The Foundation of Grey Theory. Wuhan, Huazhong University of Science and Technology Press, (2002).

Google Scholar

[2] D. Ma, Q. Zhang, Y. Peng, S.J. Liu, A Particle Swarm Optimization Based Grey Forecast Model of Underground Pressure for Working Surface, Electronic Journal of Geotechnical Engineering, 16 H (2011) 811-830.

Google Scholar

[3] D. Ma, Z.Q. Chen, X.L. Shan, Forecasting of the Fatigue Life of Metal Weld Joints Based on Combined Genetic Neural Network, Key Engineering Materials, 439-440 (2010) 195-201.

DOI: 10.4028/www.scientific.net/kem.439-440.195

Google Scholar

[4] K. Adamowski, J. Franklin. Peak daily water demand forecast modeling using artificial neural networks, American Society of Civil Engineers, Reston, VA 2019-14400, United States. 2008. 119-128.

DOI: 10.1061/(asce)0733-9496(2008)134:2(119)

Google Scholar

[5] L. Wang, H. Zhang, Z. Niu. Application of support vector machines in short-term prediction of urban water consumption. Journal of Tianjin University. Tianjin, 2005, 1021-1025.

Google Scholar

[6] G.Y. Wang, B. Shi, J. Yun. Grey Verhulst prediction model and its application in land subsidence in Changzhou. Hydrogeology and Engineering Geology. 2206, 6: 80-83.

Google Scholar

[7] W.D. Gao, H.R. Zhang, Q.Y. Feng, L. Meng. Grey Verhulst Prediction Model And Its Application In Land Subsidence. Surveying And Mapping Of Sichuan. 2007, 10 (5): 195-197.

Google Scholar

[8] D. Guo; N. Li, D. Pei, D. Zheng. Prediction method of coal and gas outburst using the grey theory and neural network, University of Science and Technology Beijing, Beijing, 100083, China, 2007. pp.354-357.

Google Scholar

[9] Y. Zhang, Y. He. Study of prediction model on grey relational BP neural network based on rough set, Institute of Electrical and Electronics Engineers Computer Society, Piscataway, NJ 08855-1331, United States. 2005. pp.4764-4769.

DOI: 10.1109/icmlc.2005.1527780

Google Scholar

[10] C.S. Cheng, Y.T. Hsu, C.C. Wu. Grey neural network. IEICE of Japan. Tokyo, Japan. 1998. pp.2433-2442.

Google Scholar