Selection and Prediction of Earthquake Casualties Related Factors Based on BP Neural Network Analysis

Abstract:

To reduce the prediction error rate of earthquake casualties, the paper proposed a prediction model with two steps: (1) screening of the earthquake casualties correlation factors; (2) improving the predictive veracity of general BP(Back Propagation) neural network model.By the analysis of 9 kinds of correlation factors, the paper established the MIV(Mean Impact Value) model based on BP neural network to screen the final correlation factors, and the paper got 6 main correlation factors according to the size of output weights of the factors. Finally, the paper verified the accuracy and practicability of the model through the validation of the model and the solving of prediction error of relevant factors hasn't been selected.

• Periodical: Applied Mechanics and Materials (Volumes 543-547)
• Pages: 2084-2088
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.543-547.2084
• Online since: March 2014
• Authors: Run Biao Bao*, Man Zhang
• Keywords: BP, Casualties Prediction, Correlation Factor, MIV

References

[1] Li Yong-song, Yin Jian-min, Chen Jian-ping, el at. Analysis of 3D In-situ Stress Field and Query System's Development Based on Visual BP Neural Network, Procedia Earth and Planetary Science, vol. 5, pp.64-69, (2012).

DOI: 10.1016/j.proeps.2012.01.011

[2] Ismail, D. -S. Jeng, L.L. Zhang. An optimised product-unit neural network with a novel PSO–BP hybrid training algorithm: Applications to load–deformation analysis of axially loaded piles, Engineering Applications of Artificial Intelligence, vol. 26, no. 10, pp.2305-2314 , (2013).

DOI: 10.1016/j.engappai.2013.04.007

[3] Chunnian Liu，Yiyun Huang, Yubao Li, el at. Research on Construction and Application of Agricultural Risk Knowledge Based on Ontology. International Journal of Digital Content Technology and its Applications, vol. 6, no. 15, pp.338-346, 20.

[4] Li Zhang, Jianhua Luo, Suying Yang. Forecasting box office revenue of movies with BP neural network, Expert Systems with Applications, vol. 36, no. 3, pp.6580-6587, (2009).

DOI: 10.1016/j.eswa.2008.07.064

[5] Chao Ren, Ning An, Jianzhou Wang, Lian Li, Bin Hu, Duo Shang, Optimal parameters selection for BP neural network based on particle swarm optimization: A case study of wind speed forecasting, Knowledge-Based Systems, vol. 56, pp.226-239, (2014).

DOI: 10.1016/j.knosys.2013.11.015

[6] Lin-nan Yang, Lin Peng, Li-min Zhang, el at. A prediction model for population occurrence of paddy stem borer (Scirpophaga incertulas), based on Back Propagation Artificial Neural Network and Principal Components Analysis, Computers and Electronics in Agriculture, vol. 68, no. 2, pp.200-206, (2009).

DOI: 10.1016/j.compag.2009.06.003

[7] Savita G. Kulkarni, Amit Kumar Chaudhary, Somnath Nandi, el at. Modeling and monitoring of batch processes using principal component analysis (PCA) assisted generalized regression neural networks (GRNN), Biochemical Engineering Journal, vol. 18, no. 3, pp.193-210, (2004).

DOI: 10.1016/j.bej.2003.08.009

[8] Tian Xin, Zhu Ranran. Study on Earthquanke Casualty Forecasting Model Based on Principal Component Analysis and BP Neural Network Analysis, Northwestern Seismological Journal, vol. 34, no. 4, (2012).

[9] Hongfei Xiao, Yunli Tian, Prediction of mine coal layer spontaneous combustion danger based on genetic algorithm and BP neural networks, Procedia Engineering, vol. 26, no. 11, pp.139-146, (2011).

DOI: 10.1016/j.proeng.2011.11.2151