Projection Pursuit Flood Disaster Classification Assessment Method Based on Multi-Swarm Cooperative Particle Swarm Optimization

ABSTRACT

The indicators of flood damage assessment in the flood classification are often incompatible, and it is very difficult to use those indicators value directly for classification assessment. Projection pursuit technology can project higher dimensional incompatible data into lower dimensional sub-space, and find the projection values for optimal projection index function to get the higher dimensional data structure features, which has been improved to be reasonable and effective for flood disaster classification assessment. However, it is a bit difficult to optimize the parameters of projection index functions, as a result, that limits the applications of this method. As an emerging heuristic global optimization algorithm based on swarm intelligence, particle swarm optimization algorithm has the ability of solving complex optimization problem, but it still be easily convergent early, and can not search the global optimal solution. In this paper, a flood disaster classification assessment method based on multi-swarm cooperative particle swarm optimization is proposed, which adopts a tri-parameter Logistic curve to construct the flood disaster projection pursuit model, and uses mul-ti-swarm system particle swarm optimization method to optimize the parameters of the projection index functions. The typical test function experiment shows that this optimization method can solve the early convergence commonly found in standard particle swarm optimization algorithm, which global optimized ability is improved greatly. Applied in flood disaster assessment in HeNan Province, the results using this method comparing with others indicates that it can assess effectively the flood disaster, and has better assessment accuracy and disaster resolution.

The indicators of flood damage assessment in the flood classification are often incompatible, and it is very difficult to use those indicators value directly for classification assessment. Projection pursuit technology can project higher dimensional incompatible data into lower dimensional sub-space, and find the projection values for optimal projection index function to get the higher dimensional data structure features, which has been improved to be reasonable and effective for flood disaster classification assessment. However, it is a bit difficult to optimize the parameters of projection index functions, as a result, that limits the applications of this method. As an emerging heuristic global optimization algorithm based on swarm intelligence, particle swarm optimization algorithm has the ability of solving complex optimization problem, but it still be easily convergent early, and can not search the global optimal solution. In this paper, a flood disaster classification assessment method based on multi-swarm cooperative particle swarm optimization is proposed, which adopts a tri-parameter Logistic curve to construct the flood disaster projection pursuit model, and uses mul-ti-swarm system particle swarm optimization method to optimize the parameters of the projection index functions. The typical test function experiment shows that this optimization method can solve the early convergence commonly found in standard particle swarm optimization algorithm, which global optimized ability is improved greatly. Applied in flood disaster assessment in HeNan Province, the results using this method comparing with others indicates that it can assess effectively the flood disaster, and has better assessment accuracy and disaster resolution.

Cite this paper

nullW. Huang and X. Zhang, "Projection Pursuit Flood Disaster Classification Assessment Method Based on Multi-Swarm Cooperative Particle Swarm Optimization,"*Journal of Water Resource and Protection*, Vol. 3 No. 6, 2011, pp. 415-420. doi: 10.4236/jwarp.2011.36051.

nullW. Huang and X. Zhang, "Projection Pursuit Flood Disaster Classification Assessment Method Based on Multi-Swarm Cooperative Particle Swarm Optimization,"

References

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[2] B.-H. Wang and X.-Y. Yang, “The Application of PPC Model of RAGA on the Compositive Evaluation of Flood Disaster in Heilongjiang Province,” Journal of Guangdong Technical College of Water Resources and Electric Engineering, 2009, pp. 46-49.

[3] A.-X. Zhao and Z.-J. Ma, “Appraising Study for the Loss Evaluation System of Natural Disasters,” Journal of Natural Disasters, 1993, Vol. 2, pp. 1-7.

[4] Y.-Y. He, J.-Z. Zhou, Qin Hui, L. Mo and P.-G. Kou, “Flood Disaster Classification Based on Fuzzy Clustering Iterative Model and Modified Differential Evolution Algorithm,” Proceedings of the 6th International Conference on Fuzzy Systems and Knowledge Discovery, Tianjing, 14-16 August 2009, Vol 3.

[5] Y.-N. Chen and S.-Q. Yang, “Application of Grey Clustering Analysis in the Classification of Flood Disaster Grade,” Arid Land Geography, 1999, pp. 37-42.

[6] G. Kim and A. P. Barros, “Quantitative Flood Forecasting Using Multisensor Data and Neural Networks,” Journal of Hydrology, Vol. 246, No.1-4, 2001, pp. 45-62. doi:10.1016/S0022-1694(01)00353-5

[7] S.-J. Wang, X.-l. Zhang, Y. Hou and J. Ding, “Projection Pursuit Model for Evaluating of Flood Events,” Hydrology, Vol. 22, 2002, pp. 1-4.

[8] A. Montanari and L. Lizzani, “A Projection Pursui Approach to Variable Selection,” Computational Statisics & Data Analysis, Vol. 35, No. 4, 2001, pp. 463-473. doi:10.1016/S0167-9473(00)00026-8

[9] R. Poli, J. Kennedy and T. B. Well, “Particle Swarm Optimization: A Overview,” Swarm Intelligence, Vol. 1, No. 1, 2007, pp. 33-57. doi:10.1007/s11721-007-0002-0

[10] J. V. Ast, R. Babuska and B. D. Schutter, “Particle Swarms in Optimization and Control,” Proceedings of the 17th World Congress the International Federation of Automatic Control, Seoul, Korea, 6-11 July 2008, pp. 5131-5136.

[11] A. Banks, J. Vincent and C. Anyakoha, “A review of Particle Swarm Optimization. Part I: Background and Development,” Natural Computing, Vol. 6, No. 4, 2007, pp. 467-484. doi:10.1007/s11047-007-9049-5

[12] J. Friedman and J. Tukey, “A Projection Pursuit Algorithm for Exploratory Data Analysis,” Computers, Vol. 23, 1974, pp. 881-890.

[13] Y.-L. Lu, J.-Z. Zhou, L.-X. Song and Y.-Y. He, “Flood Disaster Evaluating Method Based on CCPSO along with Project Pursuit Model and Its Application,” Journal of System Simulation, 2010, pp. 383-387+390.

[14] J. Kennedy and R. Eberhart, “Particle Swarm Optimization,” IEEE International Conference Evolutionary Computation, Anchorage, 15-19 July 1995, pp. 1942- 1948.

[15] P. J. Angeline, “Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Difference,” Proceedings of the 7th Annual Conference on Evolutionary Programming, San Diego, California, 26-29 March 1998, pp. 601-610. doi:10.1007/BFb0040811

[16] B. Niu, Y.-l. Zhu, X.-X. He and H. Shen, “A Multi-Swarm Optimizer Based Fuzzy Modeling Approach for Dynamic System Processing,” Neurocomputing, Vol. 71, No.7-9, 2008, pp. 1436-1448. doi:10.1016/j.neucom.2007.05.010

[17] Y. Marinakis and M. Marinaki, “A Hybrid Multi-Swarm Particle Swarm Optimization Algorithm for the Probabilisitc Traveling Salesman Problem,” Computers & Operations Research, Vol. 37, No. 3, 2010, pp. 432-442. doi:10.1016/j.cor.2009.03.004

[18] J.-L. Jin, X.-L. Zhang, J. Ding, “Projection Pursuit Model for Evaluating Grade of Flood Disaster Loss,” System Engineering-Theory & Practice, 2002, pp. 140-144.

[1] E. J. Plate, “Flood Risk and Flood Management,” Journal of Hydrology, Vol. 267, No.1-2, 2002, pp. 2-11. doi:10.1016/S0022-1694(02)00135-X

[2] B.-H. Wang and X.-Y. Yang, “The Application of PPC Model of RAGA on the Compositive Evaluation of Flood Disaster in Heilongjiang Province,” Journal of Guangdong Technical College of Water Resources and Electric Engineering, 2009, pp. 46-49.

[3] A.-X. Zhao and Z.-J. Ma, “Appraising Study for the Loss Evaluation System of Natural Disasters,” Journal of Natural Disasters, 1993, Vol. 2, pp. 1-7.

[4] Y.-Y. He, J.-Z. Zhou, Qin Hui, L. Mo and P.-G. Kou, “Flood Disaster Classification Based on Fuzzy Clustering Iterative Model and Modified Differential Evolution Algorithm,” Proceedings of the 6th International Conference on Fuzzy Systems and Knowledge Discovery, Tianjing, 14-16 August 2009, Vol 3.

[5] Y.-N. Chen and S.-Q. Yang, “Application of Grey Clustering Analysis in the Classification of Flood Disaster Grade,” Arid Land Geography, 1999, pp. 37-42.

[6] G. Kim and A. P. Barros, “Quantitative Flood Forecasting Using Multisensor Data and Neural Networks,” Journal of Hydrology, Vol. 246, No.1-4, 2001, pp. 45-62. doi:10.1016/S0022-1694(01)00353-5

[7] S.-J. Wang, X.-l. Zhang, Y. Hou and J. Ding, “Projection Pursuit Model for Evaluating of Flood Events,” Hydrology, Vol. 22, 2002, pp. 1-4.

[8] A. Montanari and L. Lizzani, “A Projection Pursui Approach to Variable Selection,” Computational Statisics & Data Analysis, Vol. 35, No. 4, 2001, pp. 463-473. doi:10.1016/S0167-9473(00)00026-8

[9] R. Poli, J. Kennedy and T. B. Well, “Particle Swarm Optimization: A Overview,” Swarm Intelligence, Vol. 1, No. 1, 2007, pp. 33-57. doi:10.1007/s11721-007-0002-0

[10] J. V. Ast, R. Babuska and B. D. Schutter, “Particle Swarms in Optimization and Control,” Proceedings of the 17th World Congress the International Federation of Automatic Control, Seoul, Korea, 6-11 July 2008, pp. 5131-5136.

[11] A. Banks, J. Vincent and C. Anyakoha, “A review of Particle Swarm Optimization. Part I: Background and Development,” Natural Computing, Vol. 6, No. 4, 2007, pp. 467-484. doi:10.1007/s11047-007-9049-5

[12] J. Friedman and J. Tukey, “A Projection Pursuit Algorithm for Exploratory Data Analysis,” Computers, Vol. 23, 1974, pp. 881-890.

[13] Y.-L. Lu, J.-Z. Zhou, L.-X. Song and Y.-Y. He, “Flood Disaster Evaluating Method Based on CCPSO along with Project Pursuit Model and Its Application,” Journal of System Simulation, 2010, pp. 383-387+390.

[14] J. Kennedy and R. Eberhart, “Particle Swarm Optimization,” IEEE International Conference Evolutionary Computation, Anchorage, 15-19 July 1995, pp. 1942- 1948.

[15] P. J. Angeline, “Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Difference,” Proceedings of the 7th Annual Conference on Evolutionary Programming, San Diego, California, 26-29 March 1998, pp. 601-610. doi:10.1007/BFb0040811

[16] B. Niu, Y.-l. Zhu, X.-X. He and H. Shen, “A Multi-Swarm Optimizer Based Fuzzy Modeling Approach for Dynamic System Processing,” Neurocomputing, Vol. 71, No.7-9, 2008, pp. 1436-1448. doi:10.1016/j.neucom.2007.05.010

[17] Y. Marinakis and M. Marinaki, “A Hybrid Multi-Swarm Particle Swarm Optimization Algorithm for the Probabilisitc Traveling Salesman Problem,” Computers & Operations Research, Vol. 37, No. 3, 2010, pp. 432-442. doi:10.1016/j.cor.2009.03.004

[18] J.-L. Jin, X.-L. Zhang, J. Ding, “Projection Pursuit Model for Evaluating Grade of Flood Disaster Loss,” System Engineering-Theory & Practice, 2002, pp. 140-144.