PDF(1165 KB)
Flood risk assessment model based on particle swarm optimization rule mining algorithm
WANG Zhao-li, CHEN Xiao-hong, LAI Cheng-guang, ZHAO Shi-wei
Systems Engineering - Theory & Practice ›› 2013, Vol. 33 ›› Issue (6) : 1615-1621.
PDF(1165 KB)
PDF(1165 KB)
Flood risk assessment model based on particle swarm optimization rule mining algorithm
Particle swarm optimization (PSO) as a novel intelligent optimization algorithm has been used successfully in many fields, but its application to flood hazard risk assessment is a new research topic. This paper introduces the theory and flow of application of particle swarm optimization rule mining (PSO-Miner) algorithm to flood damage risk assessment. This paper selected Beijiang River Basin, China, as study area for flood damage risk assessment based on PSO-Miner algorithm and BPANN method. The results of a case study indicate that the advantages of PSO-Miner algorithm can be summarized as follows: It does not assume an implicit assumption for processing dataset and has strong robustness; it can mine very simple assessment rules; it can have a better performance than BPANN model. So the PSO-Miner algorithm provides a new approach for flood risk assessment.
flood damage / risk assessment / particle swarm optimization / rule mining / geographical information system {{custom_keyword}} /
[1] Tingsanchali T, Karim M F. Flood hazard and risk analysis in the southwest region of Bangladesh[J]. Hydrological Process, 2005, 19: 2055-2069.
[2] 刘家福, 李京, 刘荆, 等.基于GIS/AHP集成的洪水灾害综合风险评价——以淮河流域为例[J].自然灾害学报, 2008, 17(6): 100-114.Liu J F, Li J, Liu J, et al. Integrated GIS/AHP-based flood risk assessment: A case study of Huaihe River Basin in China[J]. Journal of Natural Disasters, 2008, 17(6): 100-114.
[3] Jiang W G, Deng L, Chen L Y, et al. Risk assessment and validation of flood disaster based on fuzzy mathematics[J]. Progress in Natural Science, 2009, 19: 1419-1425.
[4] Tingsanchali T, Karim F. Flood hazard assessment and risk-based zoning of a tropical flood plain: case study of the Yom River, Thailand[J]. Hydrological Sciences Journal, 2010, 55(2): 145-161.
[5] Li L F, Wang J F, Leung H, et al. Assessment of catastrophic risk using Bayesian network constructed from domain knowledge and spatial data[J]. Risk Analysis, 2010, 30(7): 1157-1175.
[6] 陈曜,丁晶,赵永红.基于投影寻踪原理的四川省洪灾评估[J].水利学报, 2010, 41(2): 220-225.Chen Y, Ding J, Zhao Y H. Assessment on flood disaster in Sichuan Province based on the principle of projection pursuit method[J]. Journal of Hydraulic Engineering, 2010, 41(2): 220-225.
[7] 杨乐婵, 邓松, 徐建辉. 基于BP网络的洪灾风险评价算法[J].计算机技术与发展, 2010, 20(4): 232-234.Yang L C, Deng S, Xu J H. Flood risk evaluation algorithm on BP net[J]. Computer Technology and Development, 2010, 20(4): 232-234.
[8] Eberhart R C, Kennedy J. A new optimizer using particle swarm[C]// Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Janpan: IEEE Press, 1995: 39-43.
[9] Clerc M, Kennedy J. The particle swarm-explosion, stability and convergence in a multidimensional complex space[J]. IEEE Trans Evolut Comput, 2002, 6(1): 58-73.
[10] Tsai C Y, Yeh S W. A multiple objective particle swarm optimization approach for inventory classification[J]. International Journal of Production Economics, 2008, 114(2): 656-666.
[11] Martens D, Baesens B, Fawcett T. Editorial survey: Swarm intelligence for data mining[J]. Machine Learning, 2010, 82(1): 1-42.
[12] Sousa T, Silva A, Neves A. Particle swarm based data mining algorithms for classification tasks[J]. Parallel Computing, 2004, 30(5-6): 767-783.
[13] Liu Y, Qin Z, Shi Z W, et al. Rule discovery with particle swarm optimization[J]. Lecture Notes in Computer Science, 2004, 3309: 291-296.
[14] Alatas B, Akin E. Multi-objective rule mining using a chaotic particle swarm optimization algorithm[J]. Knowledge-Based Systems, 2009, 22(6): 455-460.
[15] Mpiperis I, Malassiotis S, Petridis V, et al. 3D facial expression recognition using swarm intelligence[C]// The 2008 International Conference on Acoustics, Speech and Signal Processing, Las Vegas, Nevada, USA: IEEE Press, 2008: 2133-2136.
[16] Liu X P, Li X, Peng X J, et al. Swarm intelligence for classification of remote sensing data[J]. Science in China Series D: Earth Sciences, 2008, 51(1): 79-87.
[17] Gandhi K R, Karnan M, Kannan S. Classification rule construction using particle swarm optimization algorithm for breast cancer data sets[C]// The 2010 International Conference on Signal Acquisition and Processing, Bangalore, India: IEEE Press, 2010: 233-237.
[18] 唐川, 朱静. 基于GIS的山洪灾害风险区划[J]. 地理学报, 2005, 60(1): 87-94.Tang C, Zhu J. A GIS based regional torrent risk zonation[J]. Acta Geographica Sinica, 2005, 60(1): 87-94.
[19] 宫清华, 黄光庆, 郭敏,等. 基于GIS技术的广东省洪涝灾害风险区划[J].自然灾害学报, 2009, 18(1): 58-63.Gong Q H, Huang G Q, Guo M, et al. GIS-based risk zoning of flood hazard in Guangdong Province[J]. Journal of Natural Disasters, 2009, 18(1): 58-63.
PDF(1165 KB)
/
| 〈 |
|
〉 |
