1. Flood risk assessment model based on particle swarm optimization rule mining algorithm.
- Author
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WANG Zhao-li, CHEN Xiao-hong, LAI Cheng-guang, and ZHAO Shi-we
- Subjects
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PARTICLE swarm optimization , *ALGORITHMS , *FLOOD damage prevention , *RISK assessment , *BACK propagation , *ARTIFICIAL neural networks - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2013