摘要
应用蚁群优化算法(Ant Colony Optimization,ACO)进行规则挖掘是一个新的研究热点。为解决指标变量与风险级别间非线性关系,提出一种基于蚁群规则挖掘算法(Ant-Miner)的洪灾风险区划模型。在GIS技术支持下,将该模型应用于北江流域洪灾风险区划实例中,结果表明:1 Ant-Miner模型可挖掘15条适合研究区的洪灾风险分类规则,这些规则以简单的条件语句形式表现,便于生成风险区划图;2 Ant-Miner模型测试精度(95.1%)高于相同条件下BP神经网络模型的精度(92.9%),表明其分类性能更好,对洪灾风险区划具有更好的适用性;3研究区高风险区主要集中于降雨量较大、地势平缓低洼、人口财产密集的地区,与历史洪灾风险情况较吻合,表明所构建的模型科学合理,可为流域洪灾风险评价提供了新思路。
Using Ant Colony Optimization（ ACO） to mine rules is a research hotspot nowadays. This paper proposed a new zoning model of flood risk based on ant colony rule mining algorithm（ Ant-Miner） to solve the non-linear relationship between index and flood risk grade. The model was used in the Beijiang River basin with the support of GIS technique. The assessment results show that 1 15 simple rules expressed in the form of conditional statement were mined by the Ant-Miner model. The rules are appropriate for the study areas and can be easily used for generating a zoning map of flood disaster risk. 2 The test accuracy is 95. 1% in the Ant-Miner model,92. 9% in BP neural network model,indicating that the discriminative capability and flood risk zoning applicability of the former is stronger than the latter. 3The high risk areas identified by Ant-Miner are mainly located in the regions with large precipitation,flat and low-lying terrain and dense population and property. These areas match well with the submerged areas of historical flood disasters,indicating that the Ant-Miner model is reasonable and practicable and can provide a new method for flood risk assessment.
出处
《中山大学学报（自然科学版）》
CAS
CSCD
北大核心
2015年第5期122-129,共8页
Acta Scientiarum Naturalium Universitatis Sunyatseni
基金
国家自然科学基金资助项目(51210013
51479216
51209095
41301627)
国家科技支撑计划资助项目(2012BAC21B0103)
水利部公益资助项目(201301002-02
201301071)
中央高校基本科研业务费专项基金资助项目(2014ZZ0027)
关键词
洪灾
风险区划
蚁群优化算法
规则挖掘
北江流域
Flood disaster
risk zoning
ant colony optimization
rule mining
the Beijiang River basin
