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作 者:冯快乐 周建中[1,2] 江焱生[3] 孙怀卫[1,2] 李薇[1,2] 蔡佳明[1,2]
机构地区:[1]华中科技大学水电与数字化工程学院,湖北武汉430074 [2]数字流域科学与技术湖北省重点实验室华中科技大学,湖北武汉430074 [3]湖北省防汛抗旱指挥部办公室,湖北武汉430071
出 处:《自然灾害学报》2018年第1期148-154,共7页Journal of Natural Disasters
基 金:国家自然科学基金重大研究计划重点项目(91547208);国家自然科学基金面上项目(51579107);国家自然科学基金重点项目(51239004)~~
摘 要:湖北省是山洪灾害多发地,每年因山洪灾害造成的损失十分严重,山洪灾害危险性评价对于防灾减灾具有重要意义。构建了湖北省山洪灾害危险性评价指标体系,根据山洪灾害危险性与历史山洪灾害的相关性,采用RBF(Radial Basis Function,径向基函数)神经网络对小样本调查数据进行拟合、扩展,形成可靠的数据集,使用扩展数据集对BP(Back Propagation)神经网络进行训练,建立山洪灾害危险性评价模型,通过建立的评价模型分析了湖北省山洪灾害危险性的分布特征。研究结果表明,湖北省山洪灾害危险性最高的区域分布在西南部的建始县、巴东县、鹤峰县和中部的钟祥市、孝感市,总面积为1.47万km2;湖北省中等危险区分布在全省各地,面积达到12.48万km2;湖北省低危险区主要分布在西北部的房县、东部的黄陂区、嘉鱼县、黄冈市市辖区,总面积0.84万km2。Hubei Province is a hotspot of flash flood disaster, and the loss caused by flash flood disaster is serious every year. Assessment on the hazard of flash flood disaster has great significance for disaster prevention and mitigation. This paper proposed an index system of assessment on the hazard of flash flood disaster in Hubei Province. The assessment model on the hazard of flash flood was established, according to the correlation of flash flood hazard and historical flood disasters, using RBF ( Radial Basis Function) neural network fitting and expanding the small observed data, forming a reliable data set, and training BP (Back Propagation) neural network with the data set. This paper analyzed distribution characteristics of the hazard of flash flood disaster of Hubei Province by established assessment model. The results showed that the high-hazard areas of flash flood disaster distributed in Jianshi County, Badong County and Hefeng County, the southwest of Hubei Province, and Zhongxiang City, Xiaogan City, the middle part of Hubei Province, a total area is of 14 700 km2. The medium-hazard areas in Hubei Province reached 124 800 km2. The low-hazard areas distributed in Fang County, the northwest, Huangpi District, Jiayu County,Huanggang City, the east of Hubei Province, total area is of 8, 400 km2.
关 键 词:山洪灾害 危险性评价 BP神经网络 RBF神经网络
分 类 号:P429[天文地球—大气科学及气象学] X43[环境科学与工程—灾害防治]
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