全国年度和场次洪涝灾情评估研究  

Annual and event flood disasters assessment in China

作　　者：张少春 高惠瑛[1] ZHANG Shaochun;GAO Huiying(School of Engineering,Ocean University of China,Qingdao 266100,China)

机构地区：[1]中国海洋大学工程学院土木工程系,山东青岛266100

出　　处：《人民长江》2020年第6期5-10,共6页Yangtze River

摘　　要：洪涝灾情评估受到人、经济、环境等多重因素的综合影响,呈现出非线性且复杂的关系,以往的灾情评估方法,大多是基于现有灾情数据规律探寻其灾情评估方法和指标,没有有效利用现有的SL 579-2012《洪涝灾情评估标准》。因此,结合现有标准,针对1997~2016年全国年度洪涝灾情和2016,2015,2010年各省份的场次洪涝灾情,利用模糊神经网络综合评判洪涝灾情等级,并结合中国水旱灾害公报中的实际灾情数据验证其合理性,最后提出防洪减灾建议。结果表明:全国年度和各省份洪涝灾情评估结果一致;利用模糊神经网络对年度和场次洪涝灾情的评估是有效且合理的,可提高计算效率。研究成果可为防洪救灾工作提供决策数据支持。Flood disaster assessment is affected by multiple factors such as human,economy and environment,and the relationship between flood disaster assessment and these factors is nonlinear and complex.Besides,in order to explore assessment methods and indicators,the previous disaster assessment is based on existing disasters data laws,which does not make full use of Flood Disaster Assessment Standard(SL 579-2012).Therefore,on the basis of Flood Disaster Assessment Standard,we adopted Fuzzy Neural Network to assess disaster severity of annual flood disasters conditions(1997-2016)in China and event flood disasters conditions(2016,2015,2010)in each province of China.Then,the real disasters data obtained from bulletin of floods and droughts in China was used to verify the assessment results.Then,some suggestions of flood control and disaster mitigation are proposed in this paper.The results show that the assessment results of annual flood disasters conditions is the same as that of the event flood disasters conditions.Hence,Fuzzy Neural Network is available for Flood Disaster Assessment and could improve computational efficiency.This paper can provide data support for flood control and disaster mitigation.

关 键 词：灾情评估 灾情等级 模糊神经网络 防洪减灾 

分 类 号：P426.616[天文地球—大气科学及气象学]