成都市河流水系发达,降雨量分配不均且每年变化大,洪涝灾害频繁发生。本文利用成都市基础地理信息、水系、土地利用、社会经济、气象要素、灾害等信息,通过AHP-熵权法获取成都市各区的洪涝因子权重指标,绘制了成都市洪涝灾害的风险评估...成都市河流水系发达,降雨量分配不均且每年变化大,洪涝灾害频繁发生。本文利用成都市基础地理信息、水系、土地利用、社会经济、气象要素、灾害等信息,通过AHP-熵权法获取成都市各区的洪涝因子权重指标,绘制了成都市洪涝灾害的风险评估图,并通过历史灾情数据检验研究方法的准确性。研究结果表明:在指标选取过程中纳入历时汛期数据能够有效地提高研究结果的精确性。人口密度、GDP密度、平均高程,不透水层以及植被覆盖度对洪涝风险性影响程度较大,导致城市中心、金堂县、双流区、浦江县等地的洪涝灾害风险较大。Chengdu has a well-developed river system, uneven rainfall distribution and large annual changes, and flood disasters occur frequently. This paper uses Chengdu’s basic geographic information, water system, land use, social economy, meteorological elements, disasters and other information to obtain the flood factor weight indicators of each district in Chengdu through the AHP-entropy weight method, draws a risk assessment map of flood disasters in Chengdu, and verifies the accuracy of the research method through historical disaster data. The results show that incorporating historical flood season data in the process of indicator selection can effectively improve the accuracy of the research results. Population density, GDP density, average elevation, impervious layer and vegetation coverage have a greater impact on flood risk, resulting in a greater risk of flood disasters in the city center, Jintang County, Shuangliu District, Pujiang County and other places.展开更多
文摘成都市河流水系发达,降雨量分配不均且每年变化大,洪涝灾害频繁发生。本文利用成都市基础地理信息、水系、土地利用、社会经济、气象要素、灾害等信息,通过AHP-熵权法获取成都市各区的洪涝因子权重指标,绘制了成都市洪涝灾害的风险评估图,并通过历史灾情数据检验研究方法的准确性。研究结果表明:在指标选取过程中纳入历时汛期数据能够有效地提高研究结果的精确性。人口密度、GDP密度、平均高程,不透水层以及植被覆盖度对洪涝风险性影响程度较大,导致城市中心、金堂县、双流区、浦江县等地的洪涝灾害风险较大。Chengdu has a well-developed river system, uneven rainfall distribution and large annual changes, and flood disasters occur frequently. This paper uses Chengdu’s basic geographic information, water system, land use, social economy, meteorological elements, disasters and other information to obtain the flood factor weight indicators of each district in Chengdu through the AHP-entropy weight method, draws a risk assessment map of flood disasters in Chengdu, and verifies the accuracy of the research method through historical disaster data. The results show that incorporating historical flood season data in the process of indicator selection can effectively improve the accuracy of the research results. Population density, GDP density, average elevation, impervious layer and vegetation coverage have a greater impact on flood risk, resulting in a greater risk of flood disasters in the city center, Jintang County, Shuangliu District, Pujiang County and other places.