灾害学2013，Vol.28Issue(1)：92-97,6.
基于地理因子的因旱饮水困难人口快速评估模型——以云南省2012年大旱为例
Geographical Factor Based Rapid Assessment Model of Population in Drinking Water Access Difficulties Because of Drought——A Case Study of 2012 Yunnan Extreme Drought
摘要
Abstract
The problem of rural drinking water difficulty has been continuingly concerned in China. Keeping the basic living conditions is most important for people in the drought-hit disaster areas. Predicting accurately the population in drinking water access difficulties because of drought and taking appropriate mitigation measures can minimize economic loss and personal injury. Taking 2012 Yunnan Extreme Drought as an example, on the basis of collecting the meteorological, basic geographic information, socio-economic data, and disaster effect data of the study area, a rapid assessment model based on BP neural network is constructed. The six factors are the input of network, which are the average monthly precipitation, DEM, river density, the total population, road density and GDP. The population in drinking water access difficulties because of drought is the output of network. Taking 30 drought-affected counties samples for network training, under optimizing the model parameters, the MSE is 0.003 6; by the trained model to predict the population in drinking water access difficulties of remaining 55 drought-affected counties. The fitting result of R2 between the analog value and the true value was 0.67. It shows that the BP artificial neural network method can effectively predict the population in drinking water access difficulties because of drought. The method may provide an effective reference for rapid assessment and disaster verification of the population in drinking water access difficulties because of drought.关键词
地理因子/BP人工神经网络/因旱饮水困难人口/快速评估/云南/大旱/2012年Key words
geographical factor/ BP neural network/ population in drinking water access difficulties because of drought/ rapid assessment/ Yunnan/ Extreme Drought/ 2012分类
资源环境引用本文复制引用
贾慧聪,袁艺,曹春香,潘东华,周洪建,马玉玲..基于地理因子的因旱饮水困难人口快速评估模型——以云南省2012年大旱为例[J].灾害学,2013,28(1):92-97,6.基金项目
国家重大科学研究计划项目(2012CB955402) （2012CB955402）
民政部国家减灾中心委托项目 （）
国家自然科学基金项目(41171330) （41171330）
