基于主成分神经网络的台风灾害经济损失评估
收稿日期: 2008-10-21
修回日期: 2009-05-15
网络出版日期: 2009-09-25
基金资助
国家气象局新技术推广项目(CMATG2008M40)
Economic loss assessment of typhoon based on principal component analysis and neural network
Received date: 2008-10-21
Revised date: 2009-05-15
Online published: 2009-09-25
Supported by
国家气象局新技术推广项目(CMATG2008M40)
本研究建立了浙江省台风灾害直接经济损失评估模型。把浙江省台风灾害直接经济损失资料换算成直接经济损失指数,运用主成分分析法对表示致灾因子、孕灾环境与承灾体的评估因子进行数据处理,提取主成分作为BP神经网络模型的输入,从而建立评估模型。模型历史拟合结果和实际一致。在2007年和2008年影响浙江省的5个台风的实际评估中,强台风"Vipa"灾后评估值比实际值偏大2.16,其余4个台风灾后评估值比实况偏大0.2~0.7,反映了人们对影响大的台风防灾减灾工作的重视和防灾减灾效果。根据台风开始影响时过程风雨预报值进行预评估,过程风雨预报值较准确的台风,预评估结果和灾后评估值一致;过程风雨预报值误差较大的台风,预评估效果较差。因此,该模型可用于实际台风灾害直接经济损失评估,提高台风影响前风雨预报准确率是提高预评估准确率的关键。
娄伟平, 陈海燕, 郑 峰, 吴 睿 . 基于主成分神经网络的台风灾害经济损失评估[J]. 地理研究, 2009 , 28(5) : 1243 -1254 . DOI: 10.11821/yj2009050011
The assessment model of direct economic losses from typhoon disaster in Zhejiang Province is established in this research. The data of direct economic losses in the study region are converted into direct economic losses indexes. Using principal component analysis method, the assessment factors representing disaster inducing factor, disaster-formative environment and disaster-affected body are processed, and the principal component is abstracted as the input of the BP neural network model, thus the assessment model is established. Historical fitting results are consistent with the reality. It is found in the actual assessments of five typhoons affecting Zhejiang in 2007 and 2008 that the post-disaster assessment values of typhoons are higher than the actual situations, and the severer impacts the storms have, the narrower the gap between the assessment values and the actual situation is, which reflects the impact of the disaster prevention and alleviation efforts against typhoons of great influence. According to the forecast values of wind and precipitation when the typhoon began to exert some affect, pre-assessments are conducted and the consequence shows that the pre-assessment results with relatively accurate forecast values are in accordance with the post-disaster assessment values, while the ones with less accurate forecast values are unsatisfactory. Therefore, this model can be applied in the actual assessment of direct economic loss from typhoon damage, and the accurate forecast of wind and precipitation before the typhoons have effect is crucial to the improvement of the accuracy of pre-assessments.
[1] 周子康,刘为伦.浙江台风(热带风暴)灾害的若干特点.地理研究,1995,14(2):56~63.
[2] Liang B Q, Wen Z P, Liang J.The typhoon disasters and related effects in China.The Journal of Chinese Geogaphy,1996,6(1): 61~71.
[3] Kerry Emanuel,Ragoth Sundararajan,John Williams. Hurricanes and global warming: Results from Downscaling IPCC AR4 Simulations.Bull. Amer. Meteor. Soc. ,2008,89(3):347~367.
[4] Wang Xiaoling,Wu Liguang,Ren Fumin, et al.Influences of tropical cyclones on China during 1965~2004.Adv Atmos. Sci. ,2008,25(3):417~426.
[5] 蒋卫国,盛绍学,朱晓华,等. 区域洪水灾害风险格局演变分析——以马来西亚吉兰丹州为例.地理研究,2008,27(3):502~508.
[6] 袁艺,张磊.中国自然灾害灾情统计现状及展望.灾害学, 2006,21(4):89~93.
[7] 徐娜.解析灾害信息管理—灾害信息管理现状.中国减灾, 2006,16(10):24~25.
[8] 卢文芳.上海地区热带气旋灾情的评估和灾年预测.自然灾害学报,1995,4(3):40~45.
[9] 钱燕珍,杨元琴.热带气旋灾害指数的估算与应用方法.气象,2001,27(1):14~18,24.
[10] 孟菲,康建成,李卫江.50年来上海市台风灾害分析及预评估.灾害学,2007,22(4):71~76.
[11] 梁必骐,樊琦.热带气旋灾害的模糊数学评价.热带气象学报,1999,15(4):305~311.
[12] 李春梅,罗晓玲,刘锦銮,等.层次分析法在热带气旋灾害影响评估模式中的应用.热带气象学报,2006,22(3):223~228.
[13] 叶雯,刘美南,陈晓宏.感知器算法在台风风暴潮灾情等级评估中的应用.中山大学学报(自然科学版),2004,43(2):117~120.
[14] 何彩芬,钱燕珍.2000年浙江省热带气旋灾情评估.浙江气象,2002,23(2):4~6,19.
[15] 周子康,刘为纶.浙江省台风灾害的成因因子与危害分析.科技通报,1994,10(3):156~160.
[16] 瞿光中,蔡志林.浙江乐清湾台风风暴潮灾害及防御对策.灾害学,1999,14(3):64~69.
[17] 刘庭杰,顾骏强.浙江省台风灾害的统计分析.灾害学,2000,17(4):64~71.
[18] 史培军.再论灾害研究的理论与实践.自然灾害学报,1996,5(4):6~17.
[19] 吕振平,姚月伟.浙江省台风灾害及应急机制建设.灾害学,2006,21(3):69~71.
[20] 端义宏,朱建荣,秦曾灏,等.一个高分辨率的长江口台风风暴潮数值预报模式及其应用.海洋学报,2005,27(3):11~19.
[21] 史培军,杜鹃,冀萌新,等.中国城市主要自然灾害风险评价研究.地球科学进展,2006,21(2):170~177.
[22] 李克让,张豪禧,尹思明.中国沿海地区灾害发生的环境和社会经济背景.地理研究,1995,14(4):23~31.
[23] 高吉喜,段飞舟,香宝.主成分分析在农田土壤环境评价中的应用.地理研究,2006,25(5):836~842.
[24] 丛明珠,欧向军,赵清,等.基于主成分分析法的江苏省土地利用综合分区研究.地理研究,2008,27(3):574~582.
[25] 唐启义,冯明光.DPS数据处理系统.北京:科学出版社,2007.
[26] 赵清,郑国强,黄巧华.基于神经网络模型技术的南京市主城区城市森林遥感调查.地理研究,2006,25(3):468~476.
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