Evaluation Model of Meteorological Disaster Loss with Normalized Indices Based on Projection Pursuit Regression

Li Zuoyong; Xu Yuanwei; Wang Jiayang; Liu Yun

doi:10.11898/1001-7313.20160411

Vol.27, NO.4, 2016

Turn off MathJax

Article Contents

Article Navigation > Journal of Applied Meteorological Science > 2016 > 27(4): 480-487

Li Zuoyong, Xu Yuanwei, Wang Jiayang, et al. Evaluation model of meteorological disaster loss with normalized indices based on projection pursuit regression. J Appl Meteor Sci, 2016, 27(4): 480-487. DOI: 10.11898/1001-7313.20160411.

Citation:

Li Zuoyong, Xu Yuanwei, Wang Jiayang, et al. Evaluation model of meteorological disaster loss with normalized indices based on projection pursuit regression. J Appl Meteor Sci, 2016, 27(4): 480-487. DOI: 10.11898/1001-7313.20160411.

Citation:

PDF( 401 KB)

Evaluation Model of Meteorological Disaster Loss with Normalized Indices Based on Projection Pursuit Regression

DOI: 10.11898/1001-7313.20160411

College of Resources and Environment, Chengdu University of Information Technology, Chengdu 610225

Received Date: 2016-01-15
Rev Recd Date: 2016-05-10
Publish Date: 2016-07-31

Abstract

Abstract

Scientific and reasonable evaluation of meteorological disaster loss has important significance for decisions of disaster reduction and relief. A universal and general model of projection pursuit regression (PPR) is proposed with the matrix for the evaluations of different meteorological disaster systems, on the basis of gauge transformation for disaster loss indexes. Because of the "equivalence" of each normalized index, only models of NV-PPR (2) and NV-PPR (3) are necessary for the normalized index values (NV) of 2 indices and 3 indices of meteorological disaster system. Furthermore, the NV-PPR modeling for over 3 indexes of meteorological disaster system could be represented by the combinations of some NV-PPR (2) and (or) NV-PPR (3) models. Models are applied to the evaluations of typhoon disaster loss in Guangdong and 2 lightning disasters loss in Chongqing, and evaluation results of this method are compared with those of other methods. It shows that the evaluation model (NV-PPR) of meteorological disaster loss with gauged transformation based on projection pursuit regression is independent of index numbers, with features of simplicity and utility. The model can also be extended and apply to other disaster loss assessment systems.
- gauged transformation;
- normalized index value;
- projection pursuit regression;
- meteorological disaster;
- evaluation of disaster loss

FullText(HTML)

References(25)

Figures and Tables

Table 1 Average values, standard deviation of generated samples and variation ranges of normalized index values of grade standard for different meteorological disaster systems

k	x′_jk	x′_jk	σ_jk
1	[0.10, 0.24]	0.1855	0.0278
2	[0.18, 0.32]	0.2417	0.0341
3	[0.25, 0.40]	0.3064	0.0305
4	[0.33, 0.46]	0.3779	0.0326
5	[0.40, 0.55]	0.4430	0.0275

DownLoad: Download CSV

Table 2 Benchmarks c_j0, grade standard values c_jk and normalized standard values x′_jk of 5 indices for typhoon disaster loss

指标	c_j0	k=1(微灾)		k=2(小灾)		k=3(中灾)		k=4(大灾)
指标	c_j0	c_jk	x′_jk	c_jk	x′_jk	c_jk	x′_jk	c_jk	x′_jk
C₁	1	≤5	0.1609	20	0.2996	30	0.3401	40	0.3689
C₂	0.05	≤1	0.1498	10	0.2649	20	0.2996	100	0.3800
C₃	20	≤100	0.1609	300	0.2708	700	0.3555	900	0.3807
C₄	0.12	≤0.5	0.1427	1	0.2120	3	0.3219	10	0.4423
C₅	1.2	≤5	0.1427	20	0.2813	30	0.3219	100	0.4423

DownLoad: Download CSV

Table 3 Actual values c_j, normalized values x′_j of indices for typhoon disaster loss of 6 typhoon disasters in Guangdong Province

台风编号	C₁		C₂		C₃		C₄		C₅
台风编号	c₁	x′₁	c₂	x′₂	c₂	x′₂	c₄	x′₄	c₅	x′₅
0104	29.17	0.3373	26	0.3127	712.34	0.3573	1.09	0.2206	28.78	0.3177
0114	11.27	0.2422	4	0.2191	21.27	0.0062	1	0.212	7.89	0.1883
0220	5.14	0.1637	0	0.0000	64.35	0.1169	0.08	0.0000	0.78	0.0000
0604	30.9	0.3431	123	0.3904	779	0.3662	12.12	0.4615	143.67	0.4785
0606	35.18	0.3560	46	0.3412	473.5	0.3164	2.49	0.3033	66.27	0.4011
9710	27.9	0.3329	71	0.3629	913.4	0.3821	2.3	0.2953	32.18	0.3289

DownLoad: Download CSV

Table 4 Assessment results of 6 typhoon disasters loss in Guangdong Province by NV-PPR model and Hopfield neural network

台风编号	NV-PPR模型		5个NV-PPR (2)		5个NV-PPR (3)		PPR (2) 和PPR (3)		Hopfield神经网络模型评价级别
台风编号	平均值	级别	平均值	级别	平均值	级别	平均值	级别	Hopfield神经网络模型评价级别
0104	0.4838	3	0.4982	3	0.4934	3	0.4958	3	大灾
0114	0.2588	2	0.2797	2	0.2770	2	0.2784	2	小灾
0220	0.0680	1	0.0904	1	0.0896	1	0.0900	1	微灾
0604	0.6372	4~5	0.6575	5	0.6512	5	0.6544	5	巨灾
0606	0.5377	4	0.5538	4	0.5485	4	0.5512	4	大灾
9710	0.5366	4	0.5486	4	0.5434	4	0.5460	4	大灾

DownLoad: Download CSV

Table 5 Grading standards of lightning disaster loss indices

指标	c_j0	k=1(一般)		k=2(较重)		k=3(严重)		k=4(特重)
指标	c_j0	c_j1	x′_j1	c_j2	x′_j2	c_j3	x′_j3	c_j4	x′_j4
C₁	2.5	5	0.1386	7.5	0.2197	15	0.3584	20	0.4159
C₂	25	50	0.1386	75	0.2197	150	0.3584	200	0.4159
C₃	1.2	2	0.1022	3.5	0.2141	7.5	0.3665	10	0.4241
C₄	1.2	2	0.1022	3.5	0.2141	7.5	0.3665	10	0.4241
C₅	25	50	0.1386	75	0.2197	150	0.3584	200	0.4159
C₆	12	67	0.1720	200	0.2813	500	0.3729	667	0.4018
C₇		A	0.17	B	0.25	D	0.33	E	0.40
注：A表示古迹受到轻微破坏，容易修复；B表示古迹受到较为严重破坏，可以修复但有一定难度；D表示古迹受到严重破坏，小部分损毁古迹无法修复；E表示古迹受到严重破坏，部分损毁古迹无法修复。

DownLoad: Download CSV

References

[1]	Schmidt S, Kemfert C, Hoppe P.The impact of social-economics and climate change on tropical cyclone losses in the USA.Regional Environmental Change, 2010, 10(1):13-23. doi: 10.1007/s10113-008-0082-4
[2]	Xiao Fengjin, Xiao Zinin.Characteristics of tropical cyclones in China and their impacts analysis.Nat Hazards, 2010, 54:827-837. doi: 10.1007/s11069-010-9508-7
[3]	Zhang Q, Wu L, Lin Q.Troptical cyclone damages in China 1983-2006.Bull Amer Meteor Soc, 2009, 90(4):489-495. doi: 10.1175/2008BAMS2631.1
[4]	Jose I B.Major flood disasters in Europe:1950-2005.Nat Hazards, 2007, 42:125-148. doi: 10.1007/s11069-006-9065-2
[5]	叶殿秀, 赵珊珊, 王有民, 等.2012年我国主要气象灾害回顾.灾害学, 2013, 28(3):128-132. http://www.cnki.com.cn/Article/CJFDTOTAL-ZHXU201303025.htm
[6]	陈联寿.热带气象灾害及其研究进展.气象, 2010, 36(7):101-110. doi: 10.7519/j.issn.1000-0526.2010.07.017
[7]	张蕾, 霍治国, 黄大鹏.等.10—11月海南省瓜菜苗期湿涝风险评估与区划.应用气象学报, 2015, 26(4):432-441. doi: 10.11898/1001-7313.20150405
[8]	李莉, 匡绍敏, 莫建飞, 等.广西甘蔗秋旱灾害风险评估技术初步研究.应用气象学报, 2016, 27(1):95-101. doi: 10.11898/1001-7313.20160110
[9]	Nalbantis I, Tsakiris G.Assessment of hydrological drought revisited.Water Resources Management, 2009, 23(5):881-897. doi: 10.1007/s11269-008-9305-1
[10]	Ologunorisa T E.An assessment of flood vulnerability zones in the Niger delta Nigeria.Inter J Environ Studies, 2004, 61(1):31-38. doi: 10.1080/0020723032000130061
[11]	李祚泳, 杨怀金, 燕鹏.基于免疫进化算法优化的灾情评估指数公式及效果检验.高原气象, 2004, 23(4):553-557. http://www.cnki.com.cn/Article/CJFDTOTAL-GYQX200404021.htm
[12]	李芬, 于文金, 张建新, 等.干旱灾害评估研究进展.地理科学进展, 2011, 30(7):891-898. doi: 10.11820/dlkxjz.2011.07.015
[13]	张容焱, 徐宗焕, 游立军, 等.福建热带气旋风雨空间分布特征及风险评估.应用气象学报, 2012, 23(6):672-682. doi: 10.11898/1001-7313.20120604
[14]	侯威, 杨杰, 赵俊虎.不同时间尺度下气象旱涝强度评估指数.应用气象学报, 2013, 24(6):695-703. doi: 10.11898/1001-7313.20130606
[15]	Choi K S, Kim D W, Byun H R.Statistical model for seasonal prediction of tropical cyclone frequency in the mid-latitudes of East Asia.Theoritical Application Climate, 2010, 102(1):105-114. https://www.cabdirect.org/cabdirect/abstract/20103300664
[16]	Hamid S, Golam K B M, Gulati S, et al.Predicting losses of residential structures in the state of Florida by the public hurricane loss evaluation model.Statistical Methodology, 2010, 7(5):552-573. doi: 10.1016/j.stamet.2010.02.004
[17]	闫敏慧, 姚秀萍, 王蕾, 等.用层次分析法确定气象服务评价指标权重.应用气象学报, 2014, 25(4):470-475. doi: 10.11898/1001-7313.20140410
[18]	卞洁, 李双林, 何金海.长江中下游地区洪涝灾害风险性评估.应用气象学报, 2011, 22(5):604-611. doi: 10.11898/1001-7313.20110511
[19]	刘少军, 张京红, 何政伟, 等.改进的物元可拓模型在台风灾害预评估中的应用.自然灾害学报, 2012, 21(2):135-141. http://www.cnki.com.cn/Article/CJFDTOTAL-ZRZH201202019.htm
[20]	欧阳蔚, 于艳青, 金菊良, 等.基于联系数的安徽省抗旱能力综合评价模型.人民黄河, 2014, 36(3):52-57. http://www.cnki.com.cn/Article/CJFDTOTAL-RMHH201403019.htm
[21]	刘德地, 陈晓宏.基于支持向量机的洪水灾情综合评价模型.长江流域资源与环境, 2008, 17(3):490-494. http://www.cnki.com.cn/Article/CJFDTOTAL-CJLY200803032.htm
[22]	董四辉, 宿博, 赵宇库.基于投影寻踪技术的洪水灾情综合评价.中国安全科学学报, 2012, 22(12):64-69. http://www.cnki.com.cn/Article/CJFDTOTAL-ZAQK201212010.htm
[23]	李祚泳, 徐源蔚, 汪嘉杨, 等.基于前向神经网络的广义环境系统评价普适模型.环境科学学报, 2015, 35(9):2996-3005. http://www.cnki.com.cn/Article/CJFDTOTAL-HJXX201509043.htm
[24]	陈仕鸿, 刘晓庆.基于离散型Hopfield神经网络的台风灾情评估模型.自然灾害学报, 2011, 20(5):47-52. http://www.cnki.com.cn/Article/CJFDTOTAL-ZRZH201105006.htm
[25]	李家启, 秦健, 李良福, 等.雷电灾害评估及其等级划分.西南大学学报:自然科学版, 2010, 32(11):140-144. http://www.cnki.com.cn/Article/CJFDTOTAL-XNND201011028.htm