基于小波分析的我国台风风暴潮直接经济损失周期分析及预测

刘旭; 董剑希; 姜珊; 赵达君; 付翔; 王峥; 梁颖祺

doi:10.12284/hyxb2023109

基于小波分析的我国台风风暴潮直接经济损失周期分析及预测

doi: 10.12284/hyxb2023109

刘旭^{1, 2,},
董剑希¹,
姜珊¹,
赵达君³,
付翔¹,
王峥¹,
梁颖祺¹

1.
国家海洋环境预报中心，北京 100081
2.
北京林业大学经济管理学院，北京 100083
3.
新疆农业大学经济管理学院，新疆维吾尔自治区乌鲁木齐 830052

基金项目: 国家自然科学基金（41976221，41576029）；国家重点研发计划（2021YFB3900405）。

详细信息

作者简介:
刘旭（1986-），女，北京市人，博士，主要从事海洋灾害评估及系统分析。E-mail：fairyjujube@126.com

中图分类号: P458.1⁺24
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- 文章访问数: 190
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- 被引次数: 0
出版历程
- 收稿日期: 2022-08-31
- 修回日期: 2023-01-02
- 网络出版日期: 2023-08-08
- 刊出日期: 2023-07-01

Based on wavelet analysis of the direct economic loss of the typhoon storm surge in China cycle analysis and prediction

1.
National Marine Environmental Forecasting Center, Beijing 100081, China
2.
School of Economics & Management, Beijing Forestry University, Beijing 100083, China
3.
College of Economics and Management, Xinjiang Agricultural University, Urumqi 830052, China

摘要

摘要: 本文选取1989−2021年我国台风风暴潮直接经济损失统计数据，依据线性趋势法和Mann-Kendall非参数检验法进行分析，结果表明，32年间我国风暴潮灾害经济损失呈现显著下降趋势，整体呈厚尾分布特征，采用对数化处理后呈显著的正态分布特征。采用Morlet小波变换对我国台风风暴潮直接经济损失的周期变化规律进行分析，t检验结果显示，全域存在准两次高频振荡，1～2年及7～8年的周期振荡，但随时间变化年际周期逐渐缩短为3～5年，说明风暴潮经济损失序列存在高频振荡和多周期嵌套的低频振荡规律。在此基础上，采用Daubechies小波分解分离高频信号和低频信号，均方根误差和信噪比精度分析结果表明，当小波基设置消失矩为7，分解层数为2时，我国台风风暴潮直接经济损失时间序列具有最优分解重构效果。对各分解层进行小波系数平稳性检验和白噪声检验，建立的小波分解−ARMA组合模型的模拟精度和预测精度均优于传统的自回归移动平均模型和Fourier级数拓展模型，证明了小波分解法在我国台风风暴潮经济损失快速评估中具有可靠性和优越性。
- 风暴潮直接经济损失 /
- Mann-Kendall非参数检验 /
- Morlet小波变换 /
- Daubechies小波分解 /
- 自回归移动平均模型
Abstract: Based on the statistical data of direct economic losses of typhoon storm surge in China from 1989 to 2021, the economic losses of storm surge disasters in China during 32 years showed a significant downward trend, showing a thick-tailed distribution as a whole, and a normal distribution after logarithmic processing. The periodic changes of the direct economic losses of typhoon storm surge in China were analyzed by Morlet wavelet analysis method. According to the t-test, there were two quasi-high-frequency oscillations in the whole region, 1−2 years and 7−8 years oscillation , but the annual cycle gradually shortened to 3−5 years with the change of time. It indicated that the economic loss sequence of storm surge had high-frequency oscillation and multi-period nested low-frequency oscillation. On this basis, Daubechies wavelet decomposition was used to separate high frequency signal and low frequency signal. According to the results of root mean square error (RMSE) and signal-to-noise ratio, Daubechies wavelet base was set the vanishing moment is 7 and the number of decomposition layers is 2 for the direct economic loss time series of typhoon storm surges in China from 1989 to 2021, which had the optimal decomposition and reconstruction effect. Based on the results of stationarity test and white noise test of wavelet coefficients of each decomposition layer, the combined wavelet decomposed−ARMA model was established. The simulation accuracy and prediction accuracy were both better than that of single Autoregressive Integrated Moving Average model and Fourier series expansion model, which verified the reliability and superiority of wavelet decomposition method for rapid assessment of economic loss of typhoon storm surge in China.
- storm surge economic losses /
- Mann-Kendall nonparametric test /
- Morlet wavelet transform /
- Daubechies wavelet decomposition /
- autoregressive integrated moving average

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图 1 1989−2021年中国台风风暴潮直接经济损失时间序列

Fig. 1 Time series of direct economic losses of typhoon storm surges in China from 1989 to 2021

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图 2 频数分布

Fig. 2 Frequency distribution

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图 3 1989−2021年中国台风风暴潮直接经济损失周期分析图

a. 小波变换实部分布图；b. 小波能量谱图

Fig. 3 Cycle analysis of direct economic loss of typhoon storm surge in China from 1989 to 2021

a. Real part of wavelet transform; b. wavelet energy spectrum

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图 4 自相关性检验

Fig. 4 Autocorrelation test

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图 5 模型预测结果

Fig. 5 Model prediction results

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表 1 db2~db20小波分解重构信号误差

Tab. 1 Error of reconstructed signals by db2 to db20 wavelet decomposition

消失矩	harr	db4	db6	db8	db10	db12	db14	db16	db18	db20
均方根误差	234.1	84.4	76.9	151.4	187.8	133.5	1.3×10⁻⁹	1.3×10⁻⁹	1.3×10⁻⁹	1.3×10⁻⁹

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表 2 db7小波分解层数信噪比

Tab. 2 db7 wavelet decomposition layers signal-to-noise ratio

分解层数	1	2	3	4	5	6
信噪比	18.9	18.4	19.7	26.8	675.7	1291.8

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表 3 序列平稳性及白噪声显著性检验

Tab. 3 Series stationarity test and white noise significance test

检验方法	W1	V1	W2	V2	Y
ADF检验	0.01	0.01	0.01	0.01	0.01
PP检验	0.01	0.01	0.03	0.01	0.01
Q检验	0.02	0.64	0.30	0.19	0.00
结论	平稳非白噪声	平稳白噪声	平稳白噪声	平稳白噪声	平稳非白噪声

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表 4 ARMA模型参数检验

Tab. 4 ARMA model parameter test

	模型参数（p值）	结论
ARMA（1,1）	AR1 = −0.41(0.03)，MA1 = 0.12(0.55)，XMEAN = 85.07(0.00)	未通过
AR（2）	AR1 = −0.27(0.00)，AR2 = 0.06(0.43)，XMEAN = 85.09(0.00)	未通过
MA（2）	MA1 = −0.29(0.00)，MA2 = 0.19(0.00)，XMEAN = 85.13(0.00)	通过
AR（5）	AR1 = −0.61(0.00)， AR2 = −0.51(0.00)，AR3 = −0.44(0.00)，AR4 = −0.40(0.00)，AR5 = −0.19(0.01)	通过

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表 5 模型预测精度分析

Tab. 5 Analysis of model prediction accuracy

参数	Fourier级数拓展	小波分解	ARMA	Fourier级数拓展−ARMA	小波分解−ARMA
MSE	2469.13	1034.40	685.94	938.12	249.20
RMSE	49.69	32.16	26.19	30.63	15.79
MAE	46.82	21.01	18.77	25.05	6.40
ME	36.05	15.65	8.80	11.55	5.30

下载: 导出CSV

参考文献(37)

[1]	于福江, 董剑希, 李涛, 等. 风暴潮对我国沿海影响评价[M]. 北京: 海洋出版社, 2015. Yu Fujiang, Dong Jianxi, Li Tao, et al. Assessment of the Impact of Storm Surge on Coastal Areas in China[M]. Beijing: China Ocean Press, 2015.
[2]	卢美. 浙江海岸台风风暴潮漫堤风险评估研究[D]. 杭州: 浙江大学, 2013. Lu Mei. Study on risk assessment of seawall overflowed by typhoon storm surge at Zhejiang coast[D]. Hangzhou: Zhejiang University, 2013.
[3]	孔昊, 彭本荣, 刘容子, 等. 气候变化对中国海洋经济可持续发展的影响[J]. 海洋环境科学, 2018, 37(1): 116−124. Kong Hao, Peng Benrong, Liu Rongzi, et al. Impacts analysis of climate change on China’s marine economy[J]. Marine Environmental Science, 2018, 37(1): 116−124.
[4]	Wang Ke, Yang Yongsheng, Reniers G, et al. A study into the spatiotemporal distribution of typhoon storm surge disasters in China[J]. Natural Hazards, 2021, 108(1): 1237−1256. doi: 10.1007/s11069-021-04730-9
[5]	王凯. 台风影响下灾害性海洋动力过程时空特征及其对承灾体作用研究[D]. 青岛: 中国科学院大学(中国科学院海洋研究所), 2020. Wang Kai. Study on the spatial-temporal features of disastrous marine dynamic process and its effects on hazard-bearing body under the influence of typhoon[D]. Qingdao: University of Chinese Academy of Sciences (Institute of Oceanology, Chinese Academy of Science), 2020.
[6]	王思. 基于GIS技术和卷积神经网络的风暴潮灾害风险评估与区划研究[D]. 武汉: 中国地质大学, 2021. Wang Si. Risk assessment and zoning of storm surge disaster using GIS techniques and convolutional neural network[D]. Wuhan: China University of Geosciences, 2021.
[7]	孙海, 嵇文捷, 郑雅芝. 基于栅格与云模型的风暴潮洪水风险模拟评估方法——以珠海市香洲区为例[J]. 自然灾害学报, 2022, 31(1): 69−80. Sun Hai, Ji Wenjie, Zheng Yazhi. Storm surge flood risk simulation and evaluation method based on grid and cloud model: a case study of Xiangzhou District, Zhuhai City[J]. Journal of Natural Disasters, 2022, 31(1): 69−80.
[8]	Li Jian, Hou Yijun, Mo Dongxue, et al. Influence of tropical cyclone intensity and size on storm surge in the northern East China Sea[J]. Remote Sensing, 2019, 11(24): 3033. doi: 10.3390/rs11243033
[9]	Jiang Xinyu, Mori N, Tatano H, et al. Simulation-based exceedance probability curves to assess the economic impact of storm surge inundations due to climate change: a case study in Ise Bay, Japan[J]. Sustainability, 2019, 11(4): 1090. doi: 10.3390/su11041090
[10]	郭腾蛟, 李国胜. 风暴潮灾害经济损失灾前预评估研究进展[J]. 灾害学, 2018, 33(4): 164−168. Guo Tengjiao, Li Guosheng. Research progress on pre-assessment of economic losses before storm surge disasters[J]. Journal of Catastrophology, 2018, 33(4): 164−168.
[11]	殷克东, 韦茜, 李兴东. 风暴潮灾害社会经济损失评估研究[J]. 海洋环境科学, 2012, 31(6): 835−837, 842. Yin Kedong, Wei Qian, Li Xiongdong. The evaluation techniques of the socio-economic loss caused by storm surge disaster[J]. Marine Environmental Science, 2012, 31(6): 835−837, 842.
[12]	王甜甜, 刘强. 基于BAS-BP模型的风暴潮灾害损失预测[J]. 海洋环境科学, 2018, 37(3): 457−463. doi: 10.12111/j.cnki.mes20180323 Wang Tiantian, Liu Qiang. The assessment of storm surge disaster loss based on BAS-BP model[J]. Marine Environmental Science, 2018, 37(3): 457−463. doi: 10.12111/j.cnki.mes20180323
[13]	杨雪雪, 刘强. 基于KPCA-RBF模型的风暴潮灾害经济损失预测[J]. 海洋科学, 2021, 45(10): 32−39. Yang Xuexue, Liu Qiang. Economic loss assessment of storm-surge disasters based on the KPCA-RBF model[J]. Marine Sciences, 2021, 45(10): 32−39.
[14]	潘艳艳, 王涛, 赵昕. 风暴潮灾害损失时间路径模拟与预测[J]. 统计与决策, 2016, 449(5): 87−89. doi: 10.13546/j.cnki.tjyjc.2016.05.023 Pan Yanyan, Wang Tao, Zhao Xin. Time path simulation and prediction of storm surge disaster loss[J]. Statistics and Decision, 2016, 449(5): 87−89. doi: 10.13546/j.cnki.tjyjc.2016.05.023
[15]	孙丰霖. 中国沿海赤潮灾害时间序列特征的模拟与预测[J]. 海洋通报, 2021, 40(2): 232−240. Sun Fenglin. Simulation and forecast of the red tide’s time series characteristics in China seas[J]. Marine Science Bulletin, 2021, 40(2): 232−240.
[16]	李超超, 田军仓, 申若竹. 洪涝灾害风险评估研究进展[J]. 灾害学, 2020, 35(3): 131−136. Li Chaochao, Tian Juncang, Shen Ruozhu. Review on assessment of flood and waterlogging risk[J]. Journal of Catastrophology, 2020, 35(3): 131−136.
[17]	Taramelli A, Valentini E, Sterlacchini S. A GIS-based approach for hurricane hazard and vulnerability assessment in the Cayman Islands[J]. Ocean & Coastal Management, 2015, 108: 116−130.
[18]	冯倩, 刘强. 基于SVM-BP神经网络的风暴潮灾害损失预评估[J]. 海洋环境科学, 2017, 36(4): 615−621. Feng Qian, Liu Qiang. Pre-assessment for the loss caused by storm surge based on the SVM-BP neural network[J]. Marine Environmental Science, 2017, 36(4): 615−621.
[19]	郝婧, 刘强. 基于SSA-ELM模型的台风风暴潮灾害损失预评估[J]. 海洋科学, 2022, 46(2): 55−63. Hao Jing, Liu Qiang. Pre-assessment of typhoon storm surge disaster loss based on the SSA-ELM model[J]. Marine Sciences, 2022, 46(2): 55−63.
[20]	张颖超, 范金平, 邓华. 基于组合预测的浙江省台风灾害损失预测[J]. 自然灾害学报, 2013, 22(6): 223−231. doi: 10.13577/j.jnd.2003.0630 Zhang Yingchao, Fan Jinping, Deng Hua. Forecasting of typhoon disaster loss in Zhejiang Province based on the combination forecasting model[J]. Journal of Natural Disasters, 2013, 22(6): 223−231. doi: 10.13577/j.jnd.2003.0630
[21]	郭腾蛟, 李国胜. 基于验证性因素分析的台风风暴潮灾害经济损失影响因子优化分析[J]. 自然灾害学报, 2020, 29(1): 121−131. Guo Tengjiao, Li Guosheng. The optimal analysis of the impact factors of economic losses due to typhoon storm surge based on confirmatory factor analysis[J]. Journal of Natural Disasters, 2020, 29(1): 121−131.
[22]	袁顺, 赵昕, 南旭, 等. 结构突变视角下政府灾害支出的平稳协调机制[J]. 海洋环境科学, 2016, 35(1): 35−40. Yuan Shun, Zhao Xin, Nan Xu, et al. Coordination mechanism between coastal financial expenditure and disaster losses based on structural change[J]. Marine Environmental Science, 2016, 35(1): 35−40.
[23]	Yi Xiaojing, Sheng Kun, Wang Yuanyue, et al. Can economic development alleviate storm surge disaster losses in coastal areas of China?[J]. Marine Policy, 2021, 129: 104531. doi: 10.1016/j.marpol.2021.104531
[24]	张丽旭, 赵敏, 蒋晓山. 中国赤潮发生频率的变化趋势及其多发年份的R/S预测[J]. 海洋通报, 2010, 29(1): 72−77. doi: 10.3969/j.issn.1001-6392.2010.01.011 Zhang Lixu, Zhao Min, Jiang Xiaoshan. The change trend of happened frequency and the R/S forecast of frequently happened year for red tide in China[J]. Marine Science Bulletin, 2010, 29(1): 72−77. doi: 10.3969/j.issn.1001-6392.2010.01.011
[25]	高强, 谷文凯, 林亚琼, 等. 山东省风暴潮灾害经济损失预测——基于灰色−周期外延组合模型[J]. 海洋经济, 2016, 6(1): 46−51. Gao Qiang, Gu Wenkai, Lin Yaqiong, et al. The financial loss prediction of storm surge in Shandong Province based on the gray-periodic extensional combinatorial model[J]. Marine Economy, 2016, 6(1): 46−51.
[26]	刘旭, 梁颖祺, 王兆毅, 等. 基于海温因子的传递函数模型在黄海绿潮规模预测中的应用[J]. 海洋预报, 2022, 39(4): 91−101. doi: 10.11737/j.issn.1003-0239.2022.04.010 Liu Xu, Liang Yingqi, Wang Zhaoyi, et al. Application of transfer function model in predicting the green tide scale in the Yellow Sea based on sea surface temperature[J]. Marine Forecasts, 2022, 39(4): 91−101. doi: 10.11737/j.issn.1003-0239.2022.04.010
[27]	李威骏. 基于小波分析的股票市场异常波动及风险测度研究[D]. 成都: 西南财经大学, 2019. Li Weijun. Research on abnormal shocks of stock market and risk measurement based on wavelet analysis[D]. Chengdu: Southwestern University of Finance and Economics, 2019.
[28]	孙世超, 董曜, 李娜, 等. 基于小波分解的集卡港内周转时间预测[J]. 上海海事大学学报, 2021, 42(3): 8−14. Sun Shichao, Dong Yao, Li Na, et al. Truck turnaround time prediction in a port based on wavelet decomposition[J]. Journal of Shanghai Maritime University, 2021, 42(3): 8−14.
[29]	王晓亮. 基于时间序列分析理论的风速短期预测方法研究[D]. 北京: 华北电力大学, 2018. Wang Xiaoliang. Research on short-term wind speed forecasting method based on time series analysis theory[D]. Beijing: North China Electric Power University, 2018.
[30]	王淋淋. 珠江口海平面的变化机制及对沿岸淹没风险研究[D]. 北京: 清华大学, 2017. Wang Linlin. The mechanism of sea level changes in Pearl River Estuary and the assessment of coastal flooding risk[D]. Beijing: Tsinghua University, 2017.
[31]	孙鹤泉, 金绍华, 张宇. 基于MODWT变换的海洋重力观测航行数据滤波方法[J]. 海洋通报, 2020, 39(4): 426−430. Sun Hequan, Jin Shaohua, Zhang Yu. MODWT transform applied to navigation data filtering in ocean gravity observation[J]. Marine Science Bulletin, 2020, 39(4): 426−430.
[32]	邹磊, 夏军, 张印, 等. 海河流域降水时空演变特征及其驱动力分析[J]. 水资源保护, 2021, 37(1): 53−60. Zou Lei, Xia Jun, Zhang Yin, et al. Spatial-temporal change characteristics and driving forces of precipitation in the Haihe River Basin[J]. Water Resources Protection, 2021, 37(1): 53−60.
[33]	王晓利. 中国沿海极端气候变化及其对NDVI的影响特征研究[D]. 烟台: 中国科学院烟台海岸带研究所, 2017. Wang Xiaoli. Variation of extreme climate and its impact on NDVI in the coastal area of China[D]. Yantai: Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, 2017.
[34]	方杰. 基于小波分析的人体血压预测[D]. 武汉: 华中科技大学, 2017. Fang Jie. Prediction of human blood pressure based on wavelet analysis[D]. Wuhan: Huazhong University of Science & Technology, 2017.
[35]	Fang Jiayi, Liu Wei, Yang Saini, et al. Spatial-temporal changes of coastal and marine disasters risks and impacts in Mainland China[J]. Ocean & Coastal Management, 2017, 139: 125−140.
[36]	张晓影, 叶彬, 刘海婧, 等. 基于小波功率谱和Anusplin的江苏省近58年来降水时空变化分析[J]. 水电能源科学, 2022, 40(1): 6−9. Zhang Xiaoying, Ye Bin, Liu Haijing, et al. Spatial and temporal variation of precipitation in Jiangsu Province in recent 58 years based on wavelet power spectrum and Anusplin[J]. Water Resources and Power, 2022, 40(1): 6−9.
[37]	陈志伟. 西太热带气旋的气候特征及对海洋热状态响应机理的研究[D]. 上海: 上海师范大学, 2019. Chen Zhiwei. Climatic characteristics of the Northwest Pacific tropical cyclone and its response mechanism with the thermal status of ocean[D]. Shanghai: Shanghai Normal University, 2019.