广西台风直接经济损失预估模型研究

doi:10.11978/2024191

热带海洋学报 ›› 2025, Vol. 44 ›› Issue (4): 56-66.doi: 10.11978/2024191

广西台风直接经济损失预估模型研究

黄兆泳¹(), 许贵林², 莫志明³()

1.南宁师范大学, 地理科学与规划学院, 广西南宁 530001
2.南宁师范大学, 北部湾环境演变与资源利用教育部重点实验室, 广西南宁 530001
3.广西财经学院, 经济与贸易学院, 广西南宁 530007

收稿日期:2024-10-14 修回日期:2024-11-08 出版日期:2025-07-10 发布日期:2025-07-31
通讯作者: 莫志明
作者简介:
黄兆泳(1999—), 男, 广西壮族自治区梧州市人, 硕士研究生。email: hzywangyi163@163.com
基金资助:
国家重点研发计划(2022YFD2401200); 国家自然科学基金区域创新发展联合基金重点项目(U20A20105); 广西科技重大专项(桂科)(AA22067072-5)

Research on a prediction model for direct economic losses caused by typhoons in Guangxi

HUANG Zhaoyong¹(), XU Guilin², MO Zhiming³()

1. School of Geography and Planning, Nanning Normal University, Nanning 530001, China
2. Key Laboratory of Environmental Change and Resources Use in Beibu Gulf, Ministry of Education, Nanning Normal University, Nanning 530001, China
3. Economic and Trade College, Guangxi University of Finance and Economics, Nanning 530007, China

Received:2024-10-14 Revised:2024-11-08 Online:2025-07-10 Published:2025-07-31
Contact: MO Zhiming
Supported by:
National Key Research and Development Program of China(2022YFD2401200); Key Project of Joint Fund for Regional Innovation and Development of National Natural Science Foundation of China(U20A20105); Major Science and Technology Projects in Guangxi(Guike)(AA22067072-5)

摘要/Abstract

摘要： 中国是全球受台风灾害影响最严重的国家之一, 而广西是中国受台风影响最严重的地区之一, 年均受到5次台风影响, 最高达9次。台风灾害给广西地区带来了巨大的损失, 严重阻碍了广西社会经济的高质量发展。开展台风灾害损失预评估模型研究, 对政府防灾减灾、灾后重建具有现实参考意义, 有助于当地社会经济高速发展。本研究选取2001—2020年影响广西并造成一定经济损失且有较完整记录的40个历史台风灾害事例, 从致灾因子、承灾体、防灾减灾能力3个指标层出发, 以 12个台风影响因子作为神经网络模型的输入要素, 使用4种神经网络结合三次样条插值法进行数据扩增构建了广西台风灾损智能预测模型, 通过比对4种神经网络训练集和测试集的性能表现筛选出最适用的模型, 并以台风“海鸥”“彩虹”和“暹芭”为例探索了该模型的实战应用潜力, 实现广西台风灾损动态预测。结果表明, 性能表现最好的为GA-BP (genetic algorithm-back propagation)神经网络模型, 模型训练集R²为0.9606、RMSE (root mean squared error)为1.0669、测试集R²为0.9073、RMSE为1.1635, 预测结果与实际台风灾损情况比较接近, 证明了该模型的有效性。

关键词: 台风灾害, 数据增强, 灾损预测, 神经网络, 广西

Abstract:

China is among the countries most severely impacted by typhoon disasters globally, with Guangxi being one of the regions most affected in China. Guangxi experiences an average of five typhoons annually, with a maximum of up to nine. These typhoon disasters have inflicted significant economic losses on the region, severely hindering the high-quality socioeconomic development of Guangxi. Developing pre-assessment models for typhoon disaster losses provides a practical reference for government efforts in disaster prevention, mitigation, and post-disaster reconstruction, thereby supporting the region's accelerated socioeconomic development. In this study, 40 historical typhoon disasters that impacted Guangxi between 2001 and 2020, with relatively complete records and measurable economic losses, were analyzed. From three index layers—disaster-causing factors, disaster-bearing entities, and disaster prevention and mitigation capacities—12 typhoon impact factors were selected as input variables for a neural network model. To enhance data robustness, four types of neural networks, combined with cubic spline interpolation, were employed to construct an intelligent prediction model for typhoon disaster damage in Guangxi. By comparing the performance of these four neural networks on training and testing datasets, the most suitable model was selected. The practical application potential of this model was explored using Typhoons "Kalmaegi," "Mujigae," and "Chaba" as case studies, enabling dynamic prediction of typhoon-induced losses in Guangxi. The results indicate that the GA-BP (genetic algorithm-back propagation) neural network model exhibits the best performance. For the training set, the model achieved an R² of 0.9606 and an RMSE of 1.0669. For the testing set, it achieved an R² of 0.9073 and an RMSE of 1.1635. These results demonstrate that the predictions closely align with the actual typhoon damages, validating the effectiveness of the model. The study highlights the model's potential for dynamic real-world prediction of typhoon damage in Guangxi.

Key words: typhoon disasters, data augmentation, loss prediction, neural networks, Guangxi

中图分类号:

P429

黄兆泳, 许贵林, 莫志明. 广西台风直接经济损失预估模型研究[J]. 热带海洋学报, 2025, 44(4): 56-66.

HUANG Zhaoyong, XU Guilin, MO Zhiming. Research on a prediction model for direct economic losses caused by typhoons in Guangxi[J]. Journal of Tropical Oceanography, 2025, 44(4): 56-66.

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数据类型	数据名称	数据来源
基础数据	广西行政区划数据	中国科学院地理科学与资源研究所、国家测绘地理信息局标准地图服务官网
基础数据	广西高程数据	中国科学院地理科学与资源研究所、国家测绘地理信息局标准地图服务官网
社会经济数据	广西社会经济统计数据	《广西统计年鉴》
台风相关数据	广西台风灾情数据	《中国气象灾害年鉴》《中国海洋灾害公报》《中国民政统计年鉴》《南海区海洋灾害公报》《广西海洋灾害公报》
	广西风雨数据	国家气象信息中心
	广西水文数据	项目团队自研的近40年北部湾高分辨率海洋灾害数据集

指标层	指标因子
致灾因子危险性	最大风速/(m·s^-1) (X₁)
	最大降雨量/mm (X₂)
	中心气压/hPa (X₃)
	最大增水/cm (X₄)
	总水位/m (X₅)
	有效波高/m (X₆)
	平均波长/cm (X₇)
	平均波周期/s (X₈)
承灾体脆弱性	人口密度/每平方千米人数 (X₉)
	经济密度/(10⁸ yuan·km^-2) (X₁₀)
	城镇化率/% (X₁₁)
	农作物播种面积/1000hm² (X₁₂)
	全区电力消费量/10⁸ kWh (X₁₃)
防灾减灾能力	每千人拥有病床数 (X₁₄)
	每千人拥有卫生技术人员数 (X₁₅)
	卫生机构数量 (X₁₆)
	境内公路里程/km (X₁₇)
	境内铁路营业里程/km (X₁₈)

指标	重要性程度
最大风速(X₁)	32.48%
农作物播种面积(X₁₂)	21.07%
中心气压(X₃)	9.02%
总水位(X₅)	8.02%
人口密度(X₉)	7.95%
最大降雨量(X₂)	5.26%
最大增水(X₄)	4.32%
卫生机构数量(X₁₆)	4.04%
境内铁路营业里程(X₁₈)	2.20%
每千人拥有病床数量(X₁₄)	1.28%
每千人拥有卫生技术人员数量(X₁₅)	1.23%
城镇化率(X₁₁)	0.76%
平均波长(X₇)	0.48%
平均波周期(X₈)	0.47%
经济密度(X₁₀)	0.44%
有效波高(X₆)	0.41%
全区电力消费量(X₁₃)	0.35%
境内公路里程(X₁₇)	0.22%

综合层	模型输入	模型输出
致灾因子危险性	最大降雨量	直接经济损失/亿元
	中心气压
	最大风速
	最大增水
	总水位
承灾体脆弱性	人口密度
	城镇化率
	农作物播种面积
防灾减灾能力	每千人拥有病床数量
	每千人拥有卫生技术人员数量
	卫生机构数量
	境内铁路营业里程

参数类型	值
遗传代数	10
种群规模	5
优化变量边界	[-1, 1]
选择函数参数	0.09
交叉函数参数	2
变异函数参数	[2 gen 3]

广西台风直接经济损失预估模型研究

Research on a prediction model for direct economic losses caused by typhoons in Guangxi

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图/表 16

参考文献 24

相关文章 15

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神经网络类型	训练集		测试集
神经网络类型	R²	RMSE	R²	RMSE
BP神经网络	0.9659	1.1185	-0.6589	20.9774
GA-BP神经网络	0.9458	2.3717	-0.2242	10.9302
卷积神经网络(CNN)	0.9839	1.0349	0.4827	14.9131
长短期记忆网络(LSTM)	0.9806	0.4110	-1.5828	8.8996

时刻	直接经济损失/亿元
时刻	海鸥	彩虹	暹芭
登陆前12小时	21.251	5.757	0.3108
登陆前8小时	23.116	6.867	0.6244
登陆前4小时	28.520	8.016	0.7127
灾后统计	27.930	9.190	0.7770

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神经网络类型	训练集		测试集
神经网络类型	R²	RMSE	R²	RMSE
BP神经网络	0.9572	1.5668	0.8843	2.7933
GA-BP神经网络	0.9606	1.0669	0.9073	1.1635
卷积神经网络(CNN)	0.9368	1.6186	0.8127	3.3191
长短期记忆网络(LSTM)	0.8672	3.5678	0.8053	4.3044