基于人群生活模式的房屋震毁致死率估计

doi:10.16265/j.cnki.issn1003-3033.2023.11.0500

中国安全科学学报 ›› 2023, Vol. 33 ›› Issue (11): 165-173.doi: 10.16265/j.cnki.issn1003-3033.2023.11.0500

基于人群生活模式的房屋震毁致死率估计

江雯¹(), 孟耀斌²^,³^,^**, 张东妮⁴, 张媛⁵, 吴佳馨¹

¹ 北京师范大学地理科学学部,北京 100875
² 北京师范大学国家安全与应急管理学院,北京 100875
³ 北京师范大学教育部巨灾模拟与系统性风险应对国际合作联合实验室,广东珠海 519087
⁴ 中国邮政集团综合部(安全保卫部),北京 100875
⁵ 北京机械设备研究所,北京 100875

收稿日期:2023-05-14 修回日期:2023-08-18 出版日期:2023-11-28
通讯作者:
**孟耀斌(1974—),男,山西五台人,博士,副教授,主要从事暴露分析与模拟等方面的研究。E-mail: yaobin-meng@bnu.edu.cn。
作者简介:
江雯 (1999—),女,安徽歙县人,硕士研究生,主要研究方向为暴露模拟与风险量化。E-mail: jwen981@163.com。
孟耀斌副教授
基金资助:
国家重点研发计划课题项目(2022YFC3004404)

Estimating fatality rate of house collapse based on human activity pattern

JIANG Wen¹(), MENG Yaobin²^,³^,^**, ZHANG Dongni⁴, ZHANG Yuan⁵, WU Jiaxin¹

¹ Faculty of Geographic Science, Beijing Normal University, Beijing 100875, China
² School of National Safety and Emergency Management, Beijing Normal University, Beijing 100875, China
³ Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, Beijing Normal University,Zhuhai Guangdong 519087, China
⁴ Comprehensive Department (Security Department), China Post Group, Beijing 100875, China
⁵ Beijing Institute of Mechanical Equipment, Beijing 100875, China

Received:2023-05-14 Revised:2023-08-18 Published:2023-11-28

摘要/Abstract

摘要：

为提高突发性灾害中人员伤亡快速估计的精度,识别不同空间状态下人员死亡率的差异,基于村镇居民生活模式调查(HAPRRES)数据和由此建立的人群生活模式参数模型库,仿真估计2005—2018年我国13场地震发生时人员空间状态分布,优化获取地震毁坏房屋内睡眠与清醒状态人员的死亡率模型。分析结果表明:毁坏房屋中入睡者的死亡率(5.16‰)是清醒者死亡率(0.85‰)的6倍;土坯房毁坏时,入睡者的死亡率(1.60%)是清醒者死亡率(1.34‰)的11.9倍,而非土坯房毁坏时入睡者的死亡率(2.35‰)是清醒者死亡率(0.54‰)的4.3倍。文中获得的死亡率模型具有较好的稳健性。

关键词: 人群生活模式, 地震, 房屋损毁, 死亡率, 土坯房

Abstract:

Efficient estimation of casualties in earthquake scenarios involves the identification of mortality rate variations among individuals in different spatial states, which may be achieved through the simulation and replication of population spatial distributions during earthquakes. Based on the human activity pattern model set which was generated from the China Human Activity Pattern of Rural Residents Survey (HAPRRES), we simulated individuals' activities during 13 earthquakes in China from 2005 to 2018 to estimate the spatial state snapshot distributions of the earthquake affected areas at their onset moment. These estimates allowed the optimization of mortality rate models by distinguishing between sleeping and awake people in earthquake-damaged houses. The optimized mortality models reveal that the mortality rate for individuals asleep (5.16‰) is six times higher than that for individuals awake (0.85‰) in seismically collapsed houses. Notably, when the adobe houses are destroyed, the mortality rate of sleeping people (1.60%) is 11.9 times higher than that for the awake people (1.34‰). Conversely, during the destruction of non-adobe houses, the mortality rate for the sleeping people (2.35‰) is 4.3 times higher than that of the awake people (0.54‰). An uncertainty analysis proves the robustness of the mortality rate model. As this explorative research demonstrates, incorporating human activity pattern model will help to improve the rapid estimation of casualties of sudden disasters.

Key words: human activity pattern, earthquake, building damage, mortality rate, adobe house

江雯, 孟耀斌, 张东妮, 张媛, 吴佳馨. 基于人群生活模式的房屋震毁致死率估计[J]. 中国安全科学学报, 2023, 33(11): 165-173.

JIANG Wen, MENG Yaobin, ZHANG Dongni, ZHANG Yuan, WU Jiaxin. Estimating fatality rate of house collapse based on human activity pattern[J]. China Safety Science Journal, 2023, 33(11): 165-173.

图/表 6

图1

表1

用于建模的震害数据集及用于验证模型的“6·17”长宁地震

研究区	时间	房屋毁损致死人数	房屋震害面积/m²			4状态分布比例				权重值
研究区	时间	房屋毁损致死人数	毁坏	严重破坏	合计	室外	室内	睡眠	工作	权重值
新疆维吾尔自治区塔什库尔干县	2017-05-11 T5:58	3	361 568	151 041	967 889	0.02	0.18	0.79	0.01	0.63
新疆维吾尔自治区皮山县^▲	2015-07-03 T9:07	3	1 368 665	474 726	5 779 090	0.04	0.38	0.02	0.56	0.65
甘肃省岷县VIII^*▲1	2013-07-22 T7:45	70	38	41	100	0.05	0.48	0.06	0.41	0.87
四川省芦山县^**	2013-04-20 T8:02	39	30	95	174	0.04	0.45	0.11	0.40	0.77
江苏省高邮市	2012-07-20 T20:11	1	438	2 679	388 303	0.05	0.61	0.02	0.32	0.86
云南省宁蒗县	2012-06-24 T15:59	3	252 990	1 407 374	1 660 364	0.12	0.47	0.00	0.41	0.86
四川省盐源县	2012-06-24 T15:59	1	252 990	1 407 374	1 660 364	0.09	0.54	0.00	0.36	0.90
云南省盈江县^▲	2011-03-10 T12:58	18	21 800	59 150	87 490	0.05	0.55	0.00	0.39	0.80
重庆市荣昌县^▲	2009-08-08 T21:26	2	148 256	593 025	3 991 079	0.08	0.65	0.20	0.07	0.90
云南省姚安县^▲	2009-07-09 T19:19	1	472 075	1 831 272	4 259 828	0.06	0.51	0.00	0.43	0.78
西藏自治区当雄县^▲	2008-10-06 T16:30	9	45 625	133 457	324 671	0.07	0.43	0.00	0.50	0.66
四川省盐边县^▲	2008-08-30 T16:30	1	172 955	909 462	2 669 345	0.07	0.55	0.00	0.38	0.72
四川省米易县^▲	2008-08-30 T16:30	1	39 456	764 577	7 306 300	0.06	0.57	0.00	0.37	0.88
四川省会理县^▲1	2008-08-30 T16:30	22	1 113 369	2 498 851	11 199 047	0.06	0.57	0.00	0.37	0.90
四川省仁和区	2008-08-30 T16:30	1	1 232 704	3 155 838	12 786 630	0.06	0.57	0.00	0.37	0.80
云南省盐津县^▲	2006-07-22 T9:10	2	98 750	574 465	2 340 845	0.07	0.46	0.04	0.42	0.94
云南省大关县^▲	2006-07-22 T9:10	2	169 393	947 677	3 933 136	0.06	0.47	0.04	0.42	0.92
江西省九江县^▲	2005-11-26 T8:49	5	338 767	1 837 879	6 060 438	0.06	0.43	0.07	0.44	0.83
江西省瑞昌市^▲	2005-11-26 T8:49	5	680 246	4 218 809	15 762 185	0.05	0.40	0.09	0.46	0.90
广西壮族自治区平果县	2005-10-27 T19:18	1	805	10 818	43 827	0.06	0.48	0.00	0.46	0.93
四川省宜宾市2	2019-06-17 T22:55	12	3 545	81 284	225 698	0.05	0.12	0.77	0.06	-

表1

表2

死亡率建模结果

模型编号	模型参数				优选模型	AIC	R²
模型编号	X₁	X₂	X₃	X₄	优选模型	AIC	R²
1	毁坏房屋内睡眠人数	毁坏房屋内清醒人数	—	—	$y = 5.16 × 10 - 3 · X 1 + 8.54 × 10 - 4 · X 2 (± 9.92 × 10 - 4) (± 1.01 × 10 - 4)$	235.99	0.47
2	毁坏房屋内人数	—	—	—	$y = 1.20 × 10 - 3 · X 1 (± 8.47 × 10 - 5)$	258.26	0.39
3	毁坏的土坯房内睡眠人数	毁坏的土坯房内清醒人数	毁坏的非土坯房内睡眠人数	毁坏的非土坯房内清醒人数	$y = 1.60 × 10 - 2 · X 1 + 1.34 × 10 - 3 · X 2 + 2.35 × 10 - 3 · X 3 + 5.44 × 10 - 4 · X 4 (± 4.06 × 10 - 3) (± 2.95 × 10 - 4) (± 1.06 × 10 - 3) (± 1.09 × 10 - 4)$	155.40	0.77
4	毁坏的土坯房内睡眠人数	毁坏的土坯房内清醒人数	毁坏的非土坯房内人数	—	$y = 1.96 × 10 - 2 · X 1 + 1.17 × 10 - 3 · X 2 + 5.66 × 10 - 4 · X 3 (± 3.67 × 10 - 3) (± 2.80 × 10 - 4) (± 1.85 × 10 - 4)$	160.57	0.77

表2

表3

人口空间分布加权的死亡率模型

模型编号	优选模型参数				优选模型	AIC	R²
模型编号	X₁	X₂	X₃	X₄	优选模型	AIC	R²
5	毁坏房屋内睡眠人数	毁坏房屋内清醒人数	—	—	$y = 5.12 × 10 - 3 · X 1 + 8.64 × 10 - 4 · X 2 (± 1.12 × 10 - 3) (± 1.14 × 10 - 4)$	234.96	0.47
6	毁坏的土坯房内睡眠人数	毁坏的土坯房内清醒人数	毁坏的非土坯房内睡眠人数	毁坏的非土坯房内清醒人数	$y = 1.61 × 10 - 2 · X 1 + 1.34 × 10 - 3 · X 2 + 2.20 × 10 - 3 · X 3 + 5.31 × 10 - 4 · X 4 (± 4.34 × 10 - 3) (± 3.10 × 10 - 4) (± 1.16 × 10 - 3) (± 1.16 × 10 - 4)$	154.36	0.77

表3

表4

表5

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不确定性类型	房屋结构参数	是否采用权重修正	仿真选中优选模型次数	仿真选中次优模型次数
土坯房占比	土坯房与否	否	108	63
土坯房占比	土坯房与否	是	107	75
死亡人数	不考虑	否	200	0
	不考虑	是	200	0
	土坯房与否	否	200	0
	土坯房与否	是	200	0

基于人群生活模式的房屋震毁致死率估计

Estimating fatality rate of house collapse based on human activity pattern

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模型编号	X₁	X₂	X₃	X₄	模型计算死亡/人	实际死亡/人	准确率/%
1	4 301.21	670.32	—	—	23	12	52.2
3	430.12	67.03	3 871.09	603.29	16	12	75.0

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