Knowledge-guided dynamic generation of escape route networks for forest fires

doi:10.11947/j.AGCS.2024.20230234

Abstract

Abstract:

A reasonably planned forest fire escape road network plays an important role in emergency escape decision-making, but the existing methods have weak dynamic adaptability and do not consider the key spatial information affecting people's escape safety such as ravine areas and narrow ridges, resulting in poor accuracy of escape road network planning. Therefore, this paper introduces intelligent mapping technology methods and proposes a knowledge-guided dynamic generation method of forest fire escape road network, by breaking through the key technologies of forest fire escape road network planning knowledge map construction, key spatial region extraction, et al. Then, it establishes a forest fire escape road network access raster network model, realizes the dynamic optimization generation of escape road network with the improvement of the A^* algorithm, develops a prototype system and carries out experimental analysis. The results show that the method in this paper can realize the dynamic generation of escape road network under the environment of forest fire spreading, which can provide effective escape decision-making information for the fire fighters. Compared with the existing static forest fire escape road network planning methods, the accuracy of escape planning improves the overlap rate of the high safety zone by 3.06%, and the overlap rate of the hazardous zone of the escape network reduces by 27.39%.

Key words: forest fire, knowledge-guided, key spatial information, escape route network, dynamic optimization

CLC Number:

P208

Jun ZHU, Peijing CHEN, Chao ZENG, Quanhong ZHENG, Yakun XIE, Jigang YOU, Huijie LIAN. Knowledge-guided dynamic generation of escape route networks for forest fires[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(6): 1086-1097.

Figures/Tables 17

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Tab.1

Fig.8

Tab.2

Tab.3

Fig.9

Tab.4

Fig.10

Fig.11

Tab.5

Tab.6

References 41

[1]	HUANG Chengquan, GONG Weishu, PANG Yong. Remote sensing and forest carbon monitoring—a review of recent progress, challenges and opportunities[J]. The Journal of Geodesy and Geoinformation Science, 2022, 5(2):124-147.
[2]	BOWMAN D M J S, WILLIAMSON G J, ABATZOGLOU J T, et al. Human exposure and sensitivity to globally extreme wildfire events[J]. Nature Ecology & Evolution, 2017, 1(3):58.
[3]	郑忠. 林火风险、过程与评估遥感模型与方法研究[J]. 测绘学报, 2019, 48(1):133. DOI:10.11947/j.AGCS.2019.20180023.
	ZHENG Zhong. Study on the risk, spread and assessment of forest fire based on the model and remote sensing[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(1):133. DOI:10.11947/j.AGCS.2019.20180023.
[4]	苏立娟, 何友均, 陈绍志. 1950—2010年中国森林火灾时空特征及风险分析[J]. 林业科学, 2015, 51(1):88-96.
	SU Lijuan, HE Youjun, CHEN Shaozhi. Temporal and spatial characteristics and risk analysis of forest fires in China from 1950 to 2010[J]. Scientia Silvae Sinicae, 2015, 51(1):88-96.
[5]	韩李涛, 郭欢, 张海思. 一种多出口室内应急疏散路径规划算法[J]. 测绘科学, 2018, 43(12):105-110.
	HAN Litao, GUO Huan, ZHANG Haisi. An algorithm for route planning applied in multi-exit indoor emergency evacuation[J]. Science of Surveying and Mapping, 2018, 43(12):105-110.
[6]	丁雨淋, 何小波, 朱庆, 等. 实时威胁态势感知的室内火灾疏散路径动态优化方法[J]. 测绘学报, 2016, 45(12):1464-1475.DOI:10.11947/j.AGCS.2016.20160053.
	DING Yulin, HE Xiaobo, ZHU Qing, et al. A dynamic optimization method of indoor fire evacuation route based on realtime situation awareness[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(12):1464-1475. DOI:10.11947/j.AGCS.2016.20160053.
[7]	SIAM M R K, WANG Haizhong, LINDELL M K, et al. An interdisciplinary agent-based multimodal wildfire evacuation model: critical decisions and life safety[J]. Transportation Research Part D: Transport and Environment, 2022, 259(4):660-684.
[8]	魏书精, 罗碧珍, 李小川, 等. 森林火灾导致人员伤亡的原因剖析及应对措施[J]. 森林防火, 2019 (3):15-17, 34.
	WEI Shujing, LUO Bizhen, LI Xiaochuan, et al. Analysis of the causes of casualties caused by forest fires and the associated countermeasures[J]. Forest Fire Prevention, 2019 (3):15-17, 34.
[9]	陈军, 艾廷华, 闫利, 等. 智能化测绘的混合计算范式与方法研究[J/OL]. 测绘学报: 1-19 [2024-05-14]. http://kns.cnki.net/kcms/detail/11.2089.P.20240415.1049.002.html.
	CHEN Jun, AI Tinghua, YAN Li, et al. Hybrid computational paradigm and methods for intelligentized surveying and mapping[J/OL]. Acta Geodaetica et Cartographica Sinica: 1-19 [2024-05-14]. http://kns.cnki.net/kcms/detail/11.2089.P.20240415.1049.002.html.
[10]	林珲, 游兰, 胡传博, 等. 时空大数据时代的地理知识工程展望[J]. 武汉大学学报(信息科学版), 2018, 43(12):2205-2211.
	LIN Hui, YOU Lan, HU Chuanbo, et al. Prospect of geo-knowledge engineering in the era of spatio-temporal big data[J]. Geomatics and Information Science of Wuhan University, 2018, 43(12):2205-2211.
[11]	朱军, 付林, 李维炼, 等. 知识引导的滑坡灾害场景动态表达方法[J]. 武汉大学学报(信息科学版), 2020, 45(8):1255-1262.
	ZHU Jun, FU Lin, LI Weilian, et al. Knowledge-guided dynamic representation method of landslide disaster scene[J]. Geomatics and Information Science of Wuhan University, 2020, 45(8):1255-1262.
[12]	朱庆, 苗双喜, 丁雨淋, 等. 知识引导的滑坡监测数据粗差定位与剔除方法[J]. 武汉大学学报(信息科学版), 2017, 42(4):496-502.
	ZHU Qing, MIAO Shuangxi, DING Yulin, et al. Knowledge-guided gross errors detection and elimination approach of landslide monitoring data[J]. Geomatics and Information Science of Wuhan University, 2017, 42(4):496-502.
[13]	李彦胜, 武康, 欧阳松, 等. 地学知识图谱引导的遥感影像语义分割[J]. 遥感学报, 2024, 28(2):455-469.
	LI Yansheng, WU Kang, OUYANG Song, et al. Geographic knowledge graph-guided remote sensing image semantic segmentation[J]. National Remote Sensing Bulletin, 2024, 28(2):455-469.
[14]	龚健雅, 许越, 胡翔云, 等. 遥感影像智能解译样本库现状与研究[J]. 测绘学报, 2021, 50(8):1013-1022. DOI:10.11947/j.AGCS.2021.20210085.
	GONG Jianya, XU Yue, HU Xiangyun, et al. Status analysis and research of sample database for intelligent interpretation of remote sensing image[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8):1013-1022. DOI:10.11947/j.AGCS.2021.20210085.
[15]	刘俊楠, 刘海砚, 陈晓慧, 等. 面向多源地理空间数据的知识图谱构建[J]. 地球信息科学学报, 2020, 22(7):1476-1486.
	LIU Junnan, LIU Haiyan, CHEN Xiaohui, et al. The construction of knowledge graph towards multi-source geospatial data[J]. Journal of Geo-Information Science, 2020, 22(7):1476-1486.
[16]	刘浩, 薛梅. 虚拟地理环境下的地理空间认知初步探索[J]. 遥感学报, 2021, 25(10):2027-2039.
	LIU Hao, XUE Mei. Exploring the use of virtual geographic environments for geo-spatial cognition research[J]. National Remote Sensing Bulletin, 2021, 25(10):2027-2039.
[17]	韩冰. 扑救森林火灾作战中紧急避险问题研究[D]. 北京: 北京林业大学, 2017.
	HAN Bing. Research on the problem of emergency refuge in forest fire fighting[D]. Beijing: Beijing Forestry University, 2017.
[18]	马玉春, 赵彦飞. 森林灭火安全防范与紧急避险探讨[J]. 消防科学与技术, 2021, 40(1):5-7.
	MA Yuchun, ZHAO Yanfei. Discussion on safety precaution and emergency avoidance of forest fire fighting[J]. Fire Science and Technology, 2021, 40(1):5-7.
[19]	ZHAO Xilei, LOVREGLIO R, KULIGOWSKI E, et al. Using artificial intelligence for safe and effective wildfire evacuations[J]. Fire Technology, 2021, 57(2):483-485.
[20]	LI Dapeng, COVA T J, DENNISON P E. Setting wildfire evacuation triggers by coupling fire and traffic simulation models: a spatiotemporal GIS approach[J]. Fire Technology, 2019, 55(2):617-642.
[21]	KAMILARIS A, FILIPPI J B, PADUBIDRI C, et al. Examining the potential of mobile applications to assist people to escape wildfires in real-time[J]. Fire Safety Journal, 2023, 136:103747.
[22]	陈玥璐, 赵天忠, 武刚, 等. 林区步行最优路径分析方法[J]. 农业机械学报, 2018, 49(6):198-206.
	CHEN Yuelu, ZHAO Tianzhong, WU Gang, et al. Optimal walking path analysis method for forest region[J]. Transactions of the Chinese Society for Agricultural Machinery, 2018, 49(6):198-206.
[23]	冯世盛, 徐青, 朱新铭, 等. 基于地形数据的长距离越野路径快速规划方法研究[J]. 地球信息科学学报, 2022, 24(9):1742-1754.
	FENG Shisheng, XU Qing, ZHU Xinming, et al. Fast method for long-distance off-road path planning based on terrain data[J]. Journal of Geo-Information Science, 2022, 24(9):1742-1754.
[24]	文世熙, 张贵, 吴鑫. 森林火灾逃生路径网络决策研究[J]. 中南林业科技大学学报, 2016, 36(9):62-65.
	WEN Shixi, ZHANG Gui, WU Xin. A decision-making study about multi-escaperoute network generating in forest fire[J]. Journal of Central South University of Forestry & Technology, 2016, 36(9):62-65.
[25]	HAN Bing, QU Tengteng, TONG Xiaochong, et al. Grid-optimized UAV indoor path planning algorithms in a complex environment[J]. International Journal of Applied Earth Observation and Geoinformation, 2022, 111:102857.
[26]	苗润龙, 庞硕, 姜大鹏, 等. 海洋自主航行器多海湾区域完全遍历路径规划[J]. 测绘学报, 2019, 48(2):256-264. DOI:10.11947/j.AGCS.2019.20180385.
	MIAO Runlong, PANG Shuo, JIANG Dapeng, et al. Complete coverage path planning for autonomous marine vehicle used in multi-bay areas[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(2):256-264. DOI:10.11947/j.AGCS.2019.20180385.
[27]	游天, 王光霞, 吕晓华, 等. 一种面向室内导航的通行区域模型及其自动提取算法[J]. 武汉大学学报(信息科学版), 2019, 44(2):177-184.
	YOU Tian, WANG Guangxia, LÜ Xiaohua, et al. Traversable region model and its automatic extraction algorithm for indoor navigation[J]. Geomatics and Information Science of Wuhan University, 2019, 44(2):177-184.
[28]	WANG Han, ZHANG Hongjun, WANG Kun, et al. Off-road path planning based on improved ant colony algorithm[J]. Wireless Personal Communications, 2018, 102(2):1705-1721.
[29]	胡平志, 杨小柳, 李泽滔. 复杂山地环境下的机器人路径规划[J]. 计算机仿真, 2021, 38(3):286-291.
	HU Pingzhi, YANG Xiaoliu, LI Zetao. Path planning of robots in complex terrain[J]. Computer Simulation, 2021, 38(3):286-291.
[30]	SARANYA C, UNNIKRISHNAN M, ALI S A, et al. Terrain based D algorithm for path planning[J]. IFAC-Papers OnLine, 2016, 49(1):178-182.
[31]	伍跃飞, 李建微, 毕胜, 等. 面向山地徒步应急救援路径规划的改进蚁群算法研究[J]. 地球信息科学学报, 2023, 25(1):90-101.
	WU Yuefei, LI Jianwei, BI Sheng, et al. Research on improved ant colony algorithm for mountain hiking emergency rescue path planning[J]. Journal of Geo-Information Science, 2023, 25(1):90-101.
[32]	赵璠, 舒立福, 周汝良, 等. 林火行为蔓延模型研究进展[J]. 世界林业研究, 2017, 30(2):46-50.
	ZHAO Fan, SHU Lifu, ZHOU Ruliang, et al. A review of wildland fire spread modelling[J]. World Forestry Research, 2017, 30(2):46-50.
[33]	NOY N F, MCGUINNESS D L. Ontology development 101: a guide to creating your first ontology[J]. Knowledge Systems Laboratory, 2001, 32(1).
[34]	康鑫, 王彦文, 秦承志, 等. 多分析尺度下综合判别的地形元素分类方法[J]. 地理研究, 2016, 35(9):1637-1646.
	KANG Xin, WANG Yanwen, QIN Chengzhi, et al. A new method of landform element classification based on multi-scale morphology[J]. Geographical Research, 2016, 35(9):1637-1646.
[35]	CAMPBELL M J, DENNISON P E, BUTLER B W. A LiDAR-based analysis of the effects of slope, vegetation density, and ground surface roughness on travel rates for wildland firefighter escape route mapping[J]. International Journal of Wildland Fire, 2017, 26(10):884.
[36]	HART P E, NILSSON N J, RAPHAEL B. A formal basis for the heuristic determination of minimum cost paths[J]. IEEE Transactions on Systems Science and Cybernetics, 1968, 4(2):100-107.
[37]	王琳, 武虹. 基于DEM的遗址域定量算法及可获取耕地统计[J]. 遥感技术与应用, 2017, 32(2):274-281.
	WANG Lin, WU Hong. DEM based quantitative site catchment definition and statistics on retrievable farmlands[J]. Remote Sensing Technology and Application, 2017, 32(2):274-281.
[38]	CIESA M, GRIGOLATO S, CAVALLI R. Analysis on vehicle and walking speeds of search and rescue ground crews in mountainous areas[J]. Journal of Outdoor Recreation and Tourism, 2014, 5:48-57.
[39]	魏书精, 罗碧珍, 李小川, 等. 凉山州“3·30” 林火扑救人员伤亡原因分析与启示[J]. 森林防火, 2020 (2):10-13.
	WEI Shujing, LUO Bizhen, LI Xiaochuan, et al. Analysis and enlightenment on the causes of casualties in“3·30” forest fire fighting in Liangshan prefecture[J]. Forest Fire Prevention, 2020 (2):10-13.
[40]	毛贤敏. 风和地形对林火蔓延速度的作用[J]. 应用气象学报, 1993, 4(1):100-104.
	MAO Xianmin. The influence of wind and relief on the speed of the forest fire spreading[J]. Quarterly Journal of Applied Meteorology, 1993, 4(1):100-104.
[41]	丁智, 宋开山, 王宗明, 等. 基于ArcGIS Engine林火蔓延模拟算法的比较分析[J]. 中国科学院大学学报, 2014, 31(5):640-646.
	DING Zhi, SONG Kaishan, WANG Zongming, et al. Contrastive analysis of algorithms of the forest fire spreading simulation based on ArcGIS Engine[J]. Journal of University of Chinese Academy of Sciences, 2014, 31(5):640-646.

软件设备	环境配置	详细信息
硬件	CPU	Intel Core i5-11300H@3.10 GHz
	内存/GB	16
	显卡	GeoForce MX450
	显存/MB	2000
软件	系统	Windows10
软件	软件	Visual Studio，ArcGIS

关键空间区域	类别	等级	安全代价
邻河村沿河区域	安全目的地	高	0
植被稀疏平地		中等	0
农田	高安全区域	中等	0
河流		高	0
道路		高	0
狭窄山脊	山脊地带	较低	0.33
山间鞍部	山脊地带	较低	0.33
支沟地带	山谷地带	中等	0.5
山峪沟堵	山谷地带	中等	0.5
三面环山		中等	0.5
草塘沟	草塘沟	高	1

通行栅格	坡度/（°）	植被覆盖度	安全等级
A₄₀B₇₅	0~40	0~0.75	最高
A₄₅B₈₀	0~45	0~0.8	较高
A₅₀B₈₀	0~50	0~0.8	较高
A₅₅B₈₅	0~55	0.85	中等
A₆₀B₉₀	0~60	0~0.9	中等
A₆₅B₉₅	0~65	0.95	中等
A₇₀B₁₀₀	0~70	0~1	较低
A₈₀B₁₀₀	0~80	0~1	较低

逃生路网	路径	逃生时间/min	模拟蔓延间/min	空间距离/km	通行栅格
NR₁	P₁	110	110	14.9	A₄₀B₇₅
	P₂	102	102	14.6	A₄₅B₈₀
	P₃	95	95	11.5	A₅₀B₈₀
	P₄	88	88	10.7	A₅₅B₈₅
	P₅	70	70	8.3	A₆₀B₉₀
	P₆	68	68	7.9	A₆₅B₉₅
	P₇	60	60	6.2	A₇₀B₁₀₀
	P₈	58	58	5.2	A₈₀B₁₀₀

方法	逃生路网	路径	逃生时间/min	空间距离/km	平均植被覆盖度	通行栅格
本文方法	NR₂	P₉	120	15.4	0.638	A₄₀B₇₅
		P₁₀	105	14.8	0.672	A₄₅B₈₀
		P₁₁	100	12.5	0.710	A₅₀B₈₀
		P₁₂	92	11.3	0.720	A₅₅B₈₅
		P₁₃	78	8.9	0.624	A₆₀B₉₀
		P₁₄	78	8.7	0.631	A₆₅B₉₅
		P₁₅	58	6.7	0.700	A₇₀B₁₀₀
		P₁₆	55	5.6	0.686	A₈₀B₁₀₀
传统A^*算法	NR₃	P₁₇	120	15.3	0.638	A₄₀B₇₅
		P₁₈	106	15.1	0.672	A₄₅B₈₀
		P₁₉	105	13.1	0.714	A₅₀B₈₀
		P₂₀	87	12.1	0.720	A₅₅B₈₅
		P₂₁	70	8.1	0.624	A₆₀B₉₀
		P₂₂	71	7.9	0.631	A₆₅B₉₅
		P₂₃	59	6.7	0.700	A₇₀B₁₀₀
		P₂₄	57	5.6	0.686	A₈₀B₁₀₀
文献[24]	NR₄	P₂₅	130	15.7	0.651	A₄₀B₇₅
		P₂₆	133	15.8	0.682	A₄₅B₈₀
		P₂₇	110	13.8	0.713	A₅₀B₈₀
		P₂₈	95	12.7	0.720	A₅₅B₈₅
		P₂₉	78	9.1	0.632	A₆₀B₉₀
		P₃₀	74	8.3	0.635	A₆₅B₉₅
		P₃₁	60	6.7	0.712	A₇₀B₁₀₀
		P₃₂	59	6.0	0.710	A₈₀B₁₀₀