Journal of Geo-information Science >
Forest Fire Risk Rapid Warning Model based on Meteorological Monitoring Network
Received date: 2019-12-24
Request revised date: 2020-03-25
Online published: 2021-02-25
Supported by
National Natural Science Foundation of China(41761080)
Industrial Support and Guidance Projects of Colleges and Universities in Gansu Province(2019C-04)
Funded by LZJTU EP(201806)
Natural Science Foundation of Henan(202300410345)
Copyright
Forest fire occurs frequently and suddenly. Therefore, it is essential to carry out the rapid warning of forest fire danger for the reduction of the loss caused by forest fire and the promotion of sustainable development of forest resources. This paper designs an early-warning model based on GIS spatial analysis and visualization technology and the construction of real-time meteorological monitoring network using ground meteorological stations, which can achieve timely and rapid warning of forest fire danger. To build the model, this paper first determines the forest fire danger early-warning factors, which are the input parameters of the model. Secondly, a hierarchy model of the importance of early warning indicators is constructed to determine the weight of the early warning factors via using the AHP method and combining the analysis of early warning factors. Then, the thresholds and grade division criteria of the early-warning factor are determined according to the national, industrial, and local regulations for determining forest fire danger levels, which is suitable for the model. Finally, the Voronoi Diagrams are used to establish a meteorological monitoring network based on weather stations and real-time weather data. The Overlay Analysis technology is used to calculate the early warning result. Based on the model and real-time acquisition and processing of data, a rapid warning system for forest fires was constructed. This paper took Qinghai Province as the experimental area where the feasibility and applicability of the system were verified, which indicates that early warning of forest fire danger can be realized by the model comprehensively, accurately, and rapidly. Results show that: (1) According to the early-warning model, the real-time early-warning indicators which were set before, and real-time meteorological monitoring data, the early-warning signal can be sent in time, which can quickly realize early warning and timely response of forest fire risks at the county and forest farm levels; (2) Via introducing GIS visualization methods, the thematic map of forest fire risk spatial distribution can be generated by the model quickly, which is conducive to observe changes in early-warning levels visually. The rapid warning of forest fire risks has important guiding functions for effective prevention, interruption management, and prevention measures of forest fire, and has great significance for forest fire prevention work, protection of forest resources, and safety of human life and property.
LI Yu , ZHANG Liming , ZHANG Xingguo , WANG Hao , ZHANG Xingang . Forest Fire Risk Rapid Warning Model based on Meteorological Monitoring Network[J]. Journal of Geo-information Science, 2020 , 22(12) : 2317 -2325 . DOI: 10.12082/dqxxkx.2020.190799
表1 1—9比例标度表Tab. 1 Proportional scale of 1-9 |
重要性标度 | p因子与q因子比较的结果 |
---|---|
1 | p因子与q因子同等重要 |
3 | p因子比q因子稍微重要 |
5 | p因子比q因子明显重要 |
7 | p因子比q因子强烈重要 |
9 | p因子比q因子极端重要 |
2,4,6,8 | 重要性介于上述相邻判断的中间值 |
表2 森林火险预警因子的判断矩阵与一致性检验表Tab. 2 Judgment matrix and consistency test of forest fire danger early warning factors |
预警因子 | C1 | C2 | C3 | C4 | C5 | C6 | C7 | C8 | 权重 |
---|---|---|---|---|---|---|---|---|---|
C1 | 1 | 2 | 2 | 4 | 3 | 6 | 7 | 5 | 0.2957 |
C2 | 1/2 | 1 | 1 | 3 | 2 | 5 | 6 | 4 | 0.1918 |
C3 | 1/2 | 1 | 1 | 3 | 2 | 5 | 6 | 4 | 0.1918 |
C4 | 1/4 | 1/3 | 1/3 | 1 | 1/2 | 3 | 4 | 2 | 0.0811 |
C5 | 1/3 | 1/2 | 1/2 | 2 | 1 | 4 | 5 | 3 | 0.1234 |
C6 | 1/6 | 1/5 | 1/5 | 1/3 | 1/4 | 1 | 2 | 1/2 | 0.0364 |
C7 | 1/7 | 1/6 | 1/6 | 1/4 | 1/5 | 1/2 | 1 | 1/3 | 0.0260 |
C8 | 1/5 | 1/4 | 1/4 | 1/2 | 1/3 | 2 | 3 | 1 | 0.0538 |
表3 1—10阶平均随机一致性指标Tab. 3 Mean Random Consistency Index of 1-order to 10-order |
矩阵阶数 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
---|---|---|---|---|---|---|---|---|---|---|
RI值 | 0 | 0 | 0.52 | 0.89 | 1.12 | 1.26 | 1.36 | 1.41 | 1.46 | 1.49 |
表4 森林火险各预警因子危险级别查对表Tab. 4 Risk level checklist of forest fire danger early-warning factors |
级别 | 林业类型范围 | 与道路的 距离/m | 温度范围 /℃ | 湿度范围 /% | 风力范围 /(m/s) | 连续无效降雨天数/d | 干旱指标 | 季节 |
---|---|---|---|---|---|---|---|---|
1 | 0.0~0.99 | 20 000~100 000 | -60~20 | 90~100 | 0.0~0.2 | 0~3 | 无干旱 | - |
2 | 0.99~1.0 | 13 000~20 000 | -20~0 | 80~90 | 0.2~1.5 | 4~6 | 无干旱 | - |
3 | 1.0~1.5 | 10 000~13 000 | 0~5 | 70~80 | 1.5~3.3 | 7~10 | 无干旱 | - |
4 | 1.5~2.0 | 8000~10 000 | 5~10 | 60~70 | 3.3~5.4 | 11~20 | 轻度干旱 | - |
5 | 2.0~2.5 | 6000~8000 | 10~15 | 50~60 | 5.4~8.0 | 21~25 | 中度干旱 | - |
6 | 2.5~3.0 | 4500~6000 | 15~20 | 40~50 | 8.0~10.8 | 26~30 | 中度干旱 | 夏 |
7 | 3.0~3.5 | 3000~4500 | 20~25 | 30~40 | 10.8~13.8 | 31~35 | 严重干旱 | - |
8 | 3.5~4.0 | 1800~3000 | 25~30 | 20~30 | 13.8~17.2 | 35~40 | 严重干旱 | 春/秋 |
9 | 4.0~4.5 | 800~1800 | 30~35 | 10~20 | 17.2~20.7 | 40~45 | 严重干旱 | - |
10 | 4.5~5.0 | 0~800 | 35~60 | 0~10 | 20.7~50.0 | >45 | 极度干旱 | 冬 |
表5 森林火险预警等级划分表Tab. 5 Classification of forest fire danger warning level |
林火天气预警等级 | 名称 | 危险程度 | 栅格图层预警结果 | 颜色 | 预警标志 |
---|---|---|---|---|---|
一级 | 低火险 | 低 | -800~1.0 | 无色 | - |
二级 | 较低火险 | 较低 | 1.0~4.8 | 淡黄色 | - |
三级 | 较高火险 | 较高 | 4.8~6.5 | 浅橙色 | 黄旗 |
四级 | 高火险 | 高 | 6.5~8.2 | 橙色 | 橙旗 |
五级 | 极高火险 | 极高 | 8.2~10.0 | 红色 | 红旗 |
图2 青海省林业类型危险级别分布Fig. 2 Risk level distribution of forestry types in Qinghai Province |
图3 青海省与道路的距离危险级别分布Fig. 3 Risk level distribution of distance from roads in Qinghai Province |
表6 青海省森林火险预警实验数据集Tab. 6 Experimental data set of forest fire danger early warning in Qinghai Province |
时间 | 气象因子 | 西宁市 | 同仁县 | 河南县 | |||
---|---|---|---|---|---|---|---|
监测值 | 级别 | 监测值 | 级别 | 监测值 | 级别 | ||
2019年4月1日10时 | 温度 | 9.4 | 4 | 9.7 | 4 | 5.3 | 4 |
湿度 | 24 | 7 | 26 | 8 | 50 | 8 | |
风力 | 2.5 | 3 | 2.3 | 7 | 2.4 | 5 | |
干旱 | 极度干旱 | 10 | 极度干旱 | 10 | 极度干旱 | 10 | |
季节 | 春季 | 8 | 春季 | 8 | 春季 | 8 |
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