CN103886177A - Lightning fire daily occurrence probability predicting method based on space grids - Google Patents
Lightning fire daily occurrence probability predicting method based on space grids Download PDFInfo
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Abstract
The invention discloses a method for predicting daily occurrence probability of lightning fire. The method includes: building a data file, and taking each grid point and all fire points in a testing area as sampling points; performing multi-collinearity diagnose through data; selecting factors evidently influencing fire occurrence through Logistic Forward Wald analysis; building a daily occurrence probability model of the lightning fire through a Logistic model so as to analyze daily occurrence probability of the lightning fire, determining a lightning fire ignition threshold through a secondary judging theory, and calculating precision so as to perform precise analysis; on the basis of GIS, substituting day value data into the model for calculation so as to complete forecast of the daily occurrence probability of the lightning fire, and the like. The method has the advantages that the problem that the existing lightning fire predicting models rely on a lightning monitoring network is solved, wide application is achieved, the method can be used for calculating the daily occurrence probability of the lightning fire and analyzing future lightning fire occurrence trend, and the existing lightening fire pre-warning models are supplemented effectively.
Description
Technical field
Patent of the present invention relates to a kind of method of predicting lightning fire day probability of happening, relates in particular to a kind of method based on space lattice prediction lightning fire day probability of happening.
Background technology
Thunder and lightning is considered to cause one of most important natural cause of vegetation fire, and thunder storm and thunder and lightning are very frequent all over the world, cause by lightning fire the country that forest fire is maximum, is mainly the U.S., Canada, Russia and the country such as Australian.All there is more serious lightning fire in the state such as the U.S., Canada.The lightning fire of China is also quite serious in minority area, mainly occur in the Daxing'an Mountainrange in Heilungkiang, the area, Altai Mountains of exhaling alliance and Xinjiang in the Inner Mongol, wherein particularly outstanding with He Humeng forest zone, Daxing'an Mountainrange, the forest fires that Daxinganling District almost every year has lightning fire to cause are also that the most concentrated region at most occurs national lightning fire.Lightning fire is mainly connected closely with the activity of thunderstorm.Thunderstorm has region thunderstorm and frontal thunderstorm.Region thunderstorm is due to action of topography, is confined to certain region; Daxing'an Mountainrange arid season in spring, little precipitation, ground heats, and relative humidity reduces, and combustible is dry, and a thunderbolt is just being easy to fire spread and is causing disaster.
Forecast occurs lightning fire is the important content that forecast occurs Forest Fire, existing lightning fire forecast depends on lightning monitoring network, is real-time prediction, is not having under the condition of lightning monitoring data, cannot carry out forecasting lightning fire day, also cannot be used for climate change data analysis.
This method is divided based on regional space grid, according to reacting different size and the degree of depth combustible factor and the fire behavior factor to meteorological condition response feature in region, comprise FFMC, DMC, DC, ISI, BUI, FWI, historical lightning fire generation data, combustible data, with the meteorological factor of time period, and the Julian date of reaction seasonal variety, build lightning fire generation model, well solved the position of lightning fire generation and the problem of probability forecast thereof.The method can be used for the probability of happening forecast of lightning fire day, also can be for the lightning fire day probability of happening assessment based on climate change data.
Summary of the invention
Adopt following technical scheme in order to solve position that lightning fire occurs and problem the present invention of lightning fire probability of happening forecast thereof.The method comprises the steps:
Step 1, definite factor and collection related data that affects lightning fire, wherein test zone is divided into uniform grid, each grid cell is as a space independently, and the lightning fire probability of happening calculating is the lightning fire probability of happening of each grid cell;
Step 2, set up data file, in test zone, each lattice point and all lightning fire point are as sampled point;
Step 3, carry out multicollinearity diagnosis by data;
Step 4, by Logistic Forward Wald analyze, choose the factor that fire is had to appreciable impact;
Step 5, set up lightning fire day occurrence Probability Model by Logistic model, thereby analyze lightning fire day probability of happening,
z＝b
0+b
1x
1+b
2x
2+…+b
px
p
B
pfor coefficient or constant term, e is natural number.
Step 6, differentiate the theoretical lightning fire threshold values that catches fire of determining by secondary, computational accuracy, thus carry out precision analysis;
Mean value and standard deviation by catch fire sample and missing of ignition sample probability are calculated, and can obtain criterion:
In formula
and S
1, S
2be respectively mean value and the standard deviation of catch fire sample and missing of ignition sample probability.
If P (fire)>=Y
0, be judged to and catch fire;
If P (fire) < is Y
0, be judged to and do not catch fire.
Step 7, based on GIS, a day Value Data substitution model for key factor is calculated, thereby complete the probability of happening forecast of lightning fire day.
In above-mentioned steps two, generate lightning fire point location map according to the latitude and longitude coordinates of fire data.The date and the position that occur according to lightning fire, obtaining the each component factor of each fire FWI system and weather data processes lightning fire, lightning fire data to every day are processed, lightning fire point and grid are superposeed, if this fire point is positioned at certain grid, this grid differentiation is 1 (catching fire), is 0 (not catching fire) otherwise differentiate.
Further, the factor that affects lightning fire in described step 1 comprises that the processing of lightning fire historical data, space lattice division, the calculating of lightning fire height above sea level, vegetation data processing, longitude and latitude, process meteorological data, the each component factor of FWI are calculated, the Julian date.
Above factor specific explanations is:
(1) lightning fire historical data processing
In statistical forecast region lightning fire day frequency and date of generation, and the longitude and latitude that occurs of lightning fire.
(2) space lattice is divided
Estimation range is divided into uniform grid, each grid cell as one independently sky ask, the lightning fire probability of happening calculating is the lightning fire probability of happening of each grid cell.
(3) lightning fire height above sea level calculates
Lightning fire is put to position data and altitude figures stack, generate the data of lightning fire sea level elevation.
(4) vegetation data processing
Lightning fire is put to position data and combustible substance distribution figure superposes, carry out intersection operation, obtain fuel type of each fire point position.
The space lattice data of generation and vegetation data are superposeed, calculate the vegetation pattern of each grid inner area maximum as the vegetation pattern of this grid, generate vegetation data Layer, respectively every kind of vegetation pattern is set to a code.
(5) longitude and latitude
Different longitudes and latitude produce different impacts to weather, and have affected to a certain extent the distribution of forest.Longitude and latitude, has determined the locus in a certain region, local Climatic and vegetation is distributed and produces long-term impact simultaneously, the latitude and longitude coordinates using the latitude and longitude coordinates at each cell center as cell.
(6) process meteorological data
Continuous meteorological day Value Data, comprises temperature, precipitation, phase water humidity, wind speed.
(7) the each component factor of FWI is calculated
In model, need to input the output parameter in forest fires weather index FWI system according to result, be respectively: fine fuels humidity codes FFMC, duff humidity codes DMC, arid code DC, initial rate of propagation ISI, combustible index of bunching BUI, fiery weather index FWI.According to carrying out Collinearity Diagnosis Analysis result with correlation factor, the each component factor of FWI is selected.
FFMC, DMC and DC represent that different classes of Moisture of Forest Flammable Matter changes, and ISI, BUI and FWI are fire behavior indexs.
(8) the Julian date
Reflect the impact of seasonal variation on lightning fire probability of happening.
Further, in described step 2, the index of each fire point and grid is set up in the definite employing whether each grid is caught fire, if sample on the same day, this grid state of catching fire is made as 1, otherwise is made as 0, final table data file, the interpolated data of a certain day set up.
Further, in described step 2, the data of the collection to sampled point are vegetation information, altitude information, weather information, the each component factor of FWI etc.
Further, in described step 3, carry out in multicollinearity diagnosis the factor of influence of selecting expansion factor to be less than 10 by data.
Because the collinearity of independent variable can reduce the precision of model, in order to improve the precision of modeling, reduce the collinearity between different variablees in model, before modeling, to carry out multicollinearity inspection to selected variable for this reason.According to the variance inflation factor VIF of variable, collinearity between the larger explanation related coefficient of VIF value is larger, VIF value is less than 10 under normal circumstances, representing does not have obvious correlativity between each variable, tolerance is less, multicollinearity is more serious, and in addition also can be by eigenvalue of maximum time, the variation proportion of each variable carrys out the correlativity between judgment variable.It is generally acknowledged that 10 of VIF < think that linear dependence is not obvious, the impact that these variablees occur fire is different.
Beneficial effect of the present invention is:
There is forecast and belong to the fiery forecast category that occurs in lightning fire, the research that lightning fire occurs to forecast at present depends on lightning monitoring network, belongs to real-time early warning, cannot carry out forecasting lightning fire day, also cannot carry out early warning for the region that there is no lightning monitoring network.While carrying out climate change on lightning fire impact research, because climate change data do not contain lightning monitoring data, existing lightning fire forecast model cannot carry out the trend analysis of following lightning fire generation.
This method has considered to affect the key factor that lightning fire occurs, the probability that has solved lightning fire generation every day in a certain survey region is how many problem, the method has overcome the dependence of existing lightning fire forecast model to lightning monitoring network, there is application widely, both can be for the probability of happening calculating of daily lightning fire day, also can be used for the analysis of following lightning fire occurrence tendency, existing lightning fire Early-warning Model is effectively supplemented.
The structure of lightning fire generation model need be based on specific region source data, and build model and be applicable to the specific region corresponding in source data, along with the prolongation of time, the increase of lightning fire data recording, the precision of model will further improve.
Brief description of the drawings
Fig. 1 is the inventive method step schematic diagram;
Fig. 2 is lightning fire day of the present invention probability of happening simulation drawing.
Embodiment
Further illustrate technical scheme of the present invention below in conjunction with accompanying drawing and by embodiment.
In conjunction with Fig. 1 and Fig. 2, (comprise the large portion of the Greater Hinggan Mountains in Heilongjiang and Daxinganling, Inner Mongolia) taking Daxing'an Mountainrange and carry out the probability of happening modeling of lightning fire day and simulation as example.
Step 1 101, definite factor and collection related data that affects lightning fire, wherein test zone is divided into uniform grid, each grid cell is as a space independently, and the lightning fire probability of happening calculating is the lightning fire probability of happening of each grid cell.
(1) the 1972-2006 lightning fire data of collection Heilongjiang Province and Daxinganling District, Inner Mongolia Autonomous Region, comprise date, fire number of times, lightning fire point longitude and latitude data that lightning fire occurs.Screen all lightning fire data that have latitude and longitude coordinates and have dat recorder, generate lightning fire fire point location map according to the latitude and longitude coordinates of fire data.
(2) lightning fire point and NASA90DEM data are superposeed, generate the field of sea level elevation.
(3) by survey region taking 20km as elementary cell is divided into uniform grid, each grid cell is as a space independently.
(4) grid distributed data and NASA90DEM data are superposeed, the height above sea level of each fire point position is read in fire point position distribution data, generate the height above sea level of each cell.
(5) constant in the situation that, fire is put to position data with combustible substance distribution figure superposes at supposition fuel type, carry out intersection operation, obtain each fire and put fuel type of position.
(6) grid data and 1: 100 ten thousand vegetation data are superposeed, calculate the vegetation pattern of each grid inner area maximum as the vegetation pattern of this grid, generate vegetation data Layer, respectively every kind of vegetation pattern is set to a code.
(7) different longitudes and latitude produce different impacts to weather, and have affected to a certain extent the distribution of forest.Longitude and latitude, has determined the locus in a certain region, local Climatic and vegetation is distributed and produces long-term impact simultaneously, the latitude and longitude coordinates using the latitude and longitude coordinates at each cell center as cell.
(8) collect the weather data of time period identical with lightning fire data, weather data employing mean value calculates.Weather data is used 1972-2006 country Value Data of basic website day, has 10 websites, in order to improve the effect of interpolation, has also selected 3 websites at survey region periphery.Main field comprises temperature on average, precipitation, relative humidity, mean wind speed.The each component factor of FWI is calculated.
(9) because the calculating of the each component factor of FWI system needs continuous weather data, the therefore disappearance of certain meteorological factor or extremely will exert an influence to result of calculation.Therefore need to the each component factor of FWI system before calculating or in computation process the missing values to meteorological factor and exceptional value identify and process.The processing of missing values is according to the definition of the definition code metadata of China Meteorological Administration's missing values, and weather data realizes by linear interpolation in SPSS.
The computing formula of the each component factor of FWI system can be with reference to Equations and FORTRAN program for the Canadian Forest Fire Weather Index System and two books of Development and structure of the Canadian forest fire weather index system.Also can adopt Canadian Prometheus fire spread software to carry out secondary development calculating.
(10) if the 1972-2006 data of all days are processed, data volume is too large, therefore, only chooses the date of breaking out of fire, weather data to this day and the FWI system index of correlation are carried out interpolation processing, and its cell size is consistent with position and foundation drawing.Catch fire meteorological factor and the each component factor of FWI system on date of all lightning fires of 1972-2007 carried out to interpolation, and the date that lightning fire occurs has 373, and each date has 10 variablees to carry out interpolation, carries out altogether interpolation 3730 times.
Step 2 102, set up data file, in test zone, each lattice point and all fire point are as sampled point.
In survey region, each lattice point is as a sampled point, all fire points are also as sampled point simultaneously, set up from the day data file of 1972-2006, data file comprises the vegetation information, altitude information of sampling point, from mankind's accumulation area minimum distance information etc.The index of each fire point and grid is set up in definite employing of whether catching fire for each grid, if index value is identical, is sample on the same day simultaneously, and this grid state of catching fire is made as 1, otherwise is made as 0, final table data file, the interpolated data of a certain day set up.
The date and the position that occur according to fire, obtaining the each component factor of each fire FWI system and weather data processes lightning fire, fire data to every day is processed, fire point and 20km grid are superposeed, if this fire point is positioned at certain grid, this grid differentiation is 1 (catching fire), is 0 (missing of ignition) otherwise differentiate.
In survey region, each lattice point is as a sampled point, and all lightning fire points, also as sampled point, are set up the day data file from 1972-2006 simultaneously, and data file comprises vegetation information, the altitude information etc. of sampling point.The index of each fire point and grid is set up in definite employing of whether catching fire for each grid, if index value is identical, is sample on the same day simultaneously, and this grid state of catching fire is made as 1, otherwise is made as 0, final table data file, the interpolated data of a certain day set up.
Step 3 to five 103-105, carry out multicollinearity diagnosis by data; Analyze by Logistic Forward Wald, choose the factor that fire is had to appreciable impact; Set up lightning fire day occurrence Probability Model by Logistic model, thereby analyze lightning fire day occurrence Probability Model structure.
According to the result of collinearity inspection, having 7 factor pair forest fire probability of happening exerts an influence, they are temperature on average, daily precipitation amount, relative humidity, mean wind speed, FFMC, DC and FWI, build Logistic equation, and each regression coefficient and inspection parameter are as table 1.
Adopt Logistic Forward Wald method to analyze correlated variables, remove the fire inapparent variable that makes a difference, filter out fiery probability of happening is affected to significant variable, build Logistic equation, each regression coefficient and inspection parameter are as table 1.
Table 1 regression coefficient and inspection parameter
Z＝0.0366MainT+0.01780FFMC+0.0076DMC+0.0015Elev+0.1250Long+0.41421Lat+0.0071Julian-47.86
In formula, MainT is temperature on average, DEG C; Elev is height above sea level, m; Long is longitude, °; Lat is latitude, °; Julian is the Julian date.
Step 6 106, differentiate the theoretical lightning fire threshold values that catches fire of determining by secondary, computational accuracy, thus carry out precision analysis precision analysis
Table 2 basic statistics amount
Respectively the sample catching fire and the sample that do not catch fire are added up, calculated basic statistics amount (table), according to the secondary method of discrimination whether catching fire, calculating judgment threshold is 0.002.Model again substitution is analyzed to data, fiery probability of happening is calculated, the threshold value occurring according to fire is added up catch fire sample and the sample that do not catch fire respectively, calculates the accuracy rate of judgement as table.
Table 3 accuracy rate statistics
Can find out that in the sample that catches fire, being judged as the sample size catching fire is 442, accuracy rate is 57.11%, and it is 291757 that the sample that do not catch fire is judged as the sample size not catching fire, and accuracy rate is 66.17%.
Step 7 107, based on GIS, a day Value Data substitution model for key factor is calculated, thereby judge.Taking 1988-6-7 as example, lightning fire occurrence Probability Model is simulated to (Fig. 1), can evaluate each grid lightning fire probability of happening in region.
The above; be only preferably embodiment of the present invention, but protection scope of the present invention is not limited to this, any people who is familiar with this technology is in the disclosed technical scope of the present invention; the variation that can expect easily or replacement, within all should being encompassed in protection scope of the present invention.Therefore, protection scope of the present invention should be as the criterion with the protection domain of claim.
Claims (5)
1. the method based on space lattice prediction lightning fire day probability of happening, is characterized in that, the method comprises the steps:
Step 1, definite factor and collection related data that affects lightning fire, wherein test zone is divided into uniform grid, each grid cell is as a space independently, and the lightning fire probability of happening calculating is the lightning fire probability of happening of each grid cell;
Step 2, set up data file, in test zone, each lattice point and all lightning fire point are as sampled point;
Step 3, carry out multicollinearity diagnosis by data;
Step 4, by Logistic Forward Wald analyze, choose the factor that fire is had to appreciable impact;
Step 5, set up lightning fire day probability of happening Logistic model
z＝b
0+b
1x
1+b
2x
2+…+b
px
p
B
pfor coefficient or constant term, e is natural number;
Step 6, differentiate the theoretical lightning fire threshold values that catches fire of determining by secondary,
Mean value and standard deviation by catch fire sample and missing of ignition sample probability are calculated, and can obtain criterion:
In formula
and S
1, S
2be respectively mean value and the standard deviation of catch fire sample and missing of ignition sample probability.
If P (fire)>=Y
0, be judged to and catch fire;
If P (fire) < is Y
0, be judged to and do not catch fire;
Step 7, based on GIS, a day Value Data substitution model for key factor is calculated, thereby complete the probability of happening forecast of lightning fire day.
2. a kind of method based on space lattice prediction lightning fire day probability of happening as claimed in claim 1, it is characterized in that, the factor that affects lightning fire in described step 1 comprises that the processing of lightning fire historical data, space lattice division, the calculating of lightning fire height above sea level, vegetation data processing, longitude and latitude, process meteorological data, the each component factor of FWI are calculated, the Julian date.
3. a kind of method based on space lattice prediction lightning fire day probability of happening as claimed in claim 1, it is characterized in that, in described step 2, the index of each fire point and grid is set up in the definite employing whether each grid is caught fire, if sample on the same day, this grid state of catching fire is made as 1, otherwise is made as 0, final table data file, the interpolated data of a certain day set up.
4. a kind of method based on space lattice prediction lightning fire day probability of happening as claimed in claim 1, is characterized in that, in described step 2, the data of the collection to sampled point are vegetation information, altitude information, weather information, the each component factor of FWI etc.
5. a kind of method based on space lattice prediction lightning fire day probability of happening as claimed in claim 1, is characterized in that, in described step 3, carries out in multicollinearity diagnosis the factor of influence of selecting expansion factor to be less than 10 by data.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112529291A (en) * | 2020-12-08 | 2021-03-19 | 国网湖南省电力有限公司 | Method for predicting forest and grassland fire caused by power grid intensive power transmission channel line |
| CN113049884A (en) * | 2020-12-30 | 2021-06-29 | 北京旺辰鼎科技发展有限公司 | Lightning stroke fire monitoring and early warning method, device and system based on satellite communication |
| CN113656743A (en) * | 2021-08-12 | 2021-11-16 | 贵州省建筑设计研究院有限责任公司 | Weather big data-based accurate calculation method for expected lightning strike geodetic times of building year |
| CN114861991A (en) * | 2022-04-18 | 2022-08-05 | 国家林业和草原局哈尔滨林业机械研究所 | Lightning stroke fire risk prediction method and system based on three-dimensional lightning perception |
| CN116739185A (en) * | 2023-08-09 | 2023-09-12 | 国网江苏省电力有限公司苏州供电分公司 | Real-time lightning area prediction and line early warning method and system based on lightning energy |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112529291A (en) * | 2020-12-08 | 2021-03-19 | 国网湖南省电力有限公司 | Method for predicting forest and grassland fire caused by power grid intensive power transmission channel line |
| CN113049884A (en) * | 2020-12-30 | 2021-06-29 | 北京旺辰鼎科技发展有限公司 | Lightning stroke fire monitoring and early warning method, device and system based on satellite communication |
| CN113656743A (en) * | 2021-08-12 | 2021-11-16 | 贵州省建筑设计研究院有限责任公司 | Weather big data-based accurate calculation method for expected lightning strike geodetic times of building year |
| CN114861991A (en) * | 2022-04-18 | 2022-08-05 | 国家林业和草原局哈尔滨林业机械研究所 | Lightning stroke fire risk prediction method and system based on three-dimensional lightning perception |
| CN116739185A (en) * | 2023-08-09 | 2023-09-12 | 国网江苏省电力有限公司苏州供电分公司 | Real-time lightning area prediction and line early warning method and system based on lightning energy |
| CN116739185B (en) * | 2023-08-09 | 2023-12-19 | 国网江苏省电力有限公司苏州供电分公司 | Real-time lightning area prediction and line early warning method and system based on lightning energy |
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