CN110070234B - Earthquake landslide personnel death number prediction method and application thereof - Google Patents
Earthquake landslide personnel death number prediction method and application thereof Download PDFInfo
- Publication number
- CN110070234B CN110070234B CN201910354022.0A CN201910354022A CN110070234B CN 110070234 B CN110070234 B CN 110070234B CN 201910354022 A CN201910354022 A CN 201910354022A CN 110070234 B CN110070234 B CN 110070234B
- Authority
- CN
- China
- Prior art keywords
- landslide
- earthquake
- kilometer
- grid
- vector data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
Abstract
The invention discloses a method for predicting the death number of earthquake landslide personnel and application thereofDetermining the earthquake landslide personal death rate index r of each kilometer grid unit in a research areadi，rdiDetermining the number of earthquake landslide danger grades according to the 90 m scale of each region in kilometer grid, and determining the number of earthquake landslide danger grades according to rdiAnd finally, summing the earthquake landslide death number of each kilometer grid unit to predict the earthquake landslide death number of the whole research area. The method fully considers the difference between population and the spatial distribution of earthquake landslide, and the number of dead people is the result of the spatial combined action of population distribution and landslide strength, decomposes the prediction object on kilometer grid units with uniform shape and size, and avoids the condition that the estimation of the number of dead people has larger error with the reality in earthquake disaster risk assessment before earthquake and emergency rescue decision assessment after earthquake due to no proper method.
Description
Technical Field
The invention belongs to the field of earthquake landslide, and relates to a method for predicting death number of earthquake landslide of kilometer grid units based on a death rate index, prediction and forecast of death number of earthquake landslide in an area and risk assessment of earthquake landslide disaster before earthquake by using emergency command decision after earthquake, in particular to a method for predicting death number of earthquake landslide personnel and application thereof.
Background
The evaluation of the death of people caused by earthquake is the key of the auxiliary decision suggestion evaluation of the emergency command after earthquake and the risk evaluation of earthquake disaster before earthquake. The difference of the estimated number of dead people in earthquake emergency results in completely different emergency response levels, different emergency processes and different investment are initiated, and the whole emergency rescue is greatly influenced. When 8.0 earthquake in 5.12 Wenchuan, 5.7 earthquake in 9.07 Yi-nationality, 5.6 earthquake and 6.5 earthquake in 8.03 Ludian, serious earthquake landslide causes death of a large number of people. Due to the lack of an effective earthquake landslide worker death number evaluation method, the number evaluation of death workers in earthquake emergency has larger deviation from an actual result. The high-level emergency response of excessive personnel death assessment decisions can cause great influence and rescue force waste on disaster areas and surrounding normal life, and the shortage of the personnel death prediction can cause blindness and misjudgment of early rescue scheduling to weaken the own rescue benefit and also can cause very adverse influence on disaster relief. The earthquake landslide is an important aspect of causing the death of people, particularly in western mountainous areas of China, landslide caused by an earthquake usually causes casualties of different degrees, and the number of the death of people caused by the earthquake landslide needs to be effectively predicted to effectively reduce the life loss of the earthquake landslide and reduce the casualties risk of the earthquake landslide.
The death number of people caused by landslide caused by earthquake is not only related to landslide itself, but also closely related to population distribution of a research area, and is the result of the spatial interaction of the landslide and the people. The current earthquake landslide risk assessment method can provide the earthquake landslide risk level distribution condition of a 90-meter grid in a research area, the result assessment of whether landslide occurs or not is difficult to accurately make, a prediction method for determining the death number of earthquake landslide personnel by combining the background action of high-precision population distribution of the research area on an assessment factor is not formed, and more people still roughly estimate the death number of the earthquake landslide personnel according to historical experience and the earthquake landslide risk assessment result, so that the death number of the earthquake landslide personnel cannot be effectively predicted.
Disclosure of Invention
The invention aims to provide a method for predicting the death number of earthquake landslide personnel aiming at post-earthquake emergency assistant decision and pre-earthquake disaster risk assessment and application thereof aiming at the defects and urgent practical requirements of the prior art.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
the earthquake landslide worker death number prediction method is characterized in that the earthquake landslide worker death number of each kilometer grid unit in the whole research area (to-be-predicted area) is calculated by combining the number distribution of population in each kilometer grid unit according to the landslide risk level of 90-meter grids in the grid range and the earthquake landslide death rate distribution under the condition of arbitrary combination of 2 evaluation factors of the number of grids of each level. And then summing the death number of the earthquake landslides of all kilometers of grids in the research area to predict and forecast the total death number of the earthquake landslides. Firstly, a kilometer grid evaluation unit of the whole research area is established, namely, the research area is divided into square grids with the side length of 1 kilometer, so that the shape and the scale of the unit are uniformly evaluated. The number of people in each kilometer grid cell is then assigned to the corresponding grid cell. And calculating the earthquake landslide risk level distribution data of a grid of 90 meters in the whole research area by using an earthquake landslide risk evaluation model on GIS software, and converting the obtained grid data into dot-shaped space vector data with the same landslide risk level attribute at intervals of 90 meters. And counting the number of points of each landslide risk level in each kilometer grid unit, and assigning the attributes and the number of the landslide risk levels to the corresponding grid units in space. And then starting from the research on the relation between the earthquake landslide risk grade distribution characteristics and the earthquake landslide mortality in kilometer grid units, fully combining the congruency of the landslide point quantity changes with different risk grades and the kilometer grid personnel mortality changes, and establishing an earthquake landslide personnel mortality index model under the condition of any combination of landslide risk grades and grid quantities of corresponding grades in the kilometer grid unit range. And then establishing an earthquake landslide death number calculation model of each kilometer grid unit according to the population number of the kilometer grid units and the earthquake landslide people death rate index model. And establishing a calculation model of the number of earthquake landslide death persons in the research area according to the fact that the number of earthquake landslide person deaths in the whole research area is equal to the sum of the number of earthquake landslide person deaths in each kilometer of grid units, and finally forecasting the number of earthquake landslide death persons.
The earthquake landslide death refers to the loss of life of people caused by landslide caused by the same earthquake, and the number of the earthquake landslide death refers to the number of the earthquake landslide death, which is determined by fully considering the population distribution of the area, the landslide risk level distribution and the spatial coupling of the landslide risk level distribution and the population distribution. Specifically, the method for predicting the number of dead people in earthquake landslide comprises the following steps:
1. and carrying out grid division on the whole research area by using GIS software to obtain surface attribute space vector data of × 1 km, wherein each grid is a square of × 1 km, and uniformly evaluating the shape and the scale of the unit by grid division.
2. And collecting the vector data of the spotted spatial population of the research area. Obtaining dot diagram layer space vector data of the people's mouth living areas such as the natural villages and the communities in the research areas through large-scale topographic map vectorization, then obtaining the population quantity information of each point from various statistical yearbooks, and using the population quantity information as attribute information of the dot diagram layer space vector data of the people's mouth living areas such as the corresponding natural villages and the street offices.
3. And (3) carrying out population quantity attribute assignment on the planar kilometer grid space vector data obtained in the step (1). And assigning the population sum of the point population space vector data falling in each kilometer grid unit in space to the corresponding kilometer grid unit by using a space attribute correlation module of GIS software, so that all kilometer grid units in the whole research area have the population attribute corresponding in space, and obtaining the face kilometer grid space population vector data of the whole research area.
4. And generating the space vector data of the risk level of the punctual earthquake landslide at intervals of 90 meters in the research area. And generating earthquake landslide risk grade distribution data of the whole research area by using GIS software through an earthquake landslide risk grade evaluation method (the method is the prior art). The earthquake landslide risk level data generated by the earthquake landslide risk level evaluation method is 90-meter raster space data, the attribute values of the raster data are divided into 5 levels, and the data are 1, 2, 3, 4 and 5 in sequence from low to high according to the landslide risk. For convenience of calculation, the seismic landslide risk level space data of the grids of 90 meters in the whole research area are converted into point-shaped seismic landslide risk level space vector data at intervals of 90 meters, the attribute value of each grid is assigned to the point converted by the grids during conversion to serve as the landslide risk level attribute of the point, and finally the point-shaped seismic landslide risk level space vector data at intervals of 90 meters in the whole research area are generated.
5. And 3, further carrying out earthquake landslide risk grade attribute assignment on the planar kilometer grid space population vector data obtained in the step 3. And counting the number of 90-meter interval point-like earthquake landslide danger level space vector data of each landslide danger level which completely falls in each planar kilometer grid space population vector data in space by using a space attribute correlation module of GIS software. And sequentially assigning the landslide hazard level and the number of the points of the level in each grid to corresponding planar kilometer grid space population vector data, and finally obtaining the planar kilometer grid space vector data of the whole research area with population number, landslide hazard level and the number of the landslide points of each level. The essence of this step is to convert the area for calculating different landslide risk levels in kilometer grid cells into more rapid and convenient statistics of the number of points of different landslide risk levels in each kilometer grid cell.
6. Sequentially calculating the earthquake landslide personnel mortality index r of each kilometer of grid unit obtained in the step 5 by the following formuladi：
In the formula rdiRepresenting the earthquake landslide people mortality index for the ith kilometer grid cell.
X5Point number X representing 90 m interval point-like earthquake landslide hazard level space vector data with landslide attribute of 5 in ith kilometer grid cell obtained by step 54Point number X representing 90 m interval point-like earthquake landslide hazard level space vector data with landslide attribute of 4 in ith kilometer grid cell obtained by step 5390-meter interval point-like earthquake landslide danger with landslide attribute of 3 in ith kilometer grid unit obtained in step 5Number of points, X, of risky level spatial vector data2Point number X representing 90 m interval point-like earthquake landslide hazard level space vector data with landslide attribute of 2 in ith kilometer grid cell obtained by step 51And 5, representing the point number of 90-meter interval point-like earthquake landslide risk level space vector data with the landslide attribute of 1 in the ith kilometer grid unit.
Wherein, earthquake landslide personal mortality index r of kilometer grid unitdiThe calculation model of (a) is established by:
and 6, (1) assuming that the earthquake landslide personal mortality index of the kilometer grid unit has a functional relation with the number of earthquake landslide danger levels in each grid unit:
rdi＝f(x1，x2，x3，x4，x5)
in the formula ofdiRepresenting the earthquake landslide people mortality index for the ith kilometer grid cell. X5Point number X representing 90 m interval point-like earthquake landslide hazard level space vector data with landslide attribute of 5 in ith kilometer grid cell obtained by step 54Point number X representing 90 m interval point-like earthquake landslide hazard level space vector data with landslide attribute of 4 in ith kilometer grid cell obtained by step 53The point number X of 90-meter interval point-like earthquake landslide risk level space vector data with 3 landslide attributes in the ith kilometer grid unit obtained in the step 5 is shown2Point number X representing 90 m interval point-like earthquake landslide hazard level space vector data with landslide attribute of 2 in ith kilometer grid cell obtained by step 51The number of points of 90 m interval point-like seismic landslide risk level spatial vector data having a landslide attribute of 1 in the ith kilometer grid cell obtained in step 5 (the same applies hereinafter).
Randomly selecting 10350 kilometer grid units encountering earthquake regions, and obtaining the population number of the 10350 kilometer grid units in earthquake and the number of dead people in earthquake landslide through investigation; and then acquiring the number of points with different landslide risk levels when 10350 kilometer grid units encounter an earthquake by using the intensity published by the field survey of the earthquake system as a parameter through the methods of the step 4 and the step 5. On this basis, the earthquake landslide mortality for each unit was calculated for 10350 kilometers of grid cells that encountered the earthquake. The results are shown in Table 1.
TABLE 1.7533 statistics of population of grid cells, death/rate of earthquake landslide, and danger level of earthquake landslide
In table piRepresenting the number of people in the ith kilometer grid cell. ddiRepresenting the number of earthquake landslide people deaths when the ith kilometer grid cell encounters an earthquake. r isriRepresenting the earthquake landslide personal mortality of the ith kilometer grid cell encountering the earthquake, by ddi/piThus obtaining the product.
X5The point number X of 90-meter interval point-like earthquake landslide danger level space vector data with landslide attribute of 5 in the ith kilometer grid unit when encountering earthquake is obtained through inversion in the steps 4 and 54The point number X of 90-meter interval point-like earthquake landslide danger level space vector data with landslide attribute of 4 in ith kilometer grid unit when encountering earthquake is obtained through inversion in steps 4 and 53The point number X of 90-meter interval point-like earthquake landslide danger level space vector data with 3 landslide attributes in the ith kilometer grid unit when encountering earthquake is obtained through inversion in the steps 4 and 52The point number X of 90-meter interval point-like earthquake landslide danger level space vector data with landslide attribute of 2 in ith kilometer grid unit when encountering earthquake is obtained through inversion in the steps 4 and 51The point number of 90-meter interval point-shaped earthquake landslide danger level space vector data with landslide attribute of 1 in ith kilometer grid unit is obtained through inversion in the steps 4 and 5。
X of other kilometer grid cells in the table5、X4、X3All of (1) are 0, ddi、rriAlso 0, is not listed again for space.
6(3) r for 10350 kilometer gridsriAre each independently of X5、X4、X3、X2、X1The number of (a) was subjected to tabulation analysis and study rriAnd XiThe coincidence relation of (a).
TABLE 2.X5And rriLinked list
Primitive hypothesis H0：X5And rriIndependent of each other
Alternative hypothesis H1：X5And rriPositive phase congruence
Test p-value 1.773E-93
The coefficient of coherence: 0.2359
And (4) conclusion: is remarkably X5Is equal to 0, tends to rriIs equal to 0; x5Is greater than 0, tends to be rriIs greater than 0.
TABLE 3.X4And rriLinked list
Primitive hypothesis H0：X4And rriIndependent of each other
Alternative hypothesis H1：X4And rriPositive phase congruence
Test p-value 4.67E-184
The coefficient of coherence: 0.1718
And (4) conclusion: is remarkably X4Is equal to 0 and is,tend to rriIs equal to 0; x4Is greater than 0, tends to be rriIs greater than 0
TABLE 4.X3And rriLinked list
Primitive hypothesis H0：X3And rriIndependent of each other
Alternative hypothesis H1：X3And rriPositive phase congruence
Test p-value 8.854E-34
The coefficient of coherence: 0.1389
And (4) conclusion: is remarkably X3Is equal to 0, tends to rriIs equal to 0; x3Is greater than 0, tends to be rriIs greater than 0.
TABLE 5.X2And rriLinked list
Primitive hypothesis H0：X2And rriIndependent of each other
Alternative hypothesis H1：X2And rriNegative phase of coincidence
Test p-value 8.854E-34
The coefficient of coherence: -0.0321
And (4) conclusion: x2Is equal to 0, tends to rriIs greater than 0; x2Is greater than 0, tends to be rriIs equal to 0.
TABLE 6.X1And rriLinked list
Primitive hypothesis H0：X1And rriIndependent of each other
Alternative hypothesis H1：X1And rriNegative phase of coincidence
Test p-value 9.26E-106
The coefficient of coherence: -0.2513
And (4) conclusion: is remarkably X1Is equal to 0, tends to rriIs greater than 0; x1Is greater than 0, tends to be rriIs equal to 0.
Analysis of the list shows r for each kilometer gridriAnd X in the grid5、X4、X3、X2、X1When X is present5、X4、X3When the number of (2) is greater than 0, rriTendency to be larger than 0, X5、X4、X3Number of (2) and rriPositive phase combination; when X is present2、X1When the number of (2) is greater than 0, rriTendency of value equal to 0, X2、X1Number of (2) and rriValues greater than 0 are negative relative.
R in 10350 kilometer grid in Table 1riAnd X5、X4、X3、X2、X1As sample data, according to X5、X4、X3Number of (2) and rriA positive phase when the value is greater than 0 and X2、X1Number of (2) and rriA coincidence relation of more than 0 negative coincidence for rdi＝f(x1，x2，x3，x4，x5) And (6) solving. Finding X from the sample5、X4、X3、X2、X1Are 0.04077, 0.03130, 0.01365, 0.01666, 0.09652, respectively. According to a coincidence relation, X5、X4、X3Takes a positive value of X2、X1Taking a negative value as the coefficient of (a), and solving the earthquake landslide mortality index calculation model r according to sample datadiThe following were used:
7. the number d of people dead in earthquake landslide of each kilometer of grid unit obtained in the step 6 is calculated in sequence by the following formulai：
In the formula diRepresenting the number of earthquake landslide deaths for the ith kilometer grid cell.
piRepresenting the population of the ith kilometer grid cell obtained by step 3.
rdiAn earthquake landslide personnel mortality index representing the ith kilometer grid cell obtained by step 6 is shown.
X5Point number X representing 90 m interval point-like earthquake landslide hazard level space vector data with landslide attribute of 5 in ith kilometer grid cell obtained by step 54Point number X representing 90 m interval point-like earthquake landslide hazard level space vector data with landslide attribute of 4 in ith kilometer grid cell obtained by step 53The point number X of 90-meter interval point-like earthquake landslide risk level space vector data with 3 landslide attributes in the ith kilometer grid unit obtained in the step 5 is shown2Point number X representing 90 m interval point-like earthquake landslide hazard level space vector data with landslide attribute of 2 in ith kilometer grid cell obtained by step 51And 5, representing the point number of 90-meter interval point-like earthquake landslide risk level space vector data with the landslide attribute of 1 in the ith kilometer grid unit.
8. The number of earthquake landslide death people d in the whole research area is calculated by the following formulaall：
In the formula dallRepresenting the ground of kilometers of grid cells throughout the study areaThe total number of dead people in landslide.
diRepresenting the number of earthquake landslide deaths for the ith kilometer grid cell obtained by step 7.
piRepresenting the population of the ith kilometer grid cell obtained by step 3.
rdiAn earthquake landslide personnel mortality index representing the ith kilometer grid obtained by step 6 is shown.
X5Point number X representing 90 m interval point-like earthquake landslide hazard level space vector data with landslide attribute of 5 in ith kilometer grid cell obtained by step 54Point number X representing 90 m interval point-like earthquake landslide hazard level space vector data with landslide attribute of 4 in ith kilometer grid cell obtained by step 53The point number X of 90-meter interval point-like earthquake landslide risk level space vector data with 3 landslide attributes in the ith kilometer grid unit obtained in the step 5 is shown2Point number X representing 90 m interval point-like earthquake landslide hazard level space vector data with landslide attribute of 2 in ith kilometer grid cell obtained by step 51And 5, representing the point number of 90-meter interval point-like earthquake landslide risk level space vector data with the landslide attribute of 1 in the ith kilometer grid unit.
9. For the result d calculated in step 8allAnd (5) carrying out rounding processing to finish the prediction and forecast of the number of dead people in the earthquake landslide in the whole research area. If the result d obtained in step 8 is obtainedallIs an integer, the number of earthquake landslide death in the whole research area is equal to dallItself; if the result d obtained in step 8 is obtainedallThere are two cases for decimal values: if the first digit on the right of the decimal point is greater than or equal to 5, the number of dead people in earthquake landslide in the whole research area is equal to dallThe integral part of (1) is added, if the first digit on the right side of the decimal point is less than 5, the number of earthquake landslide death in the whole research area is equal to dallThe integer part of (2).
The method for predicting the death number of the earthquake landslide persons is suitable for rapidly predicting the death number of the earthquake landslide persons in the same earthquake during earthquake emergency and predicting the death risk of the earthquake landslide persons in the same earthquake in an area during earthquake risk evaluation, the death rate index of the earthquake landslide persons in each kilometer grid can be obtained through calculation of the combination of the risk grade attribute and the number of the earthquake landslide persons at intervals of 90 meters in each kilometer grid, the death number of the earthquake landslide persons in each kilometer grid unit can be obtained through the death rate index of the persons and the population number of the kilometer grids, the death number of the earthquake landslide persons in each kilometer grid unit in the whole area can be accumulated and rounded up, the death number of the earthquake landslide persons in the earthquake emergency can be rapidly predicted accurately, and reasonable emergency response grade suggestions can be given out in the earthquake emergency or anti-seismic landslide person death risk mitigation, so as to meet the requirements of disaster prevention, reduction and relief.
In summary, the prediction method is based on such a logical reasoning: the number of earthquake landslide deaths in the whole research area is equal to the sum of the number of earthquake landslide deaths of each kilometer of grid unit, the number of earthquake landslide deaths of each kilometer of grid unit is the relation between the population number of grid units and the earthquake landslide mortality index, the earthquake landslide mortality index of each kilometer of grid unit and the number combination of earthquake landslide hazard level attributes of each region in the grid unit have an internal relation, and the internal relation can be abstracted into a public functional relation. The logical reasoning holds in return: and establishing a quantity combination function of the earthquake landslide death rate index of the kilometer grid unit and the earthquake landslide hazard grade attributes of each region in the grid unit, so that a function of the number of the death people of the earthquake landslide of the kilometer grid unit, the number of the population of the grid unit and the earthquake landslide death rate index can be established, a function of the number of the death people of the whole research region and the number of the death people of each kilometer grid can be further established, and finally, the effective prediction of the number of the death people caused by the earthquake landslide of the whole research region can be realized.
The method has the advantages that an evaluation object is divided into grid units of 1 kilometer × 1 kilometers, the size and the shape of the evaluation object are unified, the fact that earthquake landslide death is the result that population quantity and landslide size are mutually coupled in spatial distribution is fully considered, starting from the earthquake landslide person mortality of unit grids, the earthquake landslide person mortality index model of kilometer grid units is built by researching the relation between the combination of the risk grade attributes and quantity of earthquake landslide at equal intervals in the unit grids and the earthquake landslide mortality, the death number of the earthquake landslide persons in the kilometer grids is further deduced according to the death index of the grid units, finally, the prediction and the forecast of the death number of the earthquake landslide persons are completed by summing and rounding the death number of all kilometer grid units in the whole area, the problem that the death number of the earthquake landslide persons is estimated roughly and has larger error in post-earthquake emergency rescue decision-making evaluation and pre-earthquake disaster risk evaluation due to the lack of an appropriate method in the past work is solved, and the problem that the earthquake landslide person mortality risk prediction and the earthquake rescue risk prediction of the earthquake are inaccurate through the research area population quantity is solved.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
FIG. 1 is a planar kilometer grid division diagram of "8.03" Ludian M6.5 earthquake disaster area.
FIG. 2 is a distribution diagram of the punctiform natural village/street in the Ludian earthquake disaster area.
FIG. 3 is a plot of kilometer grid population for a blund earthquake disaster area.
FIG. 4 is a 90-meter grid earthquake landslide risk level distribution diagram in a Ludian earthquake disaster area.
FIG. 5 is a distribution diagram of the risk level of the point earthquake landslide in the Ludian earthquake disaster area at intervals of 90 meters.
FIG. 6 is a distribution diagram of the number of dead people in a mile earthquake disaster area and a grid earthquake landslide.
FIG. 7 is a distribution diagram of the number of dead people in each natural village/community earthquake landslide in the Ludian earthquake disaster area
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments.
The Zhaotong Ludian area is in the south of the north and south of China, the earthquake activity is strong, the earthquake disaster loss risk is high, and in addition, most areas belong to the Wumeng mountain system, the mountain height is steep, and a large amount of mountain landslides are usually induced during earthquakes. In addition, due to historical reasons, the shotong ludian area has dense population, and the earthquake landslide often causes serious casualties and aggravates the earthquake disaster loss. Therefore, emergency aid decisions in shotong ludian areas after a destructive earthquake occurs require accurate prediction of the number of people died due to the same-earthquake landslide in order to initiate a reasonable emergency response level. 3 days 8 months 2014, Ludian grade 6.5 earthquake caused a great amount of mountain collapse and landslide, and it was found that 134 people died from earthquake landslide by detailed adjustment later. The death number prediction method of the earthquake landslide personnel based on the kilometer grid unit is used for predicting and forecasting the death number of the earthquake landslide personnel in earthquake emergency rescue decisions.
The method comprises the steps of firstly, generating planar kilometer grid space vector data of a research area, carrying out grid division on the whole research area by utilizing GIS software to obtain 1 kilometer × 1 kilometer planar attribute space vector data, wherein each grid is a square with the length of 1 kilometer × 1 kilometers, and uniformly evaluating the shape and the scale of a unit through grid division to generate the planar kilometer grid space vector data of the whole Ludian earthquake disaster area, wherein the planar kilometer grid space vector data are shown in figure 1.
The second step is that: and collecting the data of the office population space of the punctiform natural village/street in the Ludian earthquake disaster area. Obtaining the vector space data of the natural villages and the street offices in the whole Ludian earthquake disaster area through vectorization of a 1:5 ten thousand topographic maps (as shown in figure 2), then obtaining the population quantity information of each natural village and street office from a statistical yearbook, and taking the population quantity information as the attribute information of the corresponding space data of the natural villages and the streets office. And generating punctate natural village/street office population space data of the whole Ludian earthquake disaster area.
The third step: and carrying out population quantity attribute assignment on the whole Ludian earthquake disaster area attribute kilometer grid space vector data obtained in the first step. And assigning the population sum of the point-like natural village/street office population spatial data which spatially fall in each kilometer grid unit to the corresponding kilometer grid by using a spatial attribute correlation module of GIS software, so that all kilometer grid units in the whole research area have spatially corresponding population attributes, and obtaining the facial kilometer grid spatial population vector data of the whole research area. The spatial distribution of the population number of the planar kilometer grid in the entire meadow earthquake disaster area obtained by the step is shown in fig. 3.
And fourthly, generating space vector data of the risk level of the point earthquake landslide in the whole Ludian earthquake disaster area at intervals of 90 meters. The method is characterized in that the GIS software is utilized to generate earthquake landslide risk level distribution data of the whole research area through an earthquake landslide risk assessment method (the method is the prior art), the earthquake landslide risk level data is 90-meter grid space data, the data attribute values are divided into 5 levels in total, and the data are 1, 2, 3, 4 and 5 in sequence from low to high according to landslide risk (as shown in figure 4). And then converting the seismic landslide risk level space data of the 90-meter grids in the entire Ludian seismic disaster area into dot-shaped seismic landslide risk level space vector data at intervals of 90 meters, assigning the attribute value of each grid to the corresponding dot-shaped vector data at intervals of 90 meters during conversion, and finally generating the dot-shaped seismic landslide risk level space vector data at intervals of 90 meters in the entire research area (as shown in figure 5).
The fifth step: and carrying out landslide risk grade attribute assignment on the vector data of the attribute kilometer grid space population of the Ludian earthquake disaster area obtained in the third step. And counting the number of 90-meter interval point-like earthquake landslide danger level space vector data of each landslide danger level which completely falls in each planar kilometer grid space population vector data in space by using a space attribute correlation module of GIS software. And sequentially assigning the landslide hazard level in each grid and the number of points of the level to corresponding planar kilometer grid space population vector data, and finally obtaining the planar kilometer grid vector data with population, landslide hazard level and quantity of each level in the whole Ludian earthquake disaster area.
And a sixth step: sequentially calculating the earthquake landslide personnel mortality index r of each kilometer of grid units in the whole Ludian earthquake disaster area obtained in the fifth step through the following formuladi：
In the formula rdiAnd (3) representing the earthquake landslide personnel mortality index of the ith kilometer grid of the Ludian earthquake disaster area.
5Point number X of 90-meter interval point-like earthquake landslide danger level space vector data with landslide attribute of 5 in ith kilometer grid of the Ludian earthquake disaster area obtained in the fifth step4Point number X of 90-meter interval point-like earthquake landslide danger level space vector data with landslide attribute of 4 in ith kilometer grid of the Ludian earthquake disaster area obtained in the fifth step3The point number X of 90-meter interval point-like earthquake landslide danger level space vector data with landslide attribute of 3 in the ith kilometer grid of the Ludian earthquake disaster area obtained in the fifth step2Point number X of 90-meter interval point-like earthquake landslide danger level space vector data with landslide attribute of 2 in ith kilometer grid of the Ludian earthquake disaster area obtained in the fifth step1And (4) representing the point number of 90-meter interval point-like earthquake landslide danger level space vector data with landslide attribute of 1 in the ith kilometer grid of the Ludian earthquake disaster area obtained in the fifth step.
The seventh step: sequentially calculating the number d of earthquake landslide death persons of each kilometer of grid units in the whole Ludian earthquake disaster area obtained by the sixth step by the following formulai：
In the formula diThe number of earthquake landslide death of the ith kilometer grid of the entire Ludian earthquake disaster area is shown.
piAnd (3) representing the population of the ith kilometer grid of the whole Ludian earthquake disaster area obtained by the third step.
rdiAnd (3) representing the earthquake landslide person mortality index of the ith kilometer grid of the whole Ludian earthquake disaster area obtained by the sixth step.
X5Showing the ith kilometer net of the Ludian earthquake disaster area obtained by the fifth stepNumber of points of 90 m interval punctual earthquake landslide hazard level space vector data with an intra-grid landslide attribute of 5, X4Point number X of 90-meter interval point-like earthquake landslide danger level space vector data with landslide attribute of 4 in ith kilometer grid of the Ludian earthquake disaster area obtained in the fifth step3The point number X of 90-meter interval point-like earthquake landslide danger level space vector data with landslide attribute of 3 in the ith kilometer grid of the Ludian earthquake disaster area obtained in the fifth step2Point number X of 90-meter interval point-like earthquake landslide danger level space vector data with landslide attribute of 2 in ith kilometer grid of the Ludian earthquake disaster area obtained in the fifth step1And (4) representing the point number of 90-meter interval point-like earthquake landslide danger level space vector data with landslide attribute of 1 in the ith kilometer grid of the Ludian earthquake disaster area obtained in the fifth step.
The prediction of the number of people who die from earthquake landslide in each kilometer of the grid unit in the entire Ludian earthquake disaster area is shown in FIG. 6, and the spatial distribution prediction result is basically consistent with the spatial distribution (shown in FIG. 7) of the number of people who die from earthquake landslide in each natural village/community in the earthquake disaster area surveyed on site after earthquake.
Eighth step: the number of the dead people in the earthquake landslide of the entire Ludian earthquake disaster area is calculated by the following formula:
in the formula dallThe number of dead people in earthquake landslide in the entire Ludian earthquake disaster area is shown.
diAnd (4) representing the number of earthquake landslide deaths of the ith kilometer grid of the whole Ludian earthquake disaster area obtained in the seventh step.
piAnd (3) representing the population of the ith kilometer grid of the whole Ludian earthquake disaster area obtained by the third step.
rdiAnd (3) representing the earthquake landslide person mortality index of the ith kilometer grid of the whole Ludian earthquake disaster area obtained by the sixth step.
X5Showing the Ludian earthquake disaster area obtained by the fifth stepNumber of points of 90 m interval point-like earthquake landslide hazard level space vector data with landslide attribute of 5 in ith kilometer grid, X4Point number X of 90-meter interval point-like earthquake landslide danger level space vector data with landslide attribute of 4 in ith kilometer grid of the Ludian earthquake disaster area obtained in the fifth step3The point number X of 90-meter interval point-like earthquake landslide danger level space vector data with landslide attribute of 3 in the ith kilometer grid of the Ludian earthquake disaster area obtained in the fifth step2Point number X of 90-meter interval point-like earthquake landslide danger level space vector data with landslide attribute of 2 in ith kilometer grid of the Ludian earthquake disaster area obtained in the fifth step1And (4) representing the point number of 90-meter interval point-like earthquake landslide danger level space vector data with landslide attribute of 1 in the ith kilometer grid of the Ludian earthquake disaster area obtained in the fifth step.
The ninth step: for the result d of the eighth stepallAnd (4) carrying out rounding processing to complete the prediction and forecast of the number of people who die from earthquake landslides in the entire Ludian earthquake disaster area. The number d of dead people in earthquake landslides in the Ludian earthquake disaster area is obtained through the eighth stepall128.7532 people, according to the rule that if the first digit after the decimal point is more than or equal to 5, the number of earthquake landslide death in the whole research area is equal to dallThe integral part of (1) is added, and the predicted prediction number of the death people in the earthquake landslide in the Ludian earthquake disaster area is 128+1, namely 129 people.
In addition, aiming at other earthquake conditions in China, the method disclosed by the invention is adopted to carry out prediction results as follows (the steps are the same as above and are not described in detail):
| example of earthquake | Actual earthquake landslide death | The invention predicts earthquake landslide death |
| "5.12" Wenchuan 8.0 grade earthquake | About 20000 people | 18732 human (S) |
| 9.07 Yi 5.7 and 5.6 grade earthquake | 59 human being | 62 persons |
| "7.22" zhangxian county 6.6 grade earthquake | 14 persons | 15 persons |
| 8.31 Shangri-De Rong grade 5.9 |
1 |
1 person |
| "8.03" Ludian 6.5 grade earthquake | 134 person | 129 persons |
| Grade 6.6 earthquake of '10.7' |
0 |
0 person |
The foregoing illustrates and describes the principles, general features, and advantages of the present invention. It should be understood by those skilled in the art that the above embodiments do not limit the scope of the present invention in any way, and all technical solutions obtained by using equivalent substitution methods fall within the scope of the present invention. The parts not involved in the present invention are the same as or can be implemented using the prior art.
Claims (4)
1. A method for predicting the number of people who die in earthquake landslide, wherein the death in earthquake landslide is the life loss of people caused by landslides caused by the same earthquake, is characterized in that: the prediction method considers the population number of kilometer grid units and the combination of the risk level and the number of landslides with the resolution of 90 meters in a grid, and comprises the following specific prediction steps:
A. carrying out grid division on the whole research area by using GIS software to obtain surface attribute space vector data of × 1 km, wherein each grid is a square of × 1 km, and uniformly evaluating the shape and the scale of a unit by grid division;
B. collecting the vector data of the spotted spatial population in the research area; obtaining point-like space vector data of the study area population permanent region through large-scale topographic map vectorization, then obtaining population quantity information of each point from various statistical yearbooks, and taking the population quantity information as population attribute information of corresponding study area population permanent region space data;
C. carrying out population quantity attribute assignment on the planar kilometer grid space vector data obtained in the step A; assigning the population sum of the point population space vector data falling in each kilometer grid unit in space to the corresponding kilometer grid unit by using a space attribute correlation module of GIS software, so that all kilometer grid units in the whole research area have spatially corresponding population attributes, and obtaining the face kilometer grid space population vector data of the whole research area;
D. generating space vector data of the risk level of the punctual earthquake landslide in the research area at intervals of 90 meters; generating earthquake landslide risk grade distribution data of the whole research area by using GIS software through an earthquake landslide risk grade evaluation method; the danger level data is 90 m raster space data, the attribute values of the raster data are divided into 5 levels, and the risk levels are 1, 2, 3, 4 and 5 in sequence from low to high according to the landslide danger; converting the seismic landslide risk grade space data of the grids of 90 meters in the whole research area into point-shaped seismic landslide risk grade space vector data at intervals of 90 meters, assigning the attribute value of each grid to the converted points of the grids as landslide risk grade attributes of the points during conversion, and finally generating point-shaped seismic landslide risk grade space vector data at intervals of 90 meters in the whole research area;
E. c, further carrying out earthquake landslide risk grade attribute assignment on the planar kilometer grid space population vector data obtained in the step C; counting the number of 90-meter interval point-like earthquake landslide danger level space vector data of each landslide danger level which completely falls in each planar kilometer grid space population vector data in space by using a space attribute correlation module of GIS software; sequentially assigning the landslide hazard level and the number of points of the level in each grid to corresponding planar kilometer grid space population vector data to finally obtain the planar kilometer grid space vector data of the population number, the landslide hazard level and the landslide point number of each level in the whole research area;
F. e, sequentially calculating the death rate index r of the earthquake landslide personnel of each kilometer of grid unit according to the data obtained in the step EdiAnd the number of dead people on the earthquake landslide of each kilometer of grid unitiAnd the number of earthquake landslide deaths d in the whole research areaall(ii) a The specific calculation method is as follows:
earthquake landslide personnel mortality index r of each kilometer of grid unitdiCalculated by the following formula:
the number of dead people on earthquake landslide of each kilometer of grid unitiCalculated by the following formula:
the number of earthquake landslide deaths d in the whole research areaallCalculated by the following formula:
in the above formulas:
direpresenting the number of earthquake landslide deaths for the ith kilometer grid cell;
pirepresenting the population of the ith kilometer grid cell obtained by step C;
rdif, representing the earthquake landslide personnel mortality index of the ith kilometer grid unit obtained in the step F;
dallrepresenting the total number of dead people in earthquake landslide of each kilometer of grid units in the whole research area;
X5point number X representing 90 m interval point-like earthquake landslide hazard level space vector data having landslide attribute of 5 in ith kilometer grid cell obtained by step E4Point number X representing 90 m interval point-like earthquake landslide hazard level space vector data with landslide attribute of 4 in ith kilometer grid cell obtained by step E3E, the point number X of 90-meter interval point-like earthquake landslide danger level space vector data with 3 landslide attributes in the ith kilometer grid unit is obtained in the table2Point number X representing 90 m interval point-like earthquake landslide hazard level space vector data with landslide attribute of 2 in ith kilometer grid cell obtained by step E1And E, representing the point number of 90-meter interval point-like earthquake landslide risk level space vector data with the landslide attribute of 1 in the ith kilometer grid unit obtained in the step E.
2. The method for predicting the number of dead people in landslide according to claim 1, wherein: for the calculated result dallCarrying out rounding processing to finish the prediction and forecast of the number of dead people in the earthquake landslide in the whole research area; if the result d is obtainedallIs an integer, the number of earthquake landslide death in the whole research area is equal to dallItself; if the result d of step F is reachedallThere are two cases for decimal values: if the first digit on the right of the decimal point is greater than or equal to 5, the number of dead people in earthquake landslide in the whole research area is equal to dallThe integer part of (1) is added, if the first digit on the right side of the decimal point is less than 5, the earthquake slip of the whole research area is detectedThe number of dead people on the slope is equal to dallThe integer part of (2).
3. The method for predicting the number of dead people in landslide according to claim 1, wherein: the sum of the number of earthquake landslide danger grade points at intervals of 90 meters in any kilometer grid unit is relatively uniform, the number range is (0,134), the average value is 124, and the earthquake landslide personal mortality index value range of any kilometer grid unit is [0,1 ].
4. The use of the method for predicting the number of dead people in earthquake landslide of claim 1, wherein: the prediction method is suitable for earthquake disaster damage risk assessment before earthquake for reducing disaster risk, earthquake emergency rescue aid decision assessment for starting reasonable emergency response level after earthquake and prediction and forecast of earthquake landslide death number.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910354022.0A CN110070234B (en) | 2019-04-29 | 2019-04-29 | Earthquake landslide personnel death number prediction method and application thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910354022.0A CN110070234B (en) | 2019-04-29 | 2019-04-29 | Earthquake landslide personnel death number prediction method and application thereof |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110070234A CN110070234A (en) | 2019-07-30 |
| CN110070234B true CN110070234B (en) | 2020-06-30 |
Family
ID=67369448
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910354022.0A Active CN110070234B (en) | 2019-04-29 | 2019-04-29 | Earthquake landslide personnel death number prediction method and application thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110070234B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115565062B (en) * | 2022-09-05 | 2023-06-16 | 应急管理部国家自然灾害防治研究院 | Earthquake geological disaster and property population loss risk prediction method and system |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| AU2013100104A4 (en) * | 2013-02-04 | 2013-03-07 | Beijing Normal University | An assessment method of earthquake casualty |
| CN105809266A (en) * | 2014-12-31 | 2016-07-27 | 郑静晨 | Method for preliminary evaluation and prediction on earthquake casualty |
| CN107944722A (en) * | 2017-12-01 | 2018-04-20 | 河南奔云实业有限公司 | A kind of minor's drowning public safety index statistical method |
| CN108960481A (en) * | 2018-05-22 | 2018-12-07 | 北京师范大学 | Earthquake disaster injures and deaths population estimation method and computer-readable medium |
-
2019
- 2019-04-29 CN CN201910354022.0A patent/CN110070234B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN110070234A (en) | 2019-07-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106651211A (en) | Different-scale regional flood damage risk evaluation method | |
| CN113283802A (en) | Landslide risk assessment method for complex and difficult mountain area | |
| CN104217257A (en) | Integrated risk calculating method of disaster chain | |
| CN110263109A (en) | A kind of family's amount evaluation method merging internet information and GIS technology | |
| CN116151483B (en) | Regional rainfall landslide probabilistic prediction method and prediction terminal | |
| Yazdani et al. | Statistical prediction of the sequence of large earthquakes in Iran | |
| CN113139760A (en) | Typhoon risk comprehensive evaluation method and system based on wind and rain big data | |
| CN110070234B (en) | Earthquake landslide personnel death number prediction method and application thereof | |
| CN112923900B (en) | Method and device for judging differential settlement of building based on RS technology | |
| CN113378396A (en) | Early identification method for hidden danger points of small watershed geological disaster | |
| CN112651611A (en) | Resource environment bearing capacity evaluation method based on geospatial intelligent analysis | |
| CN115860213A (en) | Multi-scenario city emergency sampling place site selection optimization method, system, equipment and medium | |
| Custer et al. | A COMPARISON OF HAND‐AND SPLINE‐DRAWN PRECIPITATION MAPS FOR MOUNTAINOUS MONTANA 1 | |
| CN114842163A (en) | Multi-scale dynamic gridding model construction method for disaster assessment | |
| CN113689048A (en) | Method, system and computer-readable storage medium for predicting refined spatial distribution of future population | |
| Chu et al. | A Hybrid Intelligent Model for Urban Seismic Risk Assessment from the Perspective of Possibility and Vulnerability Based on Particle Swarm Optimization | |
| Khalik Al-Tae et al. | Prediction analysis of trip production using cross-classification technique | |
| Lv et al. | Analysis on the Spatiotemporal Characteristics of the Postearthquake Reconstruction Efficiency of the extremely earthquake-stricken areas by the Wenchuan earthquake based on the DEA-Malmquist index | |
| Li | Assessment of flood hazard risk based on catastrophe theory in flood detention basins | |
| CN113722878B (en) | Simulation-oriented traffic demand determination method based on identity perception data | |
| CN116756263B (en) | Method for processing town big data based on geographic information data | |
| Zolfaghari et al. | Design and development of a GIS‐based seismo‐tectonic source model for Iran | |
| Setyawan et al. | Multi-variable forecasting model using ARIMA (P, Q, N) method to project number of population in Bandung, Indonesia | |
| CN112923901B (en) | RS and GIS technology-based engineering settlement calculation method and device | |
| Hariyanto et al. | Measurement of Sprawl Effect Based on Urban Growth Trends and Prediction in Kedungkandang District, Malang City |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |