CN112200399A - Earthquake disaster risk assessment and economic loss prediction method - Google Patents

Abstract

The invention provides a method for earthquake disaster risk assessment and economic loss prediction, which comprises the following steps: collecting disaster forming and non-disaster forming data sets P { i } with the earthquake disaster grade larger than 5 grade in history, wherein the disaster forming and non-disaster forming data sets P { i } comprise longitude and latitude coordinates (X) of earthquake occurrence places_i，Y_i) Magnitude of seismic magnitude M_iSeismic intensity grade D_iDirect economic loss of earthquake L_iAnd the number of earthquake disaster population Nap_i(ii) a And the like; the method is combined with historical seismic data to carry out analysis modeling, risk assessment is carried out on the seismic information of the target seismic position and the historical adjacent position by adopting a probability statistics method, and if the target seismic position and the historical adjacent position are judged to be the disaster-causing earthquake, direct economic loss prediction is carried out through a macroscopic economic model. The method can more accurately and effectively evaluate the risk and the loss of the target earthquake, and can be used for researching the defense strategy of the earthquake disaster and guaranteeing the social publicThe safety has high application requirements and prospects.

Description

Earthquake disaster risk assessment and economic loss prediction method

Technical Field

The invention belongs to the technical field of earthquake disaster risk assessment and earthquake economic loss prediction, and particularly relates to an earthquake disaster risk assessment and economic loss prediction method.

Background

Earthquake disasters are common natural disasters, have the characteristics of strong burst property, high persistence, great destructive power and the like, and are also called as the first group of disasters. Particularly, in a densely populated place, once an earthquake disaster occurs, the earthquake disaster is likely to cause production and immeasurable loss of life for human beings. The disaster of earthquake means whether the earthquake will cause direct economic loss or casualty accident. Therefore, once the earthquake disaster occurs, how to effectively evaluate the disaster risk of the earthquake disaster and reasonably predict the economic loss of the disaster-causing earthquake disaster have very important significance for quantitative analysis of the earthquake disaster and development of subsequent earthquake disaster reduction work.

At present, two types of common earthquake disaster loss assessment methods exist, one type is a list clearing vulnerability analysis method based on building and infrastructure structure classification, the method is relatively simple and convenient, but the method usually needs to collect and count large-range data of different areas, and manpower and material resources are consumed by the data, so that the method is not economical for macroscopic decisions of earthquake emergency, disaster relief and the like. Another method is a GDP-based macro vulnerability analysis method, which suffers the total loss of seismic disasters from all social wealth including buildings and infrastructure. The method is simple and easy to implement, and on the data, the GDP values of all regions are updated every year by the country, so that the data updating is easier. Nowadays, the method is widely applied to the actual work of earthquake disaster economic loss prediction. For example, in patent [ CN110046454A ], on a macroscopic vulnerability analysis method, a majority theorem and a full probability formula are combined to determine the probability of earthquake loss occurring at a certain position, so as to further estimate economic loss. However, this method often ignores the disaster analysis of earthquakes. The analysis and evaluation by the method not only consumes certain labor cost, but also may cause inaccurate evaluation data. If the earthquake disaster risk is evaluated to determine the disaster possibility of the earthquake when the earthquake occurs, and then the loss prediction of the earthquake disaster is carried out, the working efficiency and the prediction accuracy are greatly improved.

Aiming at the problems, a method for earthquake disaster risk assessment and economic loss prediction needs to be designed, and the method can meet the current requirements for earthquake disaster risk assessment and earthquake disaster loss prediction.

Disclosure of Invention

In view of the above, the invention provides a method for earthquake disaster risk assessment and economic loss prediction. According to historical earthquake disaster damage report evaluation of China continental province, earthquake data are collected and sorted, a rapid and accurate earthquake disaster risk assessment and economic loss prediction method is constructed, workers only need to use longitude and latitude and magnitude of a place where an earthquake occurs newly as input of an earthquake disaster risk assessment model, if the earthquake disaster risk assessment model is judged to be a disaster event, a GPD value of the area in the current year is obtained, and loss prediction is carried out through a macroscopic economic vulnerability model to predict possible direct economic loss. By adopting the method, the accuracy of prediction and the working efficiency of workers can be greatly improved, and the labor cost and the hardware cost are reduced.

In order to achieve the purpose, the invention provides the following technical scheme:

the invention relates to a method for evaluating earthquake disaster risk and predicting economic loss, which comprises 1, a historical earthquake data collecting module, a data processing module and a data processing module, wherein the historical earthquake data collecting module is mainly used for extracting earthquake records of more than 5 grades of disasters and non-disasters in historical data, and the earthquake records comprise longitude and latitude of an earthquake place, earthquake magnitude, casualty number, disaster population number, earthquake intensity and direct economic loss; 2. the earthquake disaster risk assessment model construction module is used for judging whether the current earthquake disaster is in a disaster state or not through historical data; 3. the earthquake disaster data acquisition module aims to collect real-time data of the current earthquake disaster, wherein the real-time data comprises longitude and latitude, earthquake magnitude and GDP of a current place in the same year, and the real-time data is analyzed; 4. and the intensity regression model construction module is used for carrying out linear regression on the seismic intensity through historical seismic series. 5. The macro-economic vulnerability model construction module is used for fitting an economic vulnerability model according to historical intensity data and economic loss; 6. the direct economic loss prediction module aims at predicting the direct economic loss of earthquake disaster.

A method for earthquake disaster risk assessment and economic loss prediction roughly comprises the following steps:

step (1): collecting disaster forming and non-disaster forming data sets P { i } with the earthquake disaster grade larger than 5 grade in history, wherein the disaster forming and non-disaster forming data sets P { i } comprise longitude and latitude coordinates (X) of earthquake occurrence places_i，Y_i) Magnitude of seismic magnitude M_iSeismic intensity grade D_iDirect economic loss of earthquake L_iAnd the number of earthquake disaster population Nap_i。

Step (2): obtaining longitude and latitude coordinates (X) of target seismic position T_T，Y_T) Seismic level M_TGDP of the same year in this region_T。

And (3): and (3) screening the disaster forming times N1 and the disaster non-forming times N2 with the distance of less than 200 kilometers between the target earthquake position and the historical data P { i } through the following formulas (1) and (2).

Where R represents the radius of the earth, typically 6378.137 kilometers, d (x) represents the distance between the seismic target location and the historical seismic location, and if the distance is less than or equal to 200 kilometers, d (x) is 1, otherwise d (x) is 0.

N1 indicates that in all historical seismic data sets P { i }, the earthquake is a disaster and is less than 200 kilometers away from the target earthquake T.

N2 indicates that in all historical seismic data sets P { i }, the earthquake is not disaster and is less than 200 kilometers away from the target earthquake T.

And (4): and (3) judging the ratio of N1 to N2, if N1/(N1+ N2) >0.5, indicating that the target earthquake is possible to cause a disaster, carrying out the next step, and if not, indicating that the possibility of the earthquake disaster is not high, returning to the step (2) to continuously acquire corresponding earthquake data.

And (5): if the target earthquake T is judged to be possibly in disaster, the earthquake magnitude set M in the set P { i } and in the disaster-forming earthquake data with the distance less than 200 kilometers away from the target earthquake T is continuously screened_aAnd seismic intensity set D_aWherein a is<I. Since the intensity and magnitude of the earthquake are always positively correlated, a unary linear regression method can be used to perform linear fitting on the earthquake, and D is k × M + b. Wherein the fitting coefficients b and k can be calculated by formula (3) and formula (4).

And (6): according to the magnitude M of the target earthquake obtained in the step (2)_TAnd (5) calculating the intensity M of the target seismic position by the fitting equation in the step (5)_DWherein M is_D＝k×M_T+b。

And (7): through the historical seismic data set P { i }, a loss rate formula about intensity and GDP is constructed as follows:

F(M,GDP)＝C×A×M^B (5)

wherein, F (I, GDP) represents GDP loss rate, M is seismic intensity, A, B is vulnerability statistical parameter, and C is correction coefficient.

And (8): according to the current year GDP value M of the target earthquake area obtained in the step (2)_GAnd substituting the formula (5) to predict the direct economic LOSS value LOSS of the area:

LOSS＝C×A×M_D ^B×M_G (6)

compared with the prior art, the invention has the beneficial effects that:

the method is mainly used for analyzing earthquake dangerousness, but is less in hazard analysis of earthquake disasters. Furthermore, this method requires and is exhaustive of the large amounts of data in building, geological, socioeconomic aspects, which are often difficult to obtain. However, the general macro-economic vulnerability model usually ignores the risk assessment analysis of the earthquake disaster, and the method can predict the economic loss of the earthquake disaster if the earthquake disaster is discovered under the normal condition, which often lacks certain rationality. Therefore, the method provided by the invention is combined with historical seismic data to carry out analysis modeling, risk assessment is carried out on the seismic information of the target seismic position and the historical adjacent position by adopting a probability statistics method, and if the target seismic position and the historical adjacent position are judged to be the disaster-causing earthquake, direct economic loss prediction is carried out through a macroscopic economic model. The method can more accurately and effectively evaluate the risk and the loss of the target earthquake, and has high application requirements and prospects for the research of the defense countermeasures of earthquake disasters and the guarantee of social public security.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

Fig. 1 is an overall flowchart of a method for evaluating earthquake disaster risk and predicting economic loss according to an embodiment of the present invention;

fig. 2 is a visual display diagram of the target earthquake and the historical earthquake position in the embodiment of the invention.

Detailed Description

The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.

The embodiment of the invention provides a flow chart of a method for evaluating earthquake disaster risk and predicting economic loss, and the method takes earthquake disaster data of more than 5 grades in 2019 as an example, which is shown in a table 1.

Table 1, 2019 earthquake disaster data above grade 5

The embodiment of the invention provides a method for evaluating earthquake disaster risk and predicting economic loss, which mainly comprises the following steps:

step (1): collecting data set P { i } of 5-grade or more earthquake disaster and non-disaster in 2005-2018, wherein the set comprises longitude and latitude coordinates (X) of earthquake occurrence places in 14 years_i，Y_i) Seismic magnitude M_iSeismic intensity grade D_iDirect economic loss of earthquake L_iAnd the number of earthquake disaster population Nap_i。

Step (2): the data of 8 earthquake disaster events from 2019 are obtained and shown in table 1, which includes a seismic magnitude set M_T{ i }, set of latitude and longitude coordinates (X)_T{i}，Y_T{ i }), and GDP of 2018 of that place_T160.8 billion yuan.

And (3): and (4) screening the target earthquake position and the earthquake with the distance of less than 200 kilometers from the target earthquake position in the historical data P { i } through the following formulas (7) and (8), and visually displaying the position of the target earthquake position and the earthquake with the distance of less than 200 kilometers from the target earthquake position through the graph 2.

Wherein the range of i is 1 to 8, N1 represents the disaster forming times and the disaster non-forming times of the target earthquake position in the historical earthquake, N2 represents the disaster non-forming times, and N1 and N2 can be obtained through calculation.

And (4): and judging the proportion of N1 and N2, wherein N1/(N1+ N2) >0.5 indicates that the target earthquake has 50% of disaster risk possibility, and direct economic loss prediction needs to be carried out on the target earthquake.

And (5): screening a seismic magnitude set M in disaster-forming seismic data with a distance to a target earthquake-T smaller than 200 kilometers in a set P { i }_aAnd seismic intensity set D_aWherein a is<I. Since the intensity and magnitude of the earthquake are always positively correlated, a unary linear regression method can be adopted to perform linear fitting on 14 sets of magnitude and intensity data obtained in the step (3), wherein fitting coefficients b and k can be calculated by a formula (9) and a formula (10);

here, k { i }, b { i } can be obtained by calculation. Thus, a linear fit equation for magnitude-intensity can be expressed as M { i } -, k { i } × D + b { i }.

And (6): target seismic intensity D_T{ i }, which is introduced into the linear equation, the earthquake intensity can be predicted to be M_T{i}＝k{i}×D_T{i}+b{i}。

And (7): through a historical seismic data set P { i }, the loss rate of the intensity and the GDP is constructed, and according to 2019, the GDP of the local population is more than 10000 yuan, so that the relationship between the loss rate F (M, GDP) and the intensity M can be obtained as shown in a formula (11):

F(M,GDP)＝4.0×10^-12×M_T{i}^12.67 (11)

and (8): the predicted intensity M in the step (6) is compared_TAnd { i } is substituted into the formula (11), and the economic loss rate possibly caused by the intensity is obtained as F (M, GDP) { i }.

And (9): combining the GDP set GDP of 2018 in the earthquake disaster area with the level of more than 5 in 2019 obtained in the step (2)_TI billion, directly connects F (M, GDP) { i } with GDP_T{ i } multiplication, i.e. predicted direct economic loss:

LOSS{i}＝F(M,GDP){i}×GDP_T{i}。

according to investigation, the actual direct economic loss caused by the earthquake of more than 5 grades in 2019 and the prediction result are shown in the table 2, and the test result shows that the actual economic loss of the actual 8 earthquakes is very close to the prediction value of the scheme. Therefore, the method can be further proved to be truly feasible for earthquake risk assessment and earthquake direct economic loss prediction.

TABLE 2, economic loss and prediction result of earthquake disaster of more than 5 grade in 2019

Various alterations and modifications will no doubt become apparent to those skilled in the art after having read the above description. Therefore, the appended claims should be construed to cover all such variations and modifications as fall within the true spirit and scope of the invention. Any and all equivalent ranges and contents within the scope of the claims should be considered to be within the intent and scope of the present invention.

Claims (1)

1. A method for earthquake disaster risk assessment and economic loss prediction is characterized by comprising the following steps:

step (1): collecting disaster forming and non-disaster forming data sets P { i } with the earthquake disaster grade larger than 5 grade in history, wherein the disaster forming and non-disaster forming data sets P { i } comprise longitude and latitude coordinates (X) of earthquake occurrence places_i，Y_i) Seismic magnitude set M_iSeismic intensity level set D_iEarthquakeDirect economic loss L of_iAnd the number of earthquake disaster population Nap_i；

Step (2): obtaining longitude and latitude coordinates (X) of target seismic position T_T，Y_T) Seismic magnitude set M_TGDP of the same year in this region_T；

And (3): screening disaster times N1 and disaster times N2 with the distance of less than 200 kilometers from the target earthquake position and historical data P { i } through the following formula (1) and formula (2);

if P { i } is in disaster, D (x) ═ 1 (1)

If P { i } is not in disaster, D (x) ═ 1 (2)

Wherein R represents the radius of the earth, d (x) represents the distance between the seismic target location and the historical seismic location, d (x) 1 if the distance is less than or equal to 200 kilometers, otherwise d (x) 0;

n1 represents the number of times that an earthquake is in disaster and has a distance of less than 200 kilometers from the target earthquake position T in all historical earthquake data sets P { i };

n2 represents the number of times that the earthquake is not disaster and the distance from the target earthquake position T is less than 200 kilometers in all the historical earthquake data sets P { i };

and (4): judging the proportion of N1 and N2, if N1/(N1+ N2) >0.5, indicating that the target earthquake position is possibly disastrous, and performing the following step (5); otherwise, if the possibility of the earthquake disaster is not high, returning to the step (2) to continuously acquire the corresponding earthquake data;

and (5): if the target earthquake position T is judged to be possibly in disaster, the earthquake magnitude set M in the disaster-forming earthquake data with the distance less than 200 kilometers away from the target earthquake T in the data set P { i } is continuously screened_aAnd seismic intensity ratingSet D_aWherein a is<＝i；

Because the intensity of the earthquake and the magnitude of the earthquake are always in positive correlation, linear fitting is carried out on the earthquake by adopting a unitary linear regression method to obtain D (k multiplied by M + b);

wherein the fitting coefficients b and k are calculated by the following formula (3) and formula (4);

and (6): according to the magnitude M of the target earthquake obtained in the step (2)_TCalculating the intensity M of the target seismic position through the fitting equation in the step (5)_DWherein M is_D＝k×M_T+b；

And (7): through the historical seismic data set P { i }, a loss rate formula about intensity and GDP is constructed as follows

F(M,GDP)＝C×A×M^B (5)

Wherein F (I, GDP) represents GDP loss rate, M is seismic intensity, A, B is vulnerability statistical parameter, and C is correction coefficient;

and (8): according to the current year GDP value M of the target earthquake area obtained in the step (2)_GAnd substituting the formula (5) to predict the direct economic LOSS value LOSS of the area:

LOSS＝C×A×M_D ^B×M_G (6)。

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