CN111401682A - Earthquake casualty mouth assessment method and system - Google Patents
Earthquake casualty mouth assessment method and system Download PDFInfo
- Publication number
- CN111401682A CN111401682A CN202010086186.2A CN202010086186A CN111401682A CN 111401682 A CN111401682 A CN 111401682A CN 202010086186 A CN202010086186 A CN 202010086186A CN 111401682 A CN111401682 A CN 111401682A
- Authority
- CN
- China
- Prior art keywords
- disaster
- case
- historical
- historical case
- earthquake
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 27
- 238000011156 evaluation Methods 0.000 claims abstract description 199
- 238000012937 correction Methods 0.000 claims abstract description 47
- 238000012216 screening Methods 0.000 claims abstract description 25
- 238000013210 evaluation model Methods 0.000 claims abstract description 16
- 238000012163 sequencing technique Methods 0.000 claims description 4
- 239000000969 carrier Substances 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 description 8
- 238000011161 development Methods 0.000 description 8
- 238000010586 diagram Methods 0.000 description 8
- 230000006378 damage Effects 0.000 description 7
- 238000012795 verification Methods 0.000 description 7
- 238000011160 research Methods 0.000 description 6
- 238000004458 analytical method Methods 0.000 description 4
- 238000013528 artificial neural network Methods 0.000 description 4
- 238000012360 testing method Methods 0.000 description 4
- 241001660917 Crassula ovata Species 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 230000001066 destructive effect Effects 0.000 description 2
- 230000008030 elimination Effects 0.000 description 2
- 238000003379 elimination reaction Methods 0.000 description 2
- 238000011158 quantitative evaluation Methods 0.000 description 2
- 241000581616 Aphanes Species 0.000 description 1
- 244000061408 Eugenia caryophyllata Species 0.000 description 1
- 235000016639 Syzygium aromaticum Nutrition 0.000 description 1
- 208000027418 Wounds and injury Diseases 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000012854 evaluation process Methods 0.000 description 1
- 208000014674 injury Diseases 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000012821 model calculation Methods 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 235000020339 pu-erh tea Nutrition 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
Images
Classifications
-
- 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
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- 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
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
- G06Q50/265—Personal security, identity or safety
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A10/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE at coastal zones; at river basins
- Y02A10/40—Controlling or monitoring, e.g. of flood or hurricane; Forecasting, e.g. risk assessment or mapping
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Tourism & Hospitality (AREA)
- Economics (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Computer Security & Cryptography (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Health & Medical Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Geophysics And Detection Of Objects (AREA)
Abstract
The invention relates to a method and a system for evaluating earthquake casualties. The method comprises the following steps: acquiring an earthquake history case library; determining the similarity between the current earthquake disaster and each historical case based on a similarity evaluation model of the Manhattan distance; screening historical cases in the earthquake historical case library according to the similarity to obtain an initial historical case set participating in disaster evaluation; acquiring the spatial correlation degree of each historical case in the initial historical case set and the current earthquake disaster; screening the initial historical case set to obtain a final historical case subset participating in disaster evaluation; determining the weight of each historical case participating in disaster evaluation in the final historical case sample set; acquiring a correction coefficient corresponding to each historical case; and evaluating the number of casualty population of the current earthquake disaster according to the weight of each historical case participating in disaster evaluation in the final historical case sample set and the corresponding correction coefficient. The invention can improve the accuracy of the earthquake casualty population evaluation.
Description
Technical Field
The invention relates to the field of earthquake evaluation, in particular to an earthquake casualty mouth evaluation method and system.
Background
With the development of socio-economy, casualties and economic losses caused by earthquakes are gradually increasing under the condition of the same magnitude of earthquake. The destructive earthquake not only causes great economic loss to the country, but also causes great casualties, and the evaluation of the casualties caused by the earthquake is always the key point of research.
The earthquake casualty population evaluation method mainly comprises an empirical formula method, a probability analysis method and a dynamic evaluation method, wherein the factors influencing earthquake casualty population are numerous, and the influence factors considered when different students construct a model through the empirical formula method are different, Christokov and the like consider that the earthquake death number has high correlation with population density, and construct an earthquake death evaluation model by taking the population density as a parameter, L omni constructs the earthquake evaluation model based on the relation between the earthquake death number and earthquake occurrence time, Wys counts the death number caused by the earthquake in Himalayan areas, induces the relation between the death number and earthquake magnitude and population density, Xing H and the like take the earthquake magnitude, the earthquake intensity, the population density, the earthquake occurrence time and the damaged building area as indexes, fit and improve a Gaussian curve, construct a prediction model, a typical probability analysis method is provided, the probability analysis method is used for obtaining the death rate by using a probability method, a quantitative evaluation system of family casualty by taking the damage degree as a main parameter, a quantitative evaluation method of the earthquake casualty number in a certain earthquake casualty time period, a seismic matrix is constructed, and a dynamic evaluation method of earthquake casualty is provided, and the earthquake casualty evaluation method is provided.
In addition, many scholars at home and abroad also construct earthquake casualty population evaluation models based on different angles, such as Chen chess fortune and the like, consider that the loss caused by the earthquake comprises life loss and economic loss at the same time, and provide an earthquake damage evaluation method capable of reflecting the economic loss. The Wangxingqing and the like analyze 157 times destructive seismic data generated in the continental region of China in 1989-2004, count related data such as population number, total production value in China, earthquake death number and the like in different intensity regions, and regress to obtain an casualty evaluation model taking the average human GDP as a grading standard. Hashmemi et al propose a GIS-based seismic loss assessment method and are applicable to the Irander Blanche region. Clove and the like utilize GIS to develop a system for rapid evaluation and emergency command management of earthquake disasters. The artificial neural network model is applied to earthquake casualty prediction by Aghamammadi and the like, and the building type and the damage degree are used as casualty prediction factors. Muhammet and Ali use earthquake occurrence time, earthquake magnitude and population density as prediction indexes, and use earthquakes above 5 levels in 40 years of Turkey as experimental data to construct an artificial neural network prediction model. The Yanfan and the like establish a model for carrying out casualty prediction on the occurred earthquake by comprehensively using information such as a GIS system, population distribution data, past earthquake damage data and the like on the basis of a BP artificial neural network. In the research of mountains and the like, 20 severe earthquake cases occurring in the mainland of China are selected, and the three-layer BP neural network earthquake injury and casualty assessment model is provided by taking population density, earthquake intensity, house collapse and destruction rate, earthquake damage prediction condition, earthquake fortification standard, earthquake magnitude, earthquake occurrence time and the like as indexes.
At present, in the main methods for evaluating earthquake casualty population, an empirical formula method can quickly obtain an evaluation result at the initial stage of earthquake disaster, but the considered factors are more comprehensive and the evaluation precision is unstable; the probability analysis method and the dynamic evaluation method are relatively complex in calculation, data required by the model are not easy to obtain, and rapid evaluation cannot be performed at the initial stage of earthquake disaster occurrence. The evaluation method based on the historical case aims at the limitation that a small amount of disaster information is obtained at the initial stage of disaster occurrence, and quickly evaluates disaster consequences caused by the current disaster according to the abstracted small amount of disaster information. However, the evaluation method based on historical cases at home and abroad still researches and analyzes the development rules of the historical cases aiming at a certain disaster at present, and predicts the possibility of the future disaster development and the possible influence by comparing the development rules with the current situation; the method has the advantages that the method is rarely used for directly screening the history cases which are similar to the current disasters to evaluate the disaster situations, and the method is only well applied to natural disaster situation evaluation such as typhoon and drought at present, but is not related to earthquake disaster situation evaluation.
Disclosure of Invention
The invention aims to provide a method and a system for evaluating earthquake casualty population under the condition that the acquired disaster information is limited at the initial stage of earthquake disaster occurrence so as to improve the precision of evaluation of earthquake casualty population.
In order to achieve the purpose, the invention provides the following scheme:
a method for earthquake casualty mouth assessment, comprising:
acquiring an earthquake history case library; the earthquake history case base comprises a plurality of history cases;
determining the similarity between the current earthquake disaster and each historical case based on a similarity evaluation model of the Manhattan distance;
screening the historical cases in the seismic historical case library according to the similarity between the current seismic disaster and each historical case to obtain an initial historical case set participating in disaster evaluation;
acquiring the spatial correlation degree of each historical case in the initial historical case set and the current earthquake disaster;
screening the initial historical case set according to the spatial correlation degree of each historical case and the current earthquake disaster to obtain a final historical case subset participating in disaster evaluation;
determining the weight of each historical case in the final historical case sample set participating in disaster evaluation;
obtaining a correction coefficient corresponding to each historical case in the final historical case subset; the correction coefficient comprises a disaster-resistant capability correction coefficient and a carrier quantity correction coefficient;
and evaluating the number of casualty population of the current earthquake disaster according to the weight of each historical case participating in disaster evaluation in the final historical case subset and the corresponding correction coefficient based on the number of casualty population of each historical case in the final historical case subset.
Optionally, the acquiring the earthquake history case base further includes:
acquiring earthquake disaster indexes; the disaster indicators include seismic magnitude, seismic source depth, and time of occurrence.
Optionally, the similarity evaluation model based on manhattan distance determines the similarity between the current seismic disaster and each historical case, and specifically includes:
using formula Dij＝|xij-xi0Determining absolute difference of each disaster index corresponding to the current earthquake disaster and each historical case; wherein D isijIs the absolute difference, x, of the ith disaster index corresponding to the current earthquake disaster and the jth historical caseijIs the ith disaster indicator, x, of the jth historical casei0The ith disaster index of the current earthquake disaster is obtained;
using formulas
Normalizing the absolute difference of each disaster index corresponding to the current earthquake disaster and each historical case to obtain a normalized value of the absolute difference of each disaster index corresponding to the current earthquake disaster and each historical case; wherein S isijIs the normalized value of the absolute difference of the ith disaster index corresponding to the current earthquake disaster and the jth historical case, max (D)ij) The maximum value of the absolute difference of the ith disaster index corresponding to the current earthquake disaster in all historical cases is min (D)ij) The minimum value of the absolute difference of the ith disaster index corresponding to the current earthquake disaster in all historical cases is obtained;
based on the Manhattan distance principle, the formula is utilized
Determining the similarity between the current earthquake disaster and each historical case; wherein CSjThe similarity between the current earthquake disaster and the jth historical case is shown, and n is the number of disaster indexes.
Optionally, the screening of the historical cases in the seismic historical case library according to the similarity between the current seismic disaster and each historical case to obtain an initial historical case set participating in disaster evaluation specifically includes:
sequencing the similarity of the current earthquake disaster and each historical case from big to small to generate a similarity sequencing sequence;
and screening the historical cases corresponding to the first m elements of the similarity ranking sequence to obtain an initial historical case set participating in disaster evaluation.
Optionally, the obtaining of the spatial correlation degree between each historical case in the initial historical case set and the current seismic disaster specifically includes:
acquiring fault distance of a current earthquake disaster place;
acquiring the fault distance of each historical case place in the initial historical case set;
using formulas
Determining the spatial correlation degree of each historical case in the initial historical case set and the current earthquake disaster; wherein D isjThe spatial correlation degree between the jth historical case in the initial historical case set and the current earthquake disaster, djIs the fault distance of the jth history case place in the initial history case set, d*And m is the number of history cases in the initial history case set.
Optionally, the determining the weight of each historical case participating in disaster evaluation in the final historical case example set specifically includes:
using the formula Wj＝(Hj+Dj) Determining the weight of each history case in the final history case example set participating in disaster evaluation, wherein WjWeights for the j-th historical case in the final set of historical case instances to participate in disaster assessment, DjThe spatial correlation degree between the jth historical case and the current earthquake disaster in the final historical case sample set, HjThe weighting coefficients for the jth history case in the initial history case set,
CSjthe similarity between the current earthquake disaster and the jth historical case in the initial historical case set is shown, and m is the number of the historical cases in the initial historical case set.
Optionally, the evaluating the number of casualties of the current earthquake disaster based on the number of casualties of each historical case in the final historical case subset according to the weight of each historical case in the final historical case subset participating in disaster evaluation and the corresponding correction coefficient specifically includes:
using formulas
Evaluating the number of casualty population of the current earthquake disaster; wherein, P0For the evaluation result of the number of casualty population in the current earthquake disaster, ajCorrection of coefficients for disaster resistance, bjFor correction of the number of carriers, WjWeight for jth historical case participating in disaster evaluation, PjThe number of earthquake disaster casualty population of the jth historical case is shown, and k is the number of the historical cases in the final historical case subset.
The invention also provides a earthquake casualty mouth evaluation system, which comprises:
the earthquake history case base acquisition module is used for acquiring an earthquake history case base; the earthquake history case base comprises a plurality of history cases;
the similarity determination module is used for determining the similarity between the current earthquake disaster and each historical case based on a similarity evaluation model of the Manhattan distance;
the first screening module is used for screening the historical cases in the seismic historical case library according to the similarity between the current seismic disaster and each historical case to obtain an initial historical case set participating in disaster evaluation;
the spatial correlation degree acquisition module is used for acquiring the spatial correlation degree of each historical case in the initial historical case set and the current earthquake disaster;
the second screening module is used for screening the initial historical case set according to the spatial correlation degree of each historical case and the current earthquake disaster to obtain a final historical case subset participating in disaster evaluation;
the weight determining module is used for determining the weight of each historical case in the final historical case example set participating in disaster evaluation;
a correction coefficient obtaining module, configured to obtain a correction coefficient corresponding to each historical case in the final historical case subset; the correction coefficient comprises a disaster-resistant capability correction coefficient and a carrier quantity correction coefficient;
and the evaluation module is used for evaluating the number of casualty population of the current earthquake disaster according to the weight of each historical case participating in disaster evaluation in the final historical case sample set and the corresponding correction coefficient based on the number of casualty population of each historical case in the final historical case subset.
Optionally, the spatial correlation degree obtaining module specifically includes:
the current earthquake disaster happening place fault distance acquiring unit is used for acquiring the fault distance of the current earthquake disaster happening place;
the fault distance acquisition unit of the historical case place is used for acquiring the fault distance of each historical case place in the initial historical case set;
a spatial correlation degree determination unit for using a formula
Determining the spatial correlation degree of each historical case in the initial historical case set and the current earthquake disaster; wherein the content of the first and second substances,Djthe spatial correlation degree between the jth historical case in the initial historical case set and the current earthquake disaster, djIs the fault distance of the jth history case place in the initial history case set, d*And m is the number of history cases in the initial history case set.
Optionally, the weight determination module utilizes formula Wj＝(Hj+Dj) Determining the weight of each history case in the final history case example set participating in disaster evaluation, wherein WjWeights for the j-th historical case in the final set of historical case instances to participate in disaster assessment, DjThe spatial correlation degree between the jth historical case and the current earthquake disaster in the final historical case sample set, HjThe weighting coefficients for the jth history case in the initial history case set,
CSjthe similarity between the current earthquake disaster and the jth historical case in the initial historical case set is shown, and m is the number of the historical cases in the initial historical case set.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the population casualties caused by the earthquake are related to factors of the earthquake such as the magnitude of the earthquake, the depth of an earthquake source and the like, and have a certain degree of relevance to the geographic environment, the social environment and the like of an area where the earthquake occurs. The earthquake casualty population evaluation method and system provided by the invention fully consider the influence of geographic environment factors on disaster evaluation, and add the factor of spatial correlation degree during evaluation, so that the obtained disaster evaluation result is more accurate.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a schematic flow chart of the earthquake casualty population evaluation method of the present invention;
FIG. 2 is a schematic diagram of the earthquake casualty population evaluation system according to the present invention;
FIG. 3 is a schematic flow chart of an embodiment of the present invention;
fig. 4 is a diagram of the evaluation accuracy of the SRSHC model for different numbers of evaluation cases in this embodiment;
fig. 5 is a comparison diagram of the evaluation accuracy of the SRSHC model and the SHC model for different numbers of evaluation cases in this embodiment.
Detailed Description
The technical solutions in the embodiments of the present invention will be 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 of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
FIG. 1 is a schematic flow chart of the earthquake casualty population evaluation method of the invention. As shown in FIG. 1, the earthquake casualty population evaluation method of the invention comprises the following steps:
step 100: and acquiring an earthquake history case base. The seismic history case base comprises a plurality of history cases. The data used by the invention mainly comes from the national earthquake science data sharing center (http:// data. earth square. cn/gcywfl/index. html), and specifically comprises 101 pieces of earthquake information such as the origin time, the epicenter longitude and latitude, the magnitude of the earthquake and the like of more than 4.0 grade of China continent in 2013 of the formula II. Among them, there are 50 cases of earthquake in which the number of injured persons and the number of dead persons are recorded. In the step, earthquake disaster statistical data with more than 4.0 levels and casualties is selected as an earthquake history case library.
Step 200: and determining the similarity between the current earthquake disaster and each historical case based on a similarity evaluation model of the Manhattan distance. Before similarity calculation, disaster indexes of the earthquake need to be determined, and the disaster indexes are used for judging the similarity between the current earthquake and historical cases so as to judge case similarity on the basis. The disaster index is selected according to the currently available earthquake disaster data, and the disaster index with high correlation with the disaster index needing to be evaluated is selected. A plurality of researches show that factors such as magnitude of earthquake, earthquake time, intensity in earthquake, depth of earthquake focus and the like all have certain influence on the number of casualties caused by the earthquake. Therefore, according to the information of the currently acquired data, the research selects earthquake magnitude, earthquake focus depth and earthquake occurrence time as disaster indexes, and carries out similarity judgment with the historical case, so as to carry out disaster evaluation aiming at the earthquake casualties.
And judging the similarity degree of the current earthquake disaster and the historical case based on the disaster index, and constructing a case similarity evaluation model, so that the similar historical case is quickly searched in an earthquake historical case library, and the disaster is preliminarily evaluated. The method is based on the minimum distance principle, adopts a historical case similarity evaluation model based on the Manhattan distance, namely under the condition of giving disaster indexes of the current earthquake disaster, calculates the Manhattan distance from each disaster index to each historical case in an earthquake historical case library, and screens out the historical similar cases according to distance sorting. The method comprises the following specific steps:
calculating the absolute difference of each disaster index corresponding to the current earthquake disaster and each historical case, wherein the formula is as follows:
Dij＝|xij-xi0|；
wherein D isijIs the absolute difference, x, of the ith disaster index corresponding to the current earthquake disaster and the jth historical caseijIs the ith disaster indicator, x, of the jth historical casei0The method is the ith disaster index of the current earthquake disaster.
In order to eliminate the influence of the unit dimension of the data, the calculated absolute difference data needs to be normalized and simultaneously subjected to inverse conversion, namely normalization. The formula is as follows:
wherein S isijThe method comprises the steps that a normalized value of the absolute difference of the ith disaster index corresponding to the current earthquake disaster and the jth historical case is obtained, and the larger the value is, the higher the similarity of the historical case and the current disaster in the disaster index is; max (D)ij) The maximum value of the absolute difference of the ith disaster index corresponding to the current earthquake disaster in all the historical cases, namely the maximum value of the absolute difference in all the historical cases participating in calculation, min (D)ij) And the minimum value of the absolute difference of the ith disaster index corresponding to the current earthquake disaster in all the historical cases, namely the minimum value of the absolute difference in all the historical cases participating in calculation.
According to the Manhattan distance principle, all disaster indexes S of the same historical caseiSolving to obtain the similarity with the current disaster, wherein the formula is as follows:
wherein CSjThe similarity between the current earthquake disaster and the jth historical case is shown, and n is the number of disaster indexes.
Step 300: and screening the historical cases in the seismic historical case library according to the similarity between the current seismic disaster and each historical case to obtain an initial historical case set participating in disaster evaluation. Specifically, a plurality of history cases with the maximum similarity are screened according to a set threshold. For example, the similarity between the current earthquake disaster and each historical case is sorted according to the sequence from big to small to generate a similarity sorting sequence; and then screening historical cases corresponding to the first m elements of the similarity ranking sequence to obtain an initial historical case set participating in disaster evaluation, wherein m is a set threshold value. The threshold value can be set according to actual requirements.
After the initial set of historical cases is determined, the weight coefficient for each historical case can be further calculated, and the formula is as follows:
wherein HjIs the weight coefficient, CS, of the jth history case in the initial history case setjThe similarity between the current earthquake disaster and the jth historical case in the initial historical case set is shown, and m is the number of the historical cases in the initial historical case set.
Step 400: and acquiring the spatial correlation degree of each historical case in the initial historical case set and the current earthquake disaster. The method reflects the spatial correlation degree of the historical case and the current disaster according to the difference between the fault distance of the current earthquake disaster occurrence place and the fault distance of the historical case disaster occurrence place. The method comprises the following specific steps:
acquiring fault distance d of current earthquake disaster place*。
And acquiring the fault distance of each historical case occurrence place in the initial historical case set. The formula is as follows:
wherein d isjThe fault distance of the jth history case occurrence place in the initial history case set, (x, y) the space coordinate of the location in the jth history case occurrence earthquake, (x)j,yj) The space coordinate of the fault position closest to the epicenter position in the jth historical case occurrence area.
And determining the spatial correlation degree of each historical case in the initial historical case set and the current earthquake disaster. The formula is as follows:
wherein D isjThe spatial correlation degree between the jth historical case in the initial historical case set and the current earthquake disaster, djIs the fault distance of the jth history case place in the initial history case set, d*And m is the number of history cases in the initial history case set.
Step 500: and screening the initial historical case set according to the spatial correlation degree of each historical case and the current earthquake disaster to obtain a final historical case subset participating in disaster evaluation. According to the size of the spatial correlation degree, determining the historical cases with the maximum spatial correlation degree as the historical cases participating in disaster evaluation, and obtaining a final historical case subset participating in disaster evaluation. The screening number is set according to actual requirements, and tests prove that the final evaluation effect is better when the number is 3 or 4.
Step 600: and determining the weight of each historical case participating in disaster evaluation in the final historical case example set. Specifically, using the formula Wj＝(Hj+Dj) And/2, determining the weight of each historical case in the final historical case example set participating in disaster evaluation. Wherein, WjWeights for the j-th historical case in the final set of historical case instances to participate in disaster assessment, DjThe spatial correlation degree between the jth historical case and the current earthquake disaster in the final historical case sample set, HjIs the weight coefficient of the jth history case in the initial history case set.
Step 700: and acquiring a correction coefficient corresponding to each historical case in the final historical case subset. The correction coefficient of the invention comprises a disaster-resistant capability correction coefficient and a carrier quantity correction coefficient.
The natural disaster anti-disaster and relief capability is mainly characterized in that the social and economic development level is synthesized on the investment and distribution of economic, technical and living material data, and the total GDP amount can reflect the social and economic development level of a region to a certain extent. However, the socio-economic development levels in different areas are different, and thus, the number of casualties caused by earthquake disasters is influenced to a certain extent. Therefore, the total GDP amount of the historical disaster occurrence area is compared with the total GDP amount of the current disaster occurrence area, so that the result difference caused by different socio-economic development levels of different areas is corrected. The formula is as follows:
in the formula: a isjA correction coefficient of disaster resistance capability of the jth historical case occurrence area relative to the current disaster occurrence area, GDPjThe total amount of GDP for the current year of the jth historical case occurrence area, GDP0The total amount of GDP in the current year of the current earthquake disaster area.
The disaster system is composed of a pregnant disaster environment, a disaster causing factor, a disaster bearing body and a disaster situation. The same disaster-causing factors occur in different disaster-bearing body exposure areas, and the influence on the overall disaster level is obvious. The disaster condition factor evaluated by the invention is the number of earthquake casualties, so the total population of the disaster occurrence area is selected as a disaster bearing body to carry out coefficient correction. And determining the correction coefficient of the number of disaster-bearing bodies by comparing the number of the disaster-bearing bodies in the area where the current disaster occurs with the data of the disaster-bearing bodies in the disaster-occurring area corresponding to the selected historical case. The calculation formula is as follows:
in the formula: bjPopulation exposure correction factor Db for the current earthquake disaster and the jth history case0For the number of people exposed in the current disaster area, DbjThe number of population exposures in the area where the annual disaster occurred corresponds to the jth history case.
Step 800: and evaluating the number of casualty population of the current earthquake disaster based on the number of casualty population of each historical case in the final historical case subset and according to the weight of each historical case in the final historical case sample set participating in disaster evaluation and the corresponding correction coefficient. And finally, carrying out weighted summation on the corrected historical similar cases to obtain a disaster evaluation result. In summary, the calculation formula of the earthquake casualty population evaluation model (SRSHC model) based on the historical similar case space deduction is as follows:
in the formula, P0For the evaluation result of the number of casualty population in the current earthquake disaster, ajCorrection of coefficients for disaster resistance, bjFor correction of the number of carriers, WjWeight for jth historical case participating in disaster evaluation, PjThe number of earthquake disaster casualty population of the jth historical case is shown, and k is the number of the historical cases in the final historical case subset.
Fig. 2 is a schematic structural diagram of the earthquake casualty population evaluation system of the invention. As shown in fig. 2, the earthquake casualty population evaluation system of the invention comprises the following structures:
an earthquake history case base obtaining module 201, configured to obtain an earthquake history case base; the seismic history case base comprises a plurality of history cases.
The similarity determination module 202 is configured to determine similarity between the current seismic disaster and each historical case based on a similarity evaluation model of manhattan distance.
The first screening module 203 is configured to screen the historical cases in the seismic historical case library according to the similarity between the current seismic disaster and each historical case, so as to obtain an initial historical case set participating in disaster evaluation.
A spatial correlation degree obtaining module 204, configured to obtain a spatial correlation degree between each history case in the initial history case set and the current seismic disaster.
And the second screening module 205 is configured to screen the initial historical case set according to the spatial correlation degree between each historical case and the current earthquake disaster, so as to obtain a final historical case subset participating in disaster evaluation.
A weight determining module 206, configured to determine a weight of each historical case in the final historical case sample set participating in disaster evaluation.
A correction coefficient obtaining module 207, configured to obtain a correction coefficient corresponding to each history case in the final history case subset; the correction coefficient comprises a disaster-resistant capability correction coefficient and a carrier quantity correction coefficient.
And the evaluation module 208 is configured to evaluate the number of casualty population of the current earthquake disaster according to the weight of each historical case participating in disaster evaluation in the final historical case sample set and the corresponding correction coefficient based on the number of casualty population of each historical case in the final historical case subset.
As another embodiment, in the earthquake casualty population evaluation system of the present invention, the spatial correlation degree obtaining module 204 specifically includes:
and the current seismic disaster happening place fault distance acquiring unit is used for acquiring the fault distance of the current seismic disaster happening place.
And the fault distance acquisition unit of the historical case place is used for acquiring the fault distance of each historical case place in the initial historical case set.
A spatial correlation degree determination unit for using a formula
Determining the spatial correlation degree of each historical case in the initial historical case set and the current earthquake disaster; wherein D isjThe spatial correlation degree between the jth historical case in the initial historical case set and the current earthquake disaster, djIs the fault distance of the jth history case place in the initial history case set, d*And m is the number of history cases in the initial history case set.
As another example, the weight determination module 206 in the earthquake casualty population evaluation system of the present invention utilizes the formula Wj＝(Hj+Dj) Determining the weight of each history case in the final history case example set participating in disaster evaluation, wherein WjWeights for the j-th historical case in the final set of historical case instances to participate in disaster assessment, DjFor the final history caseCentralizing the spatial correlation degree, H, of the jth historical case with the current seismic disasterjThe weighting coefficients for the jth history case in the initial history case set,
CSjthe similarity between the current earthquake disaster and the jth historical case in the initial historical case set is shown, and m is the number of the historical cases in the initial historical case set.
An embodiment is provided below to further verify the evaluation results of the earthquake casualty population evaluation method and system shown in fig. 1 and 2.
FIG. 3 is a flow chart illustrating an embodiment of the present invention. As shown in fig. 3, in the present embodiment, the earthquake disaster statistical data with the earthquake magnitude of above 4.0 and casualties in 2013 in china is used as the historical case, and considering that most of the historical cases are distributed in western regions in china, three cases are selected, which are located in areas with frequent earthquake disasters, have more casualties and have larger disaster influence: the 2010 Qinghai Yushu earthquake event, the 2013 Sichuan Lushan earthquake event and the 2014 Yunnan Ludian earthquake event are used as verification cases, and the SRSHC model is subjected to precision testing. Wherein, the 2010 Qinghai Yushu earthquake event is that the highest earthquake magnitude of 49 minutes occurred in Yushu City of Tibetan autonomous State of Jade Tree of Qinghai province is a Ri 7.1-grade earthquake, the depth of the earthquake source is 14 kilometers, and the total number of casualties is 14425; the number of the total casualties of 11687 people is determined as that in 2013, 20 th in Aphan in 4 months and 8 am in 2013, the highest earthquake level occurring in Yaan city, Lushan county, Sichuan province is Ri 7.0 level earthquake, the depth of an earthquake source is 13 kilometers; the Yunnan Ludian earthquake event in 2014 is a 6.5-grade Ri earthquake which occurs in Ludian county in Showton city in Yunnan province within 8 months and 3 days, the depth of an earthquake source is 12 kilometers, and the total number of casualties is 3872. The evaluation results are shown in table 1.
TABLE 1 SRSHC model and SHC model earthquake casualty population optimal evaluation result
As can be seen from Table 1, the SRSHC model estimates that the number of casualties of the 2010 Qinghai Yushu earthquake event is 14725, the number of casualties is the same as the actual number of casualties, and the highest precision reaches 97.92%; the number of casualties of 2013 Sichuan Lushan earthquake events is 11830 estimated by the SRSHC model, the estimated number of casualties is in the same order of magnitude as the actual number of casualties, and the highest precision reaches 98.78%; for the evaluation of the number of earthquake casualties of the Yunnan Ludian earthquake event in 2014, the evaluation result of the SRSHC model is 3799 people, which is close to the actual number of casualties, and the highest precision reaches 98.11%. Compared with the SHC model, the SRSHC model has the advantages that the casualty evaluation results are in the same order of magnitude as the actual casualties, and the accuracy of the best evaluation results is over 95%. The SHC model has low evaluation precision on the three verification cases, and the precision fluctuation of the evaluation result is large; wherein, the best estimation result of the casualties has the estimation precision which is closest to the actual casualties of 71.76 percent, and the estimation precision which is greatly different from the actual casualties is only 32.98 percent. The number of pairs of historical cases participating in the evaluation is shown in table 2.
TABLE 2 SRSHC model and historical case of SHC model participating in evaluation (best evaluation results)
As can be seen from Table 2, in the evaluation of casualty population in the event of the Qinghai Yushu earthquake in 2010, the case of the earthquake history participating in the evaluation is the earthquake disaster statistical data in 2010 of 2000-year. When the number of the earthquake history cases participating in the evaluation is 3, the evaluation result of the SRSHC model is the best, and the accuracy reaches the highest (97.92%). The 3 earthquake history cases involved in the evaluation were respectively pu-erh earthquake in cloud nanning in 2007, Wen river earthquake in 2008, and Yun river aftershock in Sichuan in 2008. Compared with the SRSHC model, the number of evaluation history cases required by the SHC model to obtain the optimal evaluation result is more. When the number of the historical cases is 6, the evaluation result of the SHC model is optimal. The 6 earthquake history cases involved in the evaluation were respectively 2008 Qinghai Dachadan earthquake, 2003 Xinjiang Bachu earthquake, 2007 Yunan Er earthquake, 2003 Xinjiang Shosu earthquake, 2000 Yunan Yaoan earthquake, and 2008 Si Wen river earthquake.
Among them, the impact of the earthquake of Wenchuan in Sichuan in 2008 on the evaluation results was large. The main reason is that compared with the high similarity of other historical cases on the disaster indexes such as earthquake magnitude, earthquake onset time and the like, on the basis that the disaster indexes of Wenchuan earthquake have high similarity, the distance between the earthquake center position and the earthquake fault is closer to the earthquake condition of the Qinghai jade tree, and the spatial correlation degree is high. And the two earthquakes are caused by collision of Indian ocean plates and Asia Europe plates, and the causes are similar. The SHC model only considers the similarity of each disaster index, so that the historical cases (the christmas earthquake in sikawa in 2008) with large influence on the evaluation result have a low proportion in the evaluation, and the overall evaluation accuracy is not high. The SRSHC model comprehensively considers the similarity of disaster indexes and the spatial correlation degree of the historical cases, and increases the evaluation proportion of the historical cases of Wenchuan earthquake in Sichuan in 2008, thereby improving the evaluation precision.
In the casualty population evaluation of the Sichuan Lushan earthquake event in 2013, when the number of earthquake history cases participating in evaluation is 3, the evaluation result of the SRSHC model is the best, and the precision reaches the highest (98.78%). The 3 earthquake history cases involved in the evaluation were respectively the 2010 Qinghai Yushu earthquake, 2007 Yunan Ning Er earthquake, and 2012 Yunan Shoutong earthquake. When the number of the earthquake history cases participating in the evaluation is 2, the evaluation result of the SHC model is optimal. The 2 earthquake history cases participating in the evaluation were the 2010 Qinghai Yushu earthquake and the 2008 Qinghai Dachadan earthquake, respectively.
In 2010, the influence of the Qinghai Yushu earthquake on the evaluation result is large. The reason is that the historical case is very similar to the Sichuan Lushan earthquake in terms of factors such as magnitude, depth of earthquake source, earthquake-initiating time and the like, and the fault distance of the earthquake-center position is very close. Although the Qinghai jade tree earthquake is the historical case with the highest similarity in the two models, and the number of the historical cases participating in evaluation of the SHC model is less, after the SRSHC model is added with the spatial weight, the importance of the Qinghai jade tree earthquake case in the evaluation is more prominent, so that the evaluation precision of the model is greatly improved.
In the casualty population evaluation of the Yunnan Ludian earthquake event in 2014, when the number of earthquake history cases participating in evaluation is 4, the evaluation result of the SRSHC model is the best, and the precision reaches the highest (98.11%). The 4 earthquake history cases involved in the evaluation were respectively pu-er earthquake in cloud nanning in 2007, lushan earthquake in Sichuan in 2013, Ming-le earthquake in 2003 and Sichuan Qingchuan aftershock in 2008. When the number of the earthquake history cases participating in the evaluation is 6, the evaluation result of the SHC model is the best. The 6 earthquake history cases involved in the evaluation were respectively 2003 Yunan Dayao earthquake, 2012 Xinjiang Xinyuan earthquake, 2003 Xinjiang Yuepu earthquake, 2007 Yunan Ning pu earthquake, 2008 Tibet Dang Xiong earthquake, and 2013 Sichuan Lushan earthquake.
Among them, in 2013, the impact of Sichuan Lushan earthquake on the evaluation result is large, and in 2007, Pu' er earthquake in Yun-Nanning is inferior. The earthquake disaster time of Pu' er Ningning is short, and although the house is damaged, a large amount of casualties are not caused; the earthquake elimination level of the Lushan mountain in Sichuan is high, aftershocks occur frequently, and a large amount of casualties are caused by a disaster accumulation effect, so that the earthquake elimination level is more similar to the situation that the Ludian earthquake event has a long disaster suffering time, and multiple aftershocks occur frequently, and secondary disasters occur frequently. Therefore, the historical cases involved in the evaluation are consistent with the actual situation. In the SHC model, the similarity difference between the above-mentioned historical cases participating in the calculation is small, so that each case presents an undifferentiated state in the model calculation. Under the condition that the difference of each disaster index is small, the SRSHC model introduces the spatial correlation degree, so that the difference between the evaluated historical cases is obvious, the calculation weight of the historical cases which is more similar to the comprehensive condition of the current evaluation disaster is increased, and the evaluation precision is improved.
Through testing the casualty population number of the three historical earthquake cases, the SRSHC model is verified to have certain feasibility and applicability in the aspect of earthquake casualty population evaluation, and the requirement of rapid evaluation of the initial stage of the earthquake can be met to a certain extent. In addition, the population casualties caused by the earthquake are related to factors of the earthquake such as the magnitude and the depth of the earthquake, and also have a certain degree of relevance to the geographic environment, the social environment and the like of the area where the earthquake occurs. The SHC model only considers disaster self factors and social environment factors, and the SRSHC model provided by the research supplementarily considers the influence of geographic environment factors on disaster evaluation, so that the obtained disaster evaluation result is more accurate. Meanwhile, the spatial relationship between the historical case and the current disaster occurrence area is further considered, and the accuracy of disaster situation evaluation can be remarkably improved.
In the disaster evaluation process, the selection of the historical disaster cases participating in evaluation has a great influence on the result of the disaster evaluation. Therefore, the specific embodiment performs experimental tests on the verification case. Considering that the evaluation based on one historical disaster case has great contingency, the evaluation cases tested by the specific embodiment are 2 or more historical disaster cases. And (3) under different evaluation case numbers, respectively using an SRSHC model and an SHC model to evaluate the casualties, wherein the evaluation results are detailed in a table 3.
TABLE 3 SRSHC model and SHC model disaster assessment results for different number of evaluation cases
Fig. 4 is a graph of the evaluation accuracy of the SRSHC model for different numbers of evaluation cases in this embodiment, and as can be seen from fig. 4, when the number of evaluation cases is 2, the evaluation accuracy of the SRSHC model fluctuates greatly, the highest accuracy reaches 90.51%, and the lowest accuracy is less than 1%, when three verification cases are evaluated using the SRSHC model. The reason is that when the number of the evaluation cases is too small, the requirement on the similarity between the historical cases and the current disaster is higher, and not only surface conditions such as earthquake factors (magnitude, depth of an earthquake source and the like), geographic environment, social environment and the like need to be considered, but also information in the aspect of earthquake occurrence mechanism needs to be combined for judgment. When the similarity between the selected 2 historical cases and the current disaster is very high, the similarity is high on various indexes and is also certain related to the earthquake occurrence mechanism, and the SRSHC model has high evaluation precision if the case evaluation of Sichuan Lushan earthquake in 2013 is carried out; and if the 2 selected historical cases only satisfy the similarity of each surface condition, the evaluation result has a larger difference with the actual situation. Thus, there was greater chance of the SRSHC model evaluation based on 2 historical seismic cases. When the number of the evaluated cases is more than 2, the evaluation accuracy of the SRSHC model is higher, and most of the evaluation accuracy of the model is more than 80%; with the increase of the number of cases, the accuracy of the SRSHC model is slightly reduced, but the overall model is stable.
For the three verification cases, the evaluation accuracy of the SRSHC model and the SHC model is consistent in the overall trend along with the change of the number of the evaluation cases, but the verification cases are slightly different. Fig. 5 is a comparison diagram of the evaluation accuracy of the SRSHC model and the SHC model for different evaluation cases in this specific embodiment, where (a) is a comparison diagram corresponding to a 2010 Qinghai Yushu earthquake, (b) is a comparison diagram corresponding to a 2013 Sichuan Lushan earthquake, and (c) is a comparison diagram corresponding to a 2014 Yunnan Ludian earthquake. As shown in part (a) of fig. 5, for the 2010 Qinghai Yushu earthquake event, when the number of reference cases is more than 2, the accuracy of the SRSHC model is 73.8% -97.92%; with the increase of the number of the evaluation cases, the model precision shows a slow descending trend, and the evaluation precision is optimal when 3 historical cases are selected for evaluation. The SHC model has the best evaluation precision when the number of the evaluation cases is 6, and the precision slightly decreases as the number of the cases increases, and the trend is consistent with that of the SRSHC model. As shown in part (b) of fig. 5, in 2013, when the number of evaluation cases is 2 or more, the accuracy of the SRSHC model is 86.45% -98.78%; with the increase of the number of the evaluation cases, the accuracy of the model slightly fluctuates, but the overall trend is stable, and the evaluation accuracy is optimal when 3 historical cases are selected for evaluation. And as the number of the evaluation cases of the SHC model increases, the model precision shows a descending trend, and the best model precision is achieved when the number of the evaluation cases is 2. As shown in part (c) of fig. 5, in the evaluation of Yunnan Ludian seismic events in 2014, the overall trends of the SRSHC model and the SHC model are consistent, the SRSHC model achieves the best evaluation accuracy when the number of evaluation cases is 4, the SHC model achieves the best evaluation accuracy when the number of evaluation cases is 6, and the SRSHC model shows a trend of steadily decreasing with the increase of the number of cases. Wherein, when the number of the reference cases is more than 2, the evaluation accuracy of the SRSHC model is between 68.61% and 98.11%.
The three verification cases are tested, and the result shows that when the number of the reference cases is more than 2, the evaluation precision of the SRSHC model is higher; with the increase of the number of the evaluation cases, the evaluation precision of the model is slightly reduced, but the overall stability is stable; and when the number of the reference cases is 3-4, the evaluation precision of the SRSHC model is the best.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (10)
1. A method for earthquake casualty mouth assessment, comprising:
acquiring an earthquake history case library; the earthquake history case base comprises a plurality of history cases;
determining the similarity between the current earthquake disaster and each historical case based on a similarity evaluation model of the Manhattan distance;
screening the historical cases in the seismic historical case library according to the similarity between the current seismic disaster and each historical case to obtain an initial historical case set participating in disaster evaluation;
acquiring the spatial correlation degree of each historical case in the initial historical case set and the current earthquake disaster;
screening the initial historical case set according to the spatial correlation degree of each historical case and the current earthquake disaster to obtain a final historical case subset participating in disaster evaluation;
determining the weight of each historical case in the final historical case sample set participating in disaster evaluation;
obtaining a correction coefficient corresponding to each historical case in the final historical case subset; the correction coefficient comprises a disaster-resistant capability correction coefficient and a carrier quantity correction coefficient;
and evaluating the number of casualty population of the current earthquake disaster according to the weight of each historical case participating in disaster evaluation in the final historical case subset and the corresponding correction coefficient based on the number of casualty population of each historical case in the final historical case subset.
2. The earthquake casualty population evaluation method as recited in claim 1, wherein the step of obtaining the earthquake history case base further comprises the following steps:
acquiring earthquake disaster indexes; the disaster indicators include seismic magnitude, seismic source depth, and time of occurrence.
3. The earthquake casualty population evaluation method as recited in claim 2, wherein the similarity evaluation model based on manhattan distance determines the similarity between the current earthquake disaster and each historical case, and specifically comprises:
using formula Dij＝|xij-xi0Determining absolute difference of each disaster index corresponding to the current earthquake disaster and each historical case; wherein D isijIs the absolute difference, x, of the ith disaster index corresponding to the current earthquake disaster and the jth historical caseijIs the ith disaster indicator, x, of the jth historical casei0The ith disaster index of the current earthquake disaster is obtained;
using formulas
Normalizing the absolute difference of each disaster index corresponding to the current earthquake disaster and each historical case to obtain a normalized value of the absolute difference of each disaster index corresponding to the current earthquake disaster and each historical case; wherein S isijIs the normalized value of the absolute difference of the ith disaster index corresponding to the current earthquake disaster and the jth historical case, max (D)ij) The maximum value of the absolute difference of the ith disaster index corresponding to the current earthquake disaster in all historical cases is min (D)ij) The minimum value of the absolute difference of the ith disaster index corresponding to the current earthquake disaster in all historical cases
Based on the Manhattan distance principle, the formula is utilized
Determining the similarity between the current earthquake disaster and each historical case; wherein CSjThe similarity between the current earthquake disaster and the jth historical case is shown, and n is the number of disaster indexes.
4. The earthquake casualty population evaluation method as recited in claim 1, wherein the step of screening the historical cases in the earthquake historical case library according to the similarity between the current earthquake disaster and each historical case to obtain an initial historical case set participating in disaster evaluation comprises the steps of:
sequencing the similarity of the current earthquake disaster and each historical case from big to small to generate a similarity sequencing sequence;
and screening the historical cases corresponding to the first m elements of the similarity ranking sequence to obtain an initial historical case set participating in disaster evaluation.
5. The earthquake casualty population evaluation method as recited in claim 1, wherein the obtaining of the spatial correlation degree between each historical case in the initial historical case set and the current earthquake disaster specifically comprises:
acquiring fault distance of a current earthquake disaster place;
acquiring the fault distance of each historical case place in the initial historical case set;
using formulas
Determining the spatial correlation degree of each historical case in the initial historical case set and the current earthquake disaster; wherein D isjThe spatial correlation degree between the jth historical case in the initial historical case set and the current earthquake disaster, djIs the fault distance of the jth history case place in the initial history case set, d*And m is the number of history cases in the initial history case set.
6. The method for evaluating earthquake casualty population according to claim 1, wherein the determining the weight of each historical case in the final historical case sample set participating in disaster evaluation specifically comprises:
using the formula Wj＝(Hj+Dj) Determining the weight of each history case in the final history case example set participating in disaster evaluation, wherein WjWeights for the j-th historical case in the final set of historical case instances to participate in disaster assessment, DjThe spatial correlation degree between the jth historical case and the current earthquake disaster in the final historical case sample set, HjThe weighting coefficients for the jth history case in the initial history case set,
CSjthe similarity between the current earthquake disaster and the jth historical case in the initial historical case set is shown, and m is the number of the historical cases in the initial historical case set.
7. The earthquake casualty population evaluation method as recited in claim 1, wherein the evaluation of the number of current earthquake disaster casualty population is performed based on the number of casualty population of each historical case in the final historical case subset and according to the weight and the corresponding correction coefficient of each historical case in the final historical case sample set participating in disaster evaluation, and specifically comprises:
using formulas
Evaluating the number of casualty population of the current earthquake disaster; wherein, P0For the evaluation result of the number of casualty population in the current earthquake disaster, ajCorrection of coefficients for disaster resistance, bjFor correction of the number of carriers, WjWeight for jth historical case participating in disaster evaluation, PjThe number of earthquake disaster casualty population of the jth historical case is shown, and k is the number of the historical cases in the final historical case subset.
8. An earthquake casualty mouth assessment system, comprising:
the earthquake history case base acquisition module is used for acquiring an earthquake history case base; the earthquake history case base comprises a plurality of history cases;
the similarity determination module is used for determining the similarity between the current earthquake disaster and each historical case based on a similarity evaluation model of the Manhattan distance;
the first screening module is used for screening the historical cases in the seismic historical case library according to the similarity between the current seismic disaster and each historical case to obtain an initial historical case set participating in disaster evaluation;
the spatial correlation degree acquisition module is used for acquiring the spatial correlation degree of each historical case in the initial historical case set and the current earthquake disaster;
the second screening module is used for screening the initial historical case set according to the spatial correlation degree of each historical case and the current earthquake disaster to obtain a final historical case subset participating in disaster evaluation;
the weight determining module is used for determining the weight of each historical case in the final historical case example set participating in disaster evaluation;
a correction coefficient obtaining module, configured to obtain a correction coefficient corresponding to each historical case in the final historical case subset; the correction coefficient comprises a disaster-resistant capability correction coefficient and a carrier quantity correction coefficient;
and the evaluation module is used for evaluating the number of casualty population of the current earthquake disaster according to the weight of each historical case participating in disaster evaluation in the final historical case sample set and the corresponding correction coefficient based on the number of casualty population of each historical case in the final historical case subset.
9. The earthquake casualty population evaluation system of claim 8, wherein the spatial correlation degree acquisition module specifically comprises:
the current earthquake disaster happening place fault distance acquiring unit is used for acquiring the fault distance of the current earthquake disaster happening place;
the fault distance acquisition unit of the historical case place is used for acquiring the fault distance of each historical case place in the initial historical case set;
a spatial correlation degree determination unit for using a formula
Determining the spatial correlation degree of each historical case in the initial historical case set and the current earthquake disaster; wherein D isjThe spatial correlation degree between the jth historical case in the initial historical case set and the current earthquake disaster, djIs the fault distance of the jth history case place in the initial history case set, d*And m is the number of history cases in the initial history case set.
10. The earthquake casualty population evaluation system of claim 8, wherein the weight determination module utilizes the formula Wj＝(Hj+Dj) Determining the weight of each history case in the final history case example set participating in disaster evaluation, wherein WjParticipation in disaster for jth history case in final history case setWeight of the situation assessment, DjThe spatial correlation degree between the jth historical case and the current earthquake disaster in the final historical case sample set, HjThe weighting coefficients for the jth history case in the initial history case set,
CSjthe similarity between the current earthquake disaster and the jth historical case in the initial historical case set is shown, and m is the number of the historical cases in the initial historical case set.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010086186.2A CN111401682B (en) | 2020-02-11 | 2020-02-11 | Earthquake casualty mouth assessment method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010086186.2A CN111401682B (en) | 2020-02-11 | 2020-02-11 | Earthquake casualty mouth assessment method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111401682A true CN111401682A (en) | 2020-07-10 |
| CN111401682B CN111401682B (en) | 2022-05-31 |
Family
ID=71428448
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010086186.2A Active CN111401682B (en) | 2020-02-11 | 2020-02-11 | Earthquake casualty mouth assessment method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111401682B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111985847A (en) * | 2020-09-02 | 2020-11-24 | 四川省地震局减灾救助研究所 | Earthquake disaster risk assessment and countermeasure analysis system |
| CN115565062A (en) * | 2022-09-05 | 2023-01-03 | 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 |
| CN117077897A (en) * | 2023-09-21 | 2023-11-17 | 四川省华地建设工程有限责任公司 | Method and system for deducing damage of earthquake disaster |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2008039446A (en) * | 2006-08-02 | 2008-02-21 | Kajima Corp | Earthquake damage evaluation program |
| CN106780050A (en) * | 2016-12-12 | 2017-05-31 | 国信优易数据有限公司 | Disaster degree appraisal procedure, system and electronic equipment |
| CN107193990A (en) * | 2017-05-31 | 2017-09-22 | 民政部国家减灾中心 | The dead population estimation method and apparatus of earthquake disaster based on history case information |
-
2020
- 2020-02-11 CN CN202010086186.2A patent/CN111401682B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2008039446A (en) * | 2006-08-02 | 2008-02-21 | Kajima Corp | Earthquake damage evaluation program |
| CN106780050A (en) * | 2016-12-12 | 2017-05-31 | 国信优易数据有限公司 | Disaster degree appraisal procedure, system and electronic equipment |
| CN107193990A (en) * | 2017-05-31 | 2017-09-22 | 民政部国家减灾中心 | The dead population estimation method and apparatus of earthquake disaster based on history case information |
Non-Patent Citations (2)
| Title |
|---|
| 夏兴生: "基于历史案例的自然灾害灾情评估方法研究", 《灾害学》 * |
| 曾婷婷: "基于历史相似案例空间推演的地震伤亡人口评估方法研究", 《地球信息科学学报》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111985847A (en) * | 2020-09-02 | 2020-11-24 | 四川省地震局减灾救助研究所 | Earthquake disaster risk assessment and countermeasure analysis system |
| CN115565062A (en) * | 2022-09-05 | 2023-01-03 | 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 |
| 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 |
| CN117077897A (en) * | 2023-09-21 | 2023-11-17 | 四川省华地建设工程有限责任公司 | Method and system for deducing damage of earthquake disaster |
| CN117077897B (en) * | 2023-09-21 | 2024-03-19 | 四川省华地建设工程有限责任公司 | Method and system for deducing damage of earthquake disaster |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111401682B (en) | 2022-05-31 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111401682B (en) | Earthquake casualty mouth assessment method and system | |
| Sokolov et al. | Influence of ground-motion correlation on probabilistic assessments of seismic hazard and loss: sensitivity analysis | |
| Du et al. | Conceptual model on regional natural disaster risk assessment | |
| US20220164908A1 (en) | Method And System For Earthquake Disaster Level Assessment | |
| CN108960481A (en) | Earthquake disaster injures and deaths population estimation method and computer-readable medium | |
| CN113191647A (en) | Urban toughness assessment method for emergency management | |
| CN112836937A (en) | Flood disaster loss evaluation method based on entropy weight and BP neural network technology | |
| CN112990379A (en) | Clustering-based method and equipment for rapidly analyzing wind load vulnerability of power transmission tower | |
| xia Wang et al. | ANN model for the estimation of life casualties in earthquake engineering | |
| Li et al. | A rapid-response earthquake fatality estimation model for mainland China | |
| CN113221426A (en) | Nuclear power plant region anti-seismic safety assessment method under seismic sequence effect | |
| Zhou et al. | Issues in dependency modeling in multi-unit seismic PRA | |
| CN112990562A (en) | Forest fire prevention grade prediction algorithm based on community characteristics | |
| Zhang et al. | The quick assessment model of casualties for Asia based on the vulnerability of earthquake | |
| CN114386699B (en) | Method and system for predicting operation condition of base station after earthquake | |
| CN118051717A (en) | Method, device and equipment for evaluating influence of earthquake disasters on population | |
| Fadjryani et al. | Analysis of poverty factors in Sulawesi by using path analysis | |
| Azotea et al. | Comparative radial analysis of Philippines’ local flood vulnerability index | |
| Prabaswara et al. | An analysis of social vulnerability clustering to natural disasters (case studies in all districts/cities in Indonesia in 2019) | |
| Agrawal et al. | Comparing priorities of Providers and Users with Respect to Disaster Management Strategies | |
| GPS et al. | Developing a Model for Estimating the Home Return of Evacuees Based on the 2011 Tohoku Earthquake | |
| Chin et al. | Nowcasting Earthquake Occurrence in Sabah | |
| ZHAO et al. | Dynamic method of assessing seismic casualty | |
| Iwasaki et al. | Evacuation and psychological distress: new evidence of reference-dependent utility and loss aversion | |
| Pratiwi et al. | SAR Modelling life expectancy in Sulawesi Island by using spatial autoregressive (SAR) |
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 |