A method for estimating population loss taking into account rescue capabilities

Abstract

The application discloses a population loss estimation method considering rescue capability, and relates to the technical field of natural disaster risk assessment and emergency rescue. The method comprises the following steps: obtaining population grid, submerged water depth, flow velocity, rising rate, alarm data, city GDP, traffic facilities and other data of a target area; quantifying a flood severity coefficient, a rescue capability coefficient, an alarm time correction coefficient and an understanding degree correction coefficient; wherein, the severity coefficient is determined based on a two-parameter mapping of the water depth, the flow velocity and the rising rate, the rescue capability coefficient is determined based on city economy and traffic data, and the understanding degree is determined based on early warning records and communication coverage; a logistic regression model is used to correct the risk population by using the above coefficients to obtain a population loss rate. The application quantifies rescue capability and crowd cognition through multi-dimensional objective data, realizes the full-chain correlation of disaster evolution, rescue window and escape effectiveness, and improves the precision and practicability of flood population loss estimation.

Description

Technical Field

[0001] This invention relates to the field of natural disaster risk assessment and emergency rescue technology, and in particular to a method for estimating population loss that takes into account rescue capabilities. Background Technology

[0002] Floods pose a serious threat to human life, and accurate estimation of population loss is the core basis for guiding the allocation of disaster prevention and mitigation resources and the deployment of emergency rescue. Its scientific logic should be based on a multi-factor coupling mechanism of "intensity of disaster-causing factors - vulnerability of disaster-bearing bodies - intervention of rescue capabilities".

[0003] Current mainstream flood loss estimation methods both domestically and internationally (including the USGS water depth-mortality vulnerability curve and the domestic water conservancy industry standard disaster loss curve method) quantify risk solely by fitting the statistical relationship between static flood water depth and flow velocity—two hazard-causing factors—and loss rates. However, existing methods have significant shortcomings in characterizing the key loss mitigation factor of "regional relief capacity": 1) Lack or ambiguity in rescue capability assessment: Traditional solutions do not take rescue capability as an independent assessment dimension, or only generalize it as "emergency response", lacking a standardized and quantifiable indicator system, and cannot reflect the actual intervention effect of regional rescue resource differences on losses.

[0004] 2) The disaster-causing factors are considered in a single way, using only static snapshot-type hydrological indicators, without incorporating dynamic processes such as flood rise rate and early warning transmission timeliness, and thus failing to depict the compression effect of the rescue and escape time window.

[0005] 3) The accuracy of risk population quantification is insufficient. It mostly uses the average population density of the whole area for rough calculation, without combining high-resolution grids to distinguish between permanent residents and migrant populations. At the same time, it lacks differentiated consideration of the escape ability of vulnerable groups such as the elderly and children.

[0006] The aforementioned shortcomings make it difficult for existing estimates to be adapted to cross-basin, large-scale flood disaster scenarios, and they cannot accurately reflect the actual mitigation effect of differences in rescue capabilities in different regions on population loss.

[0007] Therefore, there is an urgent need for a population loss estimation method that can quantify the intervention effect of rescue capabilities and combine dynamic disaster-causing factors with refined population distribution. Summary of the Invention

[0008] The purpose of this invention is to provide a population loss estimation method that takes into account rescue capabilities, aiming to address three core shortcomings of existing technologies: 1. To address the lack of standardized and quantifiable indicators for rescue capabilities: Construct a multi-dimensional rescue capability coefficient system that integrates urban economy, transportation, and historical disasters to achieve quantitative calculation of the intervention effect of rescue resources; 2. To address the limitations of static assessment of disaster-causing factors: By coupling dynamic parameters such as flood rise rate, warning period, and warning facility level, a quantitative framework for the entire chain of disaster evolution, warning window, and personnel escape is established; 3. To address the issue of crude risk population estimation: The risk population in the flooded area is calculated more finely based on high-resolution population raster, and is compatible with the structure correction of vulnerable groups to reduce estimation errors.

[0009] To achieve the above objectives, the present invention provides the following solution: A population loss estimation method that takes into account rescue capabilities includes: Acquire high-resolution population raster data, flood depth, flood velocity, flood rise rate, alarm transmission data, city GDP, city level, transportation infrastructure data, and historical disaster records for the target rescue area; Based on data from the target area, the city's flood severity coefficient, rescue capacity coefficient, alarm time correction coefficient, and comprehension level correction coefficient are quantified. A multi-factor coupled correction model is adopted, and the above coefficients are used to correct the at-risk population to obtain the total number of the lost population.

[0010] Furthermore, the flood severity coefficient, the quantification process includes: The severity of a flood is expressed as: In the formula, The severity of the flood; To submerge the water depth; For Fruder number; The flood rise rate reflects a correction for dynamic disaster risk; For flood flow velocity; It is the acceleration due to gravity; Based on flood severity and flood flow velocity The flood severity coefficient is obtained by matching it in a preset two-dimensional flood severity coefficient mapping table.

[0011] Furthermore, the two-dimensional flood severity coefficient mapping table maps flood severity according to disaster levels. Section and flood velocity Numerical construction for each interval.

[0012] Furthermore, the disaster level is determined based on the severity of the flood. The numerical division includes: like If the severity of the disaster is low; if If the disaster is of moderate to severe severity; if If so, the disaster is of high severity; if If so, the disaster is of extremely high severity.

[0013] Furthermore, the rescue capability coefficient includes: If a city's GDP is at or above the first GDP threshold, the city is a first-tier city, the city has an airport, and the most recent historical flood occurred within the first year or less, then the rescue capacity coefficient is 1.0. If a city's GDP is between the second and first GDP thresholds, it is a second-tier city, has an airport, and the most recent historical flood occurred between the first and second years, then the rescue capacity coefficient is 0.8. If a city's GDP is at or below the second GDP threshold, the city is a third-tier city, the city has more than one railway station, and the most recent historical flood occurred between the second and third years, then the rescue capacity coefficient is 0.6. If the recent historical flood recurrence period is between the third and fourth year, the rescue capacity coefficient is 0.5; If the recurrence period exceeds the fourth year, the rescue capability coefficient will be reduced to 0.4.

[0014] Furthermore, the quantification steps for the alarm time correction factor include: Theoretical alarm time for the target area Flood occurrence period Distance from riverbank and early warning facility level ; The alarm time correction coefficient of the target area is matched according to the preset alarm time correction coefficient determination table.

[0015] Furthermore, theoretical alarm time The time interval from the issuance of an evacuation warning to the arrival of floodwaters in the target area; Flood period The period from 06:00 to 18:00 is designated as daytime; and the period from 18:00 to 06:00 the following day is designated as nighttime. Riverbank Distance Based on a preset distance threshold, if the distance between the target area and the riverbank is greater than the distance threshold, it is determined that the target area is far from the river; otherwise, it is determined that the target area is close to the riverbank. Early warning facility level The division includes: If the target area has a working electronic alarm system, broadcast and SMS network dissemination system, and professional management personnel, then the early warning equipment level of the target area is determined to be high level; If the target area has basic early warning facilities, the early warning equipment level of the target area is determined to be medium level; If the target area has no fixed facilities and relies on manual alarm transmission, the early warning equipment level of the target area is determined to be low.

[0016] Furthermore, the alarm time correction coefficient determination table includes: If the theoretical alarm time for the target area The flood occurred within a time period that was greater than or equal to the first time threshold. For daytime, the warning facility level For a high level, the alarm time correction factor is 1.0; If the theoretical alarm time for the target area The time period during which the flood occurred is less than the first time threshold and greater than or equal to the second time threshold. During the daytime, the distance from the riverbank Due to its proximity to the riverbank, the early warning facility level is [not specified]. If the alarm level is medium, the alarm time correction factor is 0.8; If the theoretical alarm time for the target area The time period during which the flood occurred is less than the second time threshold and greater than or equal to the third time threshold. It was nighttime, and the riverbank was far from... Due to its proximity to the riverbank, the early warning facility level is [not specified]. If the alarm level is medium, the alarm time correction factor is 0.6; If the theoretical alarm time for the target area Less than the third time threshold, the period of flood occurrence It was nighttime, and the riverbank was far from... Due to its proximity to the riverbank, the early warning facility level is [not specified]. If the alarm level is low, the alarm time correction factor is 0.4.

[0017] Furthermore, the comprehension level correction coefficient, the quantification steps include: Acquire early warning information release records, communication facility coverage data, and basic social disaster prevention data for the target area; When the warning information release record has released a complete warning including the flood arrival time, inundation range and escape routes, and the communication facility coverage rate is greater than or equal to the preset coverage threshold, the level of understanding of the at-risk population in the target area is determined to be clear understanding, and the level of understanding correction coefficient is set to 1.0; if the warning content is complete but the communication facility coverage rate is lower than the coverage threshold, and there is only SMS push without community broadcast, it is determined to be general understanding, and the level of understanding correction coefficient is 0.8; secondary correction item for vulnerable groups: when the proportion of elderly and children in the area exceeds 30%, the coefficient is further reduced by 0.1.

[0018] When the warning information release record is missing, the warning content is incomplete, or the communication facility coverage is lower than the preset coverage threshold, the understanding level of the at-risk population in the target area is determined to be vague, and the understanding level correction coefficient is set to 0.6.

[0019] Furthermore, the population loss can be expressed as: In the formula, LR represents the population loss amount; LR represents the population loss rate. For high-risk population; This is a correction factor for alarm time. Adjustment coefficients based on comprehension level; This represents the flood severity coefficient. This refers to the rescue capability coefficient. High-resolution population density; This represents the dynamically submerged area.

[0020] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects: This invention discloses a population loss estimation method that takes into account rescue capabilities. The method constructs a multi-dimensional objective quantitative system based on city level, GDP, transportation facilities and historical disaster data, transforms rescue capabilities into calculable coefficients, clarifies the quantitative correlation between "resource input - response efficiency - loss mitigation", overcomes the defects of qualitative and subjective methods in traditional methods, and improves the matching degree between the estimation results and the actual rescue intervention effect.

[0021] By incorporating dynamic factors such as flood rise rate, warning time, and level of understanding into the model in conjunction with disaster-causing factors, the model achieves a full-chain correlation of "disaster evolution - rescue window - escape effectiveness," effectively overcoming the limitations of traditional static assessments and enabling dynamic responses to the spatiotemporal changes of large-scale floods.

[0022] Refined calculations based on high-resolution population raster data break through the traditional rough estimation model based on "regional average population density", significantly reduce quantification errors, and can accurately adapt to the assessment needs of different terrain and population distribution characteristics.

[0023] A flood severity coefficient determination logic based on dual-parameter interval mapping and an objective inference mechanism based on early warning release records and communication coverage were established to avoid subjective bias in weight allocation. At the same time, each core parameter can be flexibly calibrated according to the actual characteristics of the region, making it applicable to regions with different levels of economic development and disaster risk, and has promotional application value. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a flowchart illustrating the method of the present invention. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] The purpose of this invention is to provide a population loss estimation method that takes into account rescue capabilities, aiming to solve or improve at least one of the above-mentioned technical problems.

[0028] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0029] like Figure 1 As shown, the present invention provides a method for estimating population loss that takes into account rescue capabilities, including: Step 1: Obtain high-resolution population raster data, flood depth, flood velocity, flood rise rate, alarm transmission data, city GDP, city level, transportation infrastructure data, and historical disaster record data for the target rescue area; High-resolution population raster: Distinguish between resident population and migrant population layers, extract the raster percentages of elderly people over 60 years old and children under 12 years old, and use them as parameters for subsequent fine-tuning of population cognition and escape ability; Dynamic inundation area A: Matches the hourly inundation range of the hydrological model, overlays it with the population grid space, and extracts synchronous data of inundation duration, water depth, and flow velocity for each grid cell; Unified and standardized early warning data: Early warning records from multiple channels such as SMS, broadcast, and manual announcements are uniformly converted into two categories of tags: complete and incomplete. Communication coverage is uniformly converted into a value in the range of 0 to 1.

[0030] Step 2: Based on the data information of the target area, quantify the city's flood severity coefficient, rescue capacity coefficient, alarm time correction coefficient, and comprehension level correction coefficient, including: The severity of a flood is expressed as: In the formula, The severity of the flood; To submerge the water depth; The Froude number is used to couple the dual effects of submerged water depth and flow velocity on the difficulty of escape through dimensionless parameters. The flood rise rate reflects a correction for dynamic disaster risk; For flood flow velocity; It is the acceleration due to gravity; Based on flood severity and flood flow velocity The flood severity coefficient is obtained by matching it in a preset two-dimensional flood severity coefficient mapping table.

[0031] In this embodiment, based on the severity of the flood The numerical classification of disaster levels includes: like If the situation is as described above, the disaster is of low severity and people can be evacuated normally. like If the disaster is moderate to severe, children and the elderly will have difficulty walking and will require assisted evacuation. like If the disaster is severe, adults are easily swept away due to their instability, and the risk of drowning increases significantly. like If the disaster is extremely severe, people will be unable to stand on their own, and the mortality rate will be extremely high.

[0032] A two-dimensional flood severity coefficient mapping table, which maps flood severity to disaster levels. Section and flood velocity The numerical values ​​for each interval are constructed to determine the flood severity coefficient.

[0033] Table 1. Correspondence Table of Flood Failure Severity Coefficient α

[0034] As shown in Table 2, the rescue capacity coefficient is determined based on the target area's urban GDP, city level, transportation infrastructure data, and historical disaster record data, including: If a city's GDP is 1 trillion yuan or more, it is a first-tier city, has an airport, and experiences a flood event that occurs once every 20 years or less, then its rescue capability coefficient is 1.0. If a city's GDP is 500-1000 billion yuan, it is a second-tier city, has an airport, and experiences floods once every 20-50 years, then its rescue capacity coefficient is 0.8. If a city's GDP is 500 billion yuan or less, its city level is a third-tier city, it has more than one railway station, and the flood occurs once every 50-100 years, then the rescue capacity coefficient is 0.6. If the flood occurs once every 100-150 years or more, the rescue capability coefficient is 0.5.

[0035] If the recurrence interval exceeds 150 years, the rescue capability coefficient will be reduced to 0.4.

[0036] Second-tier cities without airports but with adequate density of emergency road networks and sufficient number of standardized shelters can have their rescue capability coefficient increased by 0.1. For regions that conduct ≥2 annual disaster prevention drills and have sufficient flood control supplies, the coefficient will be increased by an additional 0.05, up to a maximum of 1.0.

[0037] Table 2. Correspondence Coefficients for Rescue Capabilities

[0038] Alarm time correction factor, to obtain the theoretical alarm time for the target area. Flood occurrence period Distance from riverbank and early warning facility level ,include: Theoretical alarm time The time interval from the issuance of an evacuation warning to the arrival of floodwaters in the target area; Flood period To make it easier to detect floods, the period from 06:00 to 18:00 is designated as daytime; the period from 18:00 to 06:00 the next day is designated as nighttime, which makes it difficult to detect floods. Riverbank Distance Based on a preset distance threshold, if the distance between the target area and the riverbank is greater than the distance threshold, it is determined that the target area is far from the river; otherwise, it is determined that the target area is close to the riverbank. Early warning facility level The division includes: If the target area has a working electronic alarm system, broadcast and SMS network dissemination system, and professional management personnel, then the early warning equipment level of the target area is determined to be high level; If the target area has basic early warning facilities, such as village broadcasting devices or simple monitoring devices, then the early warning equipment level of the target area is determined to be medium level; If the target area has no fixed facilities and relies on manual alarm transmission, such as banging gongs, blowing whistles, or shouting, then the early warning equipment level of the target area is determined to be low.

[0039] The alarm time correction coefficient of the target area is matched according to the preset alarm time correction coefficient determination table.

[0040] Table 3 shows the alarm time correction coefficient determination table, based on the theoretical alarm time. Flood occurrence period Distance from riverbank and early warning facility level Configure the settings, including: If the theoretical alarm time for the target area Time period of flood occurrence For daytime, the warning facility level For a high level, the alarm time correction factor is 1.0; If the theoretical alarm time for the target area Time period of flood occurrence During the daytime, the distance from the riverbank Due to its proximity to the riverbank, the early warning facility level is [not specified]. If the alarm level is medium, the alarm time correction factor is 0.8; If the theoretical alarm time for the target area Time period of flood occurrence It was nighttime, and the riverbank was far from... Due to its proximity to the riverbank, the early warning facility level is [not specified]. If the alarm level is medium, the alarm time correction factor is 0.6; If the theoretical alarm time for the target area Time period of flood occurrence It was nighttime, and the riverbank was far from... Due to its proximity to the riverbank, the early warning facility level is [not specified]. If the alarm level is low, the alarm time correction factor is 0.4.

[0041] For scenarios with a theoretical alarm time ≥ 1.0h, any time period, high-level early warning facilities, and distance from the riverbank greater than the threshold (far from the river channel), the alarm time correction coefficient is 1.0; for other scenarios far from the river channel, the coefficient is uniformly increased by 0.2, with an upper limit not exceeding 1.0.

[0042] Table 3

[0043] The 1.0h, 0.5h, and 0.2h warning duration thresholds in this table are standard values ​​for plain watersheds; the thresholds for mountainous flood-prone watersheds can be uniformly lowered by 50%, while the thresholds for flood control and storage areas can be raised by 50%. Custom calibration based on watershed type is supported.

[0044] The comprehension level correction coefficient is used to characterize the impact of the disaster-affected group's clarity of understanding of the disaster on the final loss of life. Its value reflects the self-rescue preparedness of the population when a disaster occurs.

[0045] Acquire early warning information release records, communication facility coverage data, and basic social disaster prevention data for the target area; When the early warning information release record has released a complete early warning including the flood arrival time, inundation range and escape route, and the communication facility coverage is greater than or equal to the preset coverage threshold, the level of understanding of the at-risk population in the target area is determined to be clear understanding, and the level of understanding correction coefficient is set to 1.0; If the warning content is complete but the communication facility coverage is below the coverage threshold, or if only SMS is sent and no community broadcast is given, it is judged as a general understanding, and the understanding level correction coefficient is set to 0.8; for the secondary correction item for vulnerable groups: when the proportion of elderly and children in the area exceeds 30%, the coefficient is further reduced by 0.1.

[0046] When the warning information release record is missing, the warning content is incomplete, or the communication facility coverage is lower than the preset coverage threshold, the understanding level of the at-risk population in the target area is determined to be vague, and the understanding level correction coefficient is set to 0.6.

[0047] Step 3: Employ a multi-factor coupled correction model, using the aforementioned coefficients to correct the at-risk population, to obtain the total number of people at risk, including: The number of people lost is expressed as: In the formula, LR represents the number of people lost; LR represents the population loss rate. For high-risk population; This is a correction factor for alarm time. Adjustment coefficient for level of understanding; This represents the flood severity coefficient. This refers to the rescue capability coefficient. High-resolution population density; This represents the dynamically submerged area.

[0048] This method is accompanied by a model validation process: Case calibration: The threshold values ​​for each coefficient are corrected by using historical flood disaster mortality data from the region to reduce estimation bias; Sensitivity analysis: By univariately varying the flood severity coefficient, rescue capacity coefficient, and warning correction coefficient, and comparing the changes in the number of people lost, the dominant risk factors in the region can be identified. Error range output: Each estimation result is output synchronously with a floating error range of ±10%~±20% to support risk decision-making and fault tolerance judgment.

[0049] The core computational model of this invention is a multi-factor coupled product model, which is easy to implement in engineering and does not require a large number of historical samples. As a preferred optimized embodiment, logistic regression and random forest machine learning models can be used to replace manually set coefficients: flood severity, rescue capacity, warning timeliness, public understanding, population density, and inundation area are used as input features of the model, and the actual population loss in historical floods is used as labels to train the model. The weights of each factor are automatically fitted, which is suitable for watersheds with sufficient long-term disaster samples and forms a primary and secondary complement to the basic product model.

[0050] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0051] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the core ideas of the present invention. At the same time, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for estimating population loss considering rescue capabilities, characterized in that, include: Acquire high-resolution population raster data, flood depth, flood velocity, flood rise rate, alarm transmission data, city GDP, city level, transportation infrastructure data, and historical disaster records for the target rescue area; Based on data from the target area, the city's flood severity coefficient, rescue capacity coefficient, alarm time correction coefficient, and comprehension level correction coefficient are quantified. A multi-factor coupled correction model is adopted, and the above coefficients are used to correct the at-risk population to obtain the total number of the lost population.

2. The population loss estimation method considering rescue capabilities according to claim 1, characterized in that, The quantification process for the flood severity coefficient includes: The severity of a flood is expressed as: In the formula, The severity of the flood; To submerge the water depth; For Fruder number; The flood rise rate reflects a correction for dynamic disaster risk; For flood flow velocity; It is the acceleration due to gravity; Based on flood severity and flood flow velocity The flood severity coefficient is obtained by matching it in a preset two-dimensional flood severity coefficient mapping table.

3. The population loss estimation method considering rescue capabilities according to claim 2, characterized in that, The two-dimensional flood severity coefficient mapping table maps flood severity to disaster levels. Section and flood velocity Numerical construction for each interval.

4. The population loss estimation method considering rescue capabilities according to claim 3, characterized in that, The disaster level is based on the severity of the flood. The numerical division includes: like If the severity of the disaster is low; if If the disaster is of moderate to severe severity; if If so, the disaster is of high severity; if If so, the disaster is of extremely high severity.

5. The population loss estimation method considering rescue capabilities according to claim 1, characterized in that, The rescue capability coefficient includes: If a city's GDP is at or above the first GDP threshold, the city is a first-tier city, the city has an airport, and the most recent historical flood occurred within the first year or less, then the rescue capacity coefficient is 1.

0. If a city's GDP is between the second and first GDP thresholds, it is a second-tier city, has an airport, and the most recent historical flood occurred between the first and second years, then the rescue capacity coefficient is 0.

8. If a city's GDP is at or below the second GDP threshold, the city is a third-tier city, the city has more than one railway station, and the most recent historical flood occurred between the second and third years, then the rescue capacity coefficient is 0.

6. If the recent historical flood recurrence period is between the third and fourth year, the rescue capacity coefficient is 0.5; If the recurrence period exceeds the fourth year, the rescue capability coefficient will be reduced to 0.

4.

6. The population loss estimation method considering rescue capabilities according to claim 1, characterized in that, The quantification steps for the alarm time correction coefficient include: Theoretical alarm time for the target area Flood occurrence period Distance from riverbank and early warning facility level ; The alarm time correction coefficient of the target area is matched according to the preset alarm time correction coefficient determination table.

7. The population loss estimation method considering rescue capabilities according to claim 6, characterized in that, The theoretical alarm time The time interval from the issuance of an evacuation warning to the arrival of floodwaters in the target area; The period of the flood The period from 06:00 to 18:00 is designated as daytime; and the period from 18:00 to 06:00 the following day is designated as nighttime. The distance of the riverbank Based on a preset distance threshold, if the distance between the target area and the riverbank is greater than the distance threshold, it is determined that the area is far from the river. Otherwise, determine that the target area is close to the riverbank; The level of the early warning facility The division includes: If the target area has a working electronic alarm system, broadcast and SMS network dissemination system, and professional management personnel, then the early warning equipment level of the target area is determined to be high level; If the target area has basic early warning facilities, the early warning equipment level of the target area is determined to be medium level; If the target area has no fixed facilities and relies on manual alarm transmission, the early warning equipment level of the target area is determined to be low.

8. The population loss estimation method considering rescue capabilities according to claim 7, characterized in that, The alarm time correction coefficient determination table includes: If the theoretical alarm time for the target area The flood occurred within a time period that was greater than or equal to the first time threshold. For daytime, the warning facility level For a high level, the alarm time correction factor is 1.0; If the theoretical alarm time for the target area The time period during which the flood occurred is less than the first time threshold and greater than or equal to the second time threshold. During the daytime, the distance from the riverbank Due to its proximity to the riverbank, the early warning facility level is [not specified]. If the alarm level is medium, the alarm time correction factor is 0.8; If the theoretical alarm time for the target area The time period during which the flood occurred is less than the second time threshold and greater than or equal to the third time threshold. It was nighttime, and the riverbank was far from... Due to its proximity to the riverbank, the early warning facility level is [not specified]. If the alarm level is medium, the alarm time correction factor is 0.6; If the theoretical alarm time for the target area Less than the third time threshold, the period of flood occurrence It was nighttime, and the riverbank was far from... Due to its proximity to the riverbank, the early warning facility level is [not specified]. If the alarm level is low, the alarm time correction factor is 0.

4.

9. The population loss estimation method considering rescue capacity according to claim 1, characterized in that, The quantification steps for the comprehension level correction coefficient include: Acquire early warning information release records, communication facility coverage data, and basic social disaster prevention data for the target area; When the warning information release record has released a complete warning including the flood arrival time, inundation range and escape routes, and the communication facility coverage rate is greater than or equal to the preset coverage threshold, the level of understanding of the at-risk population in the target area is determined to be clear understanding, and the level of understanding correction coefficient is set to 1.0; if the warning content is complete but the communication facility coverage rate is lower than the coverage threshold, and there is only SMS push without community broadcast, it is determined to be general understanding, and the level of understanding correction coefficient is 0.8; secondary correction item for vulnerable groups: when the proportion of elderly and children in the area exceeds 30%, the coefficient is further reduced by 0.

1. When the warning information release record is missing, the warning content is incomplete, or the communication facility coverage is lower than the preset coverage threshold, the understanding level of the at-risk population in the target area is determined to be vague, and the understanding level correction coefficient is set to 0.

6.

10. The population loss estimation method considering rescue capabilities according to claim 1, characterized in that, The population loss is expressed as: In the formula, LR represents the population loss amount; LR represents the population loss rate. For high-risk population; This is a correction factor for alarm time. Adjustment coefficient for level of understanding; This represents the flood severity coefficient. This refers to the rescue capability coefficient. High-resolution population density; This represents the dynamically submerged area.

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