Flood exposure risk prediction method based on human-river spatial proximity

Abstract

The embodiment of the application discloses a flood exposure risk prediction method based on human-river spatial proximity relationship, comprising: obtaining a basin to be predicted; using current population data and future population data of the basin respectively, and expanding outward from the rivers of the basin to generate an effective area of human-river interaction; dividing the effective area into multiple gradient intervals according to the distance to the river, and identifying whether each gradient interval is in a population gathering state; merging a plurality of continuous gradient intervals in the population gathering state into an effective run, and identifying the human-river proximity type of the basin according to the number and position of the effective run; calculating the Gini coefficient according to the number of gradient intervals and the population of each gradient interval; and predicting the future flood exposure risk of the basin according to the changes of the current and future human-river proximity types and / or the Gini coefficient. The embodiment can improve the rationality of human-river relationship quantification and provide data support for flood exposure risk.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of geographic information modeling and disaster risk prediction technology, and in particular to a method for predicting flood exposure risk based on the spatial proximity relationship between humans and rivers. Background Technology

[0002] The human-river spatial proximity relationship (or simply human-river relationship) refers to the interaction between human settlements and river spaces. Generally, the closer the geographical distance and the closer the human-river relationship, the greater the potential risk of flood exposure along the river.

[0003] Current technologies primarily quantify human-river relationships using static Euclidean buffers. However, this quantification method suffers from the following problems when applied to large-scale flood exposure risk prediction: 1. Scale incompatibility: Static buffers cannot accommodate differences in watershed area and river class, resulting in a lack of comparability in quantitative results between large and small watersheds, and also a lack of comparability in flood exposure risks between watersheds; 2. Ignoring topographical constraints: Traditional straight-line distances ignore the constraints of river topography on the physical boundaries of settlements, making the quantitative results of human-river relationships unreasonable and reducing the rationality of flood exposure risk prediction; 3. Lack of dynamic evolution prediction: Static assessments are unable to reflect future trends, resulting in inaccurate predictions of future flood exposure risks.

[0004] Patent application CN110264111A provides a spatial quantification model of the human-land relationship regional system in mountainous areas based on geospatial data, and patent application CN115345283A provides a GRNN prediction method and system that takes into account the spatial relationship of neighboring sample points. Neither of these can solve the above-mentioned technical problems. Summary of the Invention

[0005] This invention provides a flood exposure risk prediction method based on the spatial proximity between humans and rivers, in order to solve at least one of the above-mentioned technical problems.

[0006] In a first aspect, embodiments of the present invention provide a flood exposure risk prediction method based on the spatial proximity relationship between humans and rivers, including: obtaining the watershed to be predicted; Using the current and future population data of the watershed, respectively, perform the following operations: S1. Based on the population data used, expand outward from the river in the basin to generate an effective area for human-river interaction; S2. Divide the effective area into multiple gradient intervals according to the distance to the river, and identify whether each gradient interval is in a state of population concentration. S3. Merge multiple consecutive gradient intervals in a state of population agglomeration into effective runs, and identify the human-river affinity type of the watershed based on the number and location of effective runs; S4. Calculate the Gini coefficient, which reflects the uneven distribution of population in the watershed, based on the number of gradient intervals and the population in each gradient interval. Based on the current and future human-river proximity type and / or changes in the Gini coefficient of the watershed, predict the future flood exposure risk of the watershed.

[0007] In a second aspect, embodiments of the present invention provide an electronic device, the electronic device comprising: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the flood exposure risk prediction method based on human-river spatial proximity as described in any embodiment.

[0008] Thirdly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the flood exposure risk prediction method based on human-river spatial proximity as described in any embodiment.

[0009] In summary, the embodiments of the present invention provide a flood exposure risk prediction method based on the spatial proximity between humans and rivers, which can achieve the following beneficial effects: 1. Based on the population distribution and distance gradient interval division of a river basin, a scale-adaptive distance metric for river basins is proposed. Specifically, by analyzing the population distribution from the river network to land units, an effective area for human activities is identified, which can simultaneously consider both topographic and population constraints. By linearly dividing the spatial domain into normalized gradient intervals, the differences caused by the spatial topology and basin area of ​​different river basins are reduced, achieving standardization and comparability of human-river affinity analysis between different river basins.

[0010] This approach overcomes the scale incompatibility of traditional static buffers in quantifying global human-river relationships, improves the accuracy of human-river relationship analysis and its applicability over a wide area, makes the quantification of human-river relationships more reasonable, and improves the accuracy of flood exposure risk prediction.

[0011] 2. Based on run theory, topological gradient extraction is performed, run identification is performed according to population aggregation status, and human-river affinity is clearly divided into three types: "near-river type", "far-river type" and "discrete type" according to effective run. This distinguishes the diverse and complex affinity topological structure, reflects the interaction pattern between human settlements and rivers in more detail, makes the quantification of human-river relationship more reasonable, and improves the accuracy of flood exposure risk prediction.

[0012] 3. It provides a quantitative and trend analysis method for inequality, enabling dynamic prediction of flood exposure risk. Specifically, by introducing an improved Gini coefficient to quantify the spatial inequality of human-river proximity, and further subdividing human-river relationships based on their types, it can precisely quantify the spatial inequality of human-river proximity and its evolutionary trend over time, providing more refined data support for flood risk exposure. Attached Figure Description

[0013] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0014] Figure 1 This is a flowchart of a flood exposure risk prediction method based on the spatial proximity between humans and rivers, provided by an embodiment of the present invention; Figure 2 This is a flowchart of another flood exposure risk prediction method based on the spatial proximity between humans and rivers provided in this embodiment of the invention; Figure 3 This is a schematic diagram illustrating the verification of the method of this application using statistical methods according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0016] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0017] In the description of this invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0018] Figure 1 This is a flowchart illustrating a flood exposure risk prediction method based on the spatial proximity between humans and rivers, provided in an embodiment of the present invention. The method is applicable to large areas including at least one watershed and is executed by electronic equipment. Figure 1 As shown, the method specifically includes: S110. Obtain the watershed to be predicted.

[0019] This embodiment can be applied to a large area including at least one watershed. When the area includes multiple watersheds, each watershed can be treated as the watershed to be predicted, and the operations of S110-S130 can be performed on each watershed to ultimately reflect the differences in human-river relationships and flood exposure risks between different watersheds.

[0020] In one specific implementation, this large area can be global. This embodiment first constructs a database of major river basins with global representativeness as the basis for subsequent analysis.

[0021] Optionally, MRB (Major River Basins of the World) data can be collected, combined with river flow sequence data derived from high-resolution topographic data (e.g., HydroSHEDS, Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales, hydrological data and maps derived from multi-scale elevation data of the Space Shuttle Radar Topographic Mapping Mission), and then watersheds that meet the following criteria can be selected as the targets for subsequent human-river relationship quantification and flood risk prediction: Condition 1: The area of ​​the water region is greater than or equal to 100,000 square kilometers; Condition 2: The total population of the river basin is greater than or equal to 1 million; Condition 3: The watershed is not primarily composed of lakes; Condition 4: The watershed has passed the geometric boundary consistency check (for example, since both MRB and Basin90m are derived from HydroSHEDS, it is necessary to ensure that the watersheds from the two sources have geometric consistency).

[0022] Then, the selected watersheds are coupled with the population data within the watersheds to form a major river watershed database containing comprehensive information such as the geometric boundaries of each watershed, the topology of the river network, and the population distribution, which serves as the data source for the entire method.

[0023] S120. Based on the current population data of the watershed, determine the human-river affinity type and Gini coefficient of the watershed in the current period; at the same time, based on the future population data of the watershed, determine the human-river affinity type and Gini coefficient of the watershed in the future period; wherein, the Gini coefficient is used to reflect the uneven distribution of the population in the watershed.

[0024] This embodiment assesses the uneven distribution of human-river affinity and population in different watersheds based on geographic and demographic information. For simplicity, current population data can be referred to as current population data, and future population data as future population data. This embodiment uses both current and future population data for the watershed to be predicted, and performs the following operations: S1. Based on the population data used, an effective area for human-river interaction is generated by expanding outward from the river in the watershed. Specifically, the watershed includes the river and the land surrounding the river. This step determines the basic shape of the human-river interaction area based on the river boundary constraints.

[0025] Optionally, the river boundary of the watershed is extracted, and each point on the river boundary is moved outward by the same distance along its respective normal direction, so that the population within the outline area formed by the moved points reaches a predetermined proportion of the total population of the watershed. This outline area is then considered the effective area for human-river interaction in the watershed. Optionally, the predetermined proportion can be 90% or 95%, and the distance moved by each point is called the effective radius of human-river interaction, which is the shortest path from each point on the river boundary along its respective normal direction to the effective area.

[0026] In one specific implementation, determining the effective area may include the following steps: Step 1: Determine the adaptive buffer radius of the river. Specifically, for a particular watershed The river network in the basin is denoted as The land area in the watershed is denoted as Then the river network can be At each point on the boundary, along the normal direction of the river boundary at that point, extend towards the land area in a certain step. The process involves gradual movement, with each point moving the same distance in each move. After each move, the points form a geometric envelope centered on the original river network. This gradual movement continues until the final geometric envelope covers the entire network. And with the smallest area, the cumulative distance moved relative to the original position at this point is denoted as... It represents the ability to completely cover the land area of ​​the watershed. The river network The minimum Euclidean buffer radius centered at the center.

[0027] Step 2: Construct the cumulative population distribution function Optional, can be Within this scope, based on high-resolution population data (such as GlobPOP, Global gridded population dataset), the cumulative population distribution function is calculated and constructed. This function represents the distance from the river boundary. The cumulative population within the geographical area represents the proportion of the total population of the watershed. Specifically, this can be calculated by extracting the distance from each spatial unit (e.g., a 1-kilometer grid) within the watershed to the nearest river boundary point. And the population of the unit, will The population of each identical spatial unit is summed to obtain the population of each The corresponding cumulative population; then each Dividing the corresponding cumulative population by the total population of the basin yields the cumulative population distribution function. .final, This indicates the distance from the river boundary from 0 to... The proportion of the cumulative population within the spatial range to the total population of the basin. [0,1], r It is about It is a monotonically increasing function.

[0028] Step 3: Determine the effective radius of human-river interaction based on the adaptive buffer radius and the cumulative population distribution function. To focus on the core areas of human activity, this embodiment defines the effective radius. Defined as the minimum distance to the river boundary that includes a preset population percentage X (e.g., typically 90% or 95%). Because The monotonicity of the property can be efficiently determined using a binary search algorithm. The specific steps are as follows: Set the search range as... By using a binary search method to repeatedly take the midpoint of the search interval, the search can be gradually approached. The minimum distance equal to or first exceeding X, i.e.: (1) Determine the effective radius of human river activities This can reduce the long-tail effect that may be caused by a sparse peripheral population.

[0029] Step 4: Extend the effective radius outward from each point on the river boundary along its respective normal direction. The distance is used to obtain the extended area, which is denoted as the effective area of ​​human-river interaction in the basin.

[0030] It can be seen that, for different watersheds, the effective area of ​​each watershed is different due to the different geometry and population distribution of each watershed.

[0031] S2. Divide the effective area into multiple gradient intervals according to the distance to the river, and identify whether each gradient interval is in a state of population concentration.

[0032] Gradient intervals refer to multiple intervals formed by equally spaced continuous variables. This embodiment addresses the limitations of traditional distance metrics that use fixed dimensions by dividing the intervals into multiple intervals based on the effective radius of each watershed. The effective area of ​​each watershed is linearly divided into a predetermined number (e.g., 100) gradient intervals, using gradient interval indices instead of geographic distances. Optionally, within a specific watershed, the effective radius of each point on the river boundary can be divided into multiple segments, with segments having the same point number forming a gradient interval around the river. For example, when the total number of gradient intervals is 100, the... Gradient intervals that is r Falling on [ , The gradient interval is a region composed of spatial units within a certain range. Although the length of the gradient interval may differ between different watersheds, the number of gradient intervals is consistent. This is equivalent to mapping different geographical distances to the same gradient interval index coordinates, which can eliminate differences caused by river area, spatial distribution, etc., and make the space between different watersheds comparable. In other words, through the standardization process of gradient interval division, the spatial relationship between people and rivers in different watersheds is mapped to a unified percentage coordinate system (0-100% relative distance), which facilitates cross-watershed comparative analysis.

[0033] Once the gradient intervals are obtained, the population concentration in each gradient interval can be determined based on the change in population density over the gradient intervals. Among them, population concentration It is the cumulative population distribution function Distance The first derivative reflects the rate of change of population density with distance. Over the discrete gradient interval, It can be approximated as: ,in, It is the normalized gradient interval width (e.g., 1%).

[0034] Then, based on the distribution of population concentration within the effective area, a population concentration threshold for identifying population aggregation status is determined. Optionally, to effectively distinguish between population cluster signals and background noise, this embodiment uses the interquartile range criterion in statistics to determine the global reference threshold. The specific approach is to aggregate the population concentration across all gradient intervals into a watershed sample space. The third quartile of the sample space is added to 1.5 times the interquartile range to determine the value as follows. This threshold can robustly identify significant population clustering signals.

[0035] Finally, gradient intervals where the population concentration exceeds the population concentration threshold are identified as areas of population agglomeration. Optionally, based on... Binarize the population concentration index series for each watershed: If Greater than If the gradient interval is then identified as a population clustering state; if Less than or equal to If the gradient interval is such that the population is dispersed, then the gradient interval is identified as a dispersed population state.

[0036] S3. Merge multiple consecutive gradient intervals in a state of population agglomeration into effective runs, and identify the human-river affinity type of the watershed based on the number and location of effective runs.

[0037] This step first uses run theory to identify continuous, consistent, valid runs. A "positive run" is a continuous run exceeding [a certain threshold]. The gradient interval sequence represents a clear population cluster.

[0038] Then, by analyzing the number of effective runs N and the starting position of the first effective run... The river-human affinity in the basin is divided into three types: Near-river type: There is one and only one valid journey, and the distance from the starting position of the valid journey to the river is zero (N=1 and...). When the value is 0, it indicates that the population is highly concentrated in the area along the river.

[0039] Dispersion: When there are no valid runs, or when there are two or more valid runs (N=0 or N≥2), it indicates that the population distribution is scattered or there is a multi-center, non-dominant clustering pattern.

[0040] Far-river type: There is one and only one valid journey, but the distance from the starting position of the valid journey to the river is not zero (N=1 and...). When the value is greater than 0, it indicates that the population is mainly concentrated in an area a certain distance from the river.

[0041] S4. Calculate the Gini coefficient, which reflects the uneven distribution of the population in the watershed, based on the number of gradient intervals and the population in each gradient interval.

[0042] This embodiment utilizes an improved Gini coefficient to measure the spatial inequality of human-river affinity and analyze its spatiotemporal evolution trend. The Gini coefficient is calculated based on the Lorenz curve principle by comparing the difference between the cumulative proportion of the population in the normalized gradient interval (i.e., the gradient interval) and the cumulative population share.

[0043] Optionally, first calculate the cumulative proportion of the number of gradient intervals for each gradient interval according to the following formula: (2) in, This represents the total number of gradient intervals. Indicates the first The cumulative proportion of the number of gradient intervals.

[0044] Meanwhile, the cumulative population proportion for each gradient interval is calculated using the following formula: (3) in, This represents the population of the i-th gradient interval. This represents the total population of the river basin. Indicates the first The cumulative population proportion of each gradient interval.

[0045] Finally, the Gini coefficient of the watershed is calculated according to the following formula. : (4) in, hour, and All are 0. and The x and y axes represent the starting points of the Lorenz curve, respectively. The closer the Gini coefficient is to 0, the more evenly the population distribution in the entire basin; the closer the Gini coefficient is to 1, the more concentrated the population in the entire basin.

[0046] In summary, by performing S1-S4 operations using current population data, we can obtain the current human-river affinity type and the current Gini coefficient (referred to as the current human-river affinity type and the current Gini coefficient); by performing S1-S4 operations using future population data, we can obtain the future human-river affinity type and the future Gini coefficient (referred to as the future human-river affinity type and the future Gini coefficient).

[0047] S130. Based on the current and future human-river proximity type and / or changes in the Gini coefficient of the watershed, predict the future flood exposure risk of the watershed.

[0048] The Gini coefficient is a further subdivision and quantification of the human-river relationship based on the human-river proximity type. Specifically: if the human-river proximity type is near-river, then a larger Gini coefficient indicates a higher degree of proximity (population concentrated closer to the river); if the human-river proximity type is dispersed, then a larger Gini coefficient indicates a higher degree of dispersion (i.e., more dispersed effective travel distances, but more concentrated populations within those travel distances); if the human-river proximity type is distant-river, then a larger Gini coefficient indicates a higher degree of distance (population concentrated further from the river).

[0049] Therefore, the risk of future flood exposure in a watershed can be predicted in the following two ways: Method 1: If the current human-river proximity type of a watershed is discrete or distant from the river, but the future human-river proximity type changes to near the river, then the future flood exposure risk of the watershed can be predicted to increase. Generally speaking, the flood control infrastructure and regulation measures of discrete or distant-river watersheds are insufficient compared to those of near-river watersheds. Therefore, when a discrete or distant-river watershed is expected to develop into a near-river watershed in the future, its resistance to flood disasters may be insufficient. A flood exposure risk warning can be issued to this watershed, and it can be recommended that it strengthen its flood control infrastructure construction and regulation measures.

[0050] Method 2: If the current and future human-river proximity type of a watershed is river-near type, but the future Gini coefficient is higher than the current Gini coefficient, then an increased flood exposure risk can be predicted for the watershed in the future. Specifically, for the same human-river proximity type, an increase in the Gini coefficient means that the population distribution will be more uneven. Therefore, when the current and future human-river proximity type of a watershed is river-near type, an increase in the future Gini coefficient compared to the current Gini coefficient indicates that the future population will be more concentrated near the river (i.e., becoming more river-oriented). Existing flood control capabilities may not be able to cope with higher-density human-river activities, thus a flood exposure risk warning can be issued, and it is recommended to strengthen flood control infrastructure construction and regulation measures.

[0051] It should be noted that the flood prevention risk prediction and early warning in this embodiment are only predictive suggestions based on the analysis of geographical and population data. Compared with suggestions without any data analysis, they improve the ability to cope with flood exposure risks. Compared with flood protection risk predictions given by traditional human-river relationship quantification methods, they are more accurate and improve the ability to cope with flood risks. However, whether to adopt the specific suggestions still needs to be analyzed by the decision-making body based on the specific circumstances. This embodiment only provides data support.

[0052] The entire method described above can also be based on Figure 2 Please refer to the flowchart shown for comprehension. Figure 2 middle In the calculation formula Indicates distance The population at that location; B in the Lorenz curve diagram represents the total area under the Lorenz curve, B i This represents the area under the Lorentz curve for the i-th gradient interval. It is important to emphasize that... Figure 2 In the near-river type, discrete type, and far-river type watersheds shown in the image, the gray area represents the watershed range, and the red curve represents the river center or effective flow. The watershed boundary is only a schematic boundary and does not represent any actual watershed boundary or any administrative boundary. It is only used to visually illustrate the distribution relationship between effective flow and watershed.

[0053] Furthermore, to demonstrate the effectiveness of the above method, this embodiment also verifies the objectivity and robustness of the aforementioned affinity classification using statistical methods. In the verification, the population distribution of different affinity types (near-river, far-river, and discrete) was fitted with a Pareto distribution, and the final results are as follows: Figure 3 As shown. Figure 3 In the figure, near-river, dispersion, and far-river represent near-river, dispersed, and far-river watersheds, respectively. All the thinner blue curves in the figure represent near-river type watersheds. By fitting the curves with probabilities, we can obtain the Pareto probability density function of the population in near-river basins (e.g.) Figure 3 (As shown by the thickest blue curve in the figure). Similarly, by using the same method for fitting, the population probability density function of the discrete watershed can be obtained (e.g., Figure 3 The thickest yellow curve is shown), and the population probability density function of distant river basins (such as...). Figure 3 (As shown by the thickest red curve). Scale parameters in the three population probability density function curves. , and These represent the distances from the initial population settlement point to the river in near-river, dispersed, and distant-river basins, respectively. It can be seen that this distance is the shortest in near-river basins, while the distances increase progressively in dispersed and distant-river basins, indicating that the measurement of the human-river relationship in this embodiment conforms to natural laws.

[0054] In summary, this embodiment provides a flood exposure risk prediction method based on the spatial proximity between humans and rivers, which can achieve the following beneficial effects: 1. Based on the population distribution and distance gradient interval division of a river basin, a scale-adaptive distance metric for river basins is proposed. Specifically, the effective radius is determined by calculating the cumulative population distribution from the river network to the land unit and a preset population ratio. This effective radius defines the core area of ​​human activity while considering both topographic and population constraints. Furthermore, by linearly dividing the spatial domain into normalized gradient intervals, the differences caused by the spatial topology and basin area of ​​different river basins are reduced, achieving standardization and comparability of human-river affinity analysis between different river basins.

[0055] This approach overcomes the scale incompatibility of traditional static buffers in quantifying global human-river relationships, improves the accuracy of human-river relationship analysis and its applicability over a wide area, makes the quantification of human-river relationships more reasonable, and improves the accuracy of flood exposure risk prediction.

[0056] 2. Based on run theory, topological gradient extraction is performed. By calculating the population concentration index, the cutoff level of settlement density on the river gradient is identified, and the topological texture of settlement distribution is extracted, transforming discrete settlement points into continuous human-river interaction gradient curves. Then, combined with the statistical interquartile range criterion, a global reference threshold is determined. By binarizing the population concentration index sequence and identifying run, human-river proximity is clearly divided into three types: "near-river type", "far-river type", and "discrete type".

[0057] This method can effectively identify and distinguish diverse and complex proximity topological structures such as "near-river type", "far-river type" and "discrete type", and more accurately reflect the interaction patterns between human settlements and rivers, making the quantification of human-river relationships more reasonable and improving the accuracy of flood exposure risk prediction.

[0058] 3. It provides a quantitative and trend analysis method for inequality, enabling dynamic prediction of flood exposure risk. By introducing an improved Gini coefficient to quantify the spatial inequality of human-river proximity, and further subdividing human-river relationships based on their types, it can precisely quantify the spatial inequality of human-river proximity and its evolutionary trend over time, providing more refined data support for flood risk exposure.

[0059] It should be noted that all data involved in this application are information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation portals are provided for users to choose to authorize or refuse.

[0060] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention, such as... Figure 4 As shown, the device includes a processor 60, a memory 61, an input device 62, and an output device 63; the number of processors 60 in the device can be one or more. Figure 4 Taking a processor 60 as an example; the processor 60, memory 61, input device 62, and output device 63 in the device can be connected via a bus or other means. Figure 4 Taking the example of a connection between China and Israel via a bus.

[0061] The memory 61, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the flood exposure risk prediction method based on human-river spatial proximity in this embodiment of the invention. The processor 60 executes various functional applications and data processing of the device by running the software programs, instructions, and modules stored in the memory 61, thereby realizing the aforementioned flood exposure risk prediction method based on human-river spatial proximity.

[0062] The memory 61 may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a given function; the data storage area may store data created based on terminal usage. Furthermore, the memory 61 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory, or other non-volatile solid-state storage device. In some instances, the memory 61 may further include memory remotely located relative to the processor 60, which can be connected to the device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0063] Input device 62 can be used to receive input digital or character information, and to generate key signal inputs related to user settings and function control of the device. Output device 63 may include display devices such as a display screen.

[0064] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the flood exposure risk prediction method based on human-river spatial proximity in any embodiment.

[0065] The computer storage medium of this invention can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0066] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0067] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0068] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—as well as conventional procedural programming languages—such as C or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0069] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A flood exposure risk prediction method based on the spatial proximity relationship between humans and rivers, characterized in that, include: Obtain the watershed to be predicted; Using the current and future population data of the watershed, respectively, perform the following operations: S1. Based on the population data used, expand outward from the river in the basin to generate an effective area for human-river interaction; S2. Divide the effective area into multiple gradient intervals according to the distance to the river, and identify whether each gradient interval is in a state of population concentration. S3. Merge multiple consecutive gradient intervals in a population-clustered state into an effective journey; if there is one and only one effective journey, and the distance from the starting position of the effective journey to the river is zero, identify the river-human affinity type of the basin as near-river type; if there is no effective journey or multiple effective journeys exist, identify the river-human affinity type of the basin as discrete type; if there is one and only one effective journey, and the distance from the starting position of the effective journey to the river is greater than zero, identify the river-human affinity type of the basin as far-river type. S4. Calculate the Gini coefficient, which reflects the uneven distribution of population in the watershed, based on the number of gradient intervals and the population in each gradient interval. If the current human-river proximity type in the basin is discrete or distant, but the future human-river proximity type changes to near-river, the risk of future flood exposure in the basin is predicted to increase; if the current and future human-river proximity type in the basin is near-river, but the future Gini coefficient is larger than the current Gini coefficient, the risk of future flood exposure in the basin is predicted to increase.

2. The method according to claim 1, characterized in that, S1 includes: Extract the river boundaries of the watershed; Each point on the river boundary is moved outward by the same distance along its respective normal direction, so that the population within the outline area formed by the moved points reaches a set proportion of the total population of the basin, and the outline area is used as the effective area for human-river interaction.

3. The method according to claim 1, characterized in that, The effective area is divided into multiple gradient intervals according to its distance from the river, including: The shortest path from each point on the river boundary to the effective area along its respective normal direction is taken as the effective radius of human-river interaction for each point, wherein the effective radius length of each point is the same. The effective radius of each point is divided into multiple segments, and the segments with the same index at each point together form a gradient interval around the river.

4. The method according to claim 1, characterized in that, The identification of whether each gradient interval is in a state of population agglomeration includes: Based on the variation of population density with gradient intervals, the population concentration of each gradient interval is determined; Based on the distribution of population concentration within the effective area, a population concentration threshold for identifying population aggregation status is determined; Gradient intervals where the population concentration exceeds the population concentration threshold are identified as being in a state of population agglomeration.

5. The method according to claim 4, characterized in that, The determination of population concentration in each gradient interval based on the variation of population density with gradient intervals includes: Calculate the ratio of the cumulative population distribution change between every two adjacent gradient intervals to the width of the gradient interval to obtain the population concentration of each gradient interval.

6. The method according to claim 1, characterized in that, S4 include: Calculate the cumulative proportion of gradient intervals for each gradient interval using the following formula: in, This represents the total number of gradient intervals. Indicates the first The cumulative proportion of the number of gradient intervals in each gradient interval; Calculate the cumulative population proportion for each gradient interval using the following formula: in, Indicates the first i Population in each gradient interval, This represents the total population of the river basin. Indicates the first The cumulative population proportion of each gradient interval; The Gini coefficient of the watershed is calculated using the following formula. : Among them, the closer the Gini coefficient is to 0, the more balanced the population distribution; hour, and All are 0. and These are the x and y coordinates of the starting point of the Lorentz curve, respectively.

7. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the flood exposure risk prediction method based on human-river spatial proximity as described in any one of claims 1-6.

8. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the flood exposure risk prediction method based on human-river spatial proximity as described in any one of claims 1-6.

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