Flood inundation extent prediction method, system, electronic device, and storage medium

Abstract

The application provides a flood inundation range prediction method, system, electronic equipment and storage medium, and relates to the technical field of data processing. The method comprises the following steps: when a new real-time SAR image is acquired, determining a new basic inundation range according to the real-time SAR image and water surface fluctuation data; determining a current real-time water body volume according to the new basic inundation range and a terrain digital elevation model; determining an actual water body increment according to the current real-time water body volume and a previous real-time water body volume; updating a dynamic coefficient according to the actual water body increment and precipitation in a revisit period; determining a predicted water body increment according to the acquired predicted precipitation and the updated dynamic coefficient; and determining a flood inundation prediction range according to the predicted water body increment and the terrain digital elevation model, with the new basic inundation range as a starting inundation range. The evolution of the flood inundation range can be predicted in a timely manner in the revisit period, the timeliness and accuracy of the prediction are taken into account, and the disaster-affected area can be warned in advance.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a flood inundation area prediction method and system, an electronic device and a storage medium. BACKGROUND

[0002] In the response to flood disasters, it is necessary to monitor the flood inundation area so as to remind people in the living area threatened by the flood to avoid and evacuate in time. The existing method uses SAR (Synthetic Aperture Radar) images to monitor the inundation area, but when monitoring according to the SAR images, the flood inundation area cannot be monitored within the revisit period of the SAR images due to the limitation of the revisit period of the SAR images, and the inundation area can only be roughly predicted according to experience.

[0003] The existing inundation area monitoring method relies on SAR images, and the monitoring of the flood inundation area has a lag, so it is difficult to give timely warning. SUMMARY

[0004] The problem to be solved by the present application is that the existing method is limited by the revisit period of the SAR images, and the monitoring of the flood inundation area has a lag, so it is difficult to give timely warning.

[0005] To solve the above problems, in a first aspect, the present application provides a flood inundation area prediction method, comprising:

[0006] When a new real-time SAR image is obtained, a new basic inundation area is determined according to the real-time SAR image and water surface fluctuation data;

[0007] A current real-time water body volume is determined according to the new basic inundation area and a terrain digital elevation model;

[0008] An actual water body increment is determined according to the current real-time water body volume and a previous real-time water body volume;

[0009] A dynamic coefficient is updated according to the actual water body increment and a precipitation within a revisit period, the revisit period being a period of obtaining SAR images;

[0010] A predicted water body increment is determined according to the obtained predicted precipitation and the updated dynamic coefficient;

[0011] A flood inundation prediction range is determined according to the predicted water body increment and the terrain digital elevation model, with the new basic inundation area as a starting inundation range.

[0012] Optionally, the water body increment wherein A represents the dynamic coefficient, and P represents the precipitation.

[0013] Optionally, the determining the new basic inundation range according to the real-time SAR image and the water surface fluctuation data when a new real-time SAR image is acquired comprises:

[0014] When a new real-time SAR image is acquired, real-time water level data of a preset time length is intercepted based on a time point of acquisition of the real-time SAR image, the real-time water level data being water level data collected by a water level sensor;

[0015] According to the intercepted real-time water level data, a plurality of water level peak-valley difference values are obtained, and a water level fluctuation mean value is determined;

[0016] According to the water level fluctuation mean value and the real-time SAR image, a new basic inundation range is determined.

[0017] Optionally, the determining the new basic inundation range according to the water level fluctuation mean value and the real-time SAR image comprises:

[0018] According to the real-time SAR image and a preset threshold, a first water body identification area is determined;

[0019] According to the water level fluctuation mean value and a unit water level fluctuation value, a fluctuation increment is determined;

[0020] According to a backscattering intensity of the real-time SAR image and the fluctuation increment, a corrected image is obtained;

[0021] According to the corrected image and a preset threshold, a second water body identification area is determined;

[0022] The first water body identification area and the second water body identification area are superimposed and fused to obtain a water body coverage area;

[0023] According to the water body coverage area, a new basic inundation range is determined.

[0024] Optionally, the determining the fluctuation increment according to the water level fluctuation mean value and the unit water level fluctuation value comprises:

[0025] According to the unit water level fluctuation value, a radar incident angle, a radar wavelength, a wave number and a dielectric constant of a natural water body, a backscattering intensity corresponding to the unit water level fluctuation value is obtained;

[0026] According to the water level fluctuation mean value and the unit water level fluctuation value, a fluctuation multiple is determined;

[0027] According to the fluctuation multiple and the backscattering intensity corresponding to the unit water level fluctuation value, a backscattering intensity corresponding to the water level fluctuation mean value is determined;

[0028] According to the backscattering intensity corresponding to the water level fluctuation mean value and a standard backscattering intensity, a fluctuation increment is determined.

[0029] Optionally, the backscattering intensity corresponding to the unit water level fluctuation value is:

[0030] ,

[0031] , ,

[0032] wherein, represents the backscattering intensity corresponding to the unit water level fluctuation value; represents the water surface roughness spectrum; R represents the reflection coefficient; represents the dielectric constant of the natural water body; θ represents the radar incidence angle; represents the wave number; h represents the unit water level fluctuation value; λ represents the radar wavelength.

[0033] Optionally, the fluctuation increment is:

[0034] ,

[0035] wherein, represents the fluctuation increment, represents the mean value of water level fluctuation, and h represents the unit water level fluctuation value; represents the backscattering intensity corresponding to the unit water level fluctuation value; represents the standard backscattering intensity.

[0036] In a second aspect, the present application further provides a flood inundation range prediction system, comprising:

[0037] a basic inundation range updating module configured to determine a new basic inundation range according to a real-time SAR image and water surface fluctuation data when the real-time SAR image is acquired;

[0038] a water body increment determining module configured to determine a current real-time water body volume according to the new basic inundation range and a digital terrain elevation model, and configured to determine an actual water body increment according to the current real-time water body volume and a previous real-time water body volume;

[0039] a dynamic coefficient updating module configured to update a dynamic coefficient according to the actual water body increment and a precipitation amount in a revisit period, the revisit period being a period for acquiring the SAR image;

[0040] a water body increment predicting module configured to determine a predicted water body increment according to an acquired predicted precipitation amount and the updated dynamic coefficient;

[0041] a flood inundation predicting module configured to determine a flood inundation prediction range according to the predicted water body increment and the digital terrain elevation model, with the new basic inundation range as a starting inundation range.

[0042] Thirdly, the present invention provides an electronic device, including a memory and a processor;

[0043] The memory is used to store computer programs;

[0044] The processor is configured to implement the flood inundation range prediction method as described in the first aspect when executing the computer program.

[0045] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the flood inundation range prediction method as described in the first aspect.

[0046] This invention provides a method, system, electronic device, and storage medium for predicting flood inundation range. Compared with existing technologies, it has the following advantages:

[0047] Based on newly acquired real-time SAR images and water surface fluctuation data, a new basic inundation range is determined. This basic inundation range is then located in the topographic digital elevation model (DEM). The current real-time water volume within the basic inundation range can be calculated using data from the DEM. The actual water volume increment can be determined based on the current real-time water volume and the previous real-time water volume. Precipitation data within the revisit period is statistically analyzed, and the dynamic coefficient needs to be dynamically updated as precipitation and precipitation time increase. This ensures that subsequent predictions of water volume increments based on predicted precipitation data from meteorological data and real-time updated dynamic data are more accurate. Based on the initial inundation range, a new flood inundation range is searched upwards in the DEM until the hollow volume between the new flood inundation range and the initial inundation range matches the predicted water volume increment. This determines the flood inundation prediction range, enabling relatively accurate prediction of the flood inundation range even within the revisit period. This timely prediction balances timeliness and accuracy, allowing for early warning of disaster-stricken areas and reducing flood damage. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art 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.

[0049] Figure 1 A flowchart illustrating a flood inundation range prediction method provided in an embodiment of the present invention;

[0050] Figure 2A new basic inundation range analysis process schematic diagram provided by the embodiment of the present application;

[0051] Figure 3 A flood inundation range prediction system structure schematic diagram provided by the embodiment of the present application. DETAILED DESCRIPTION

[0052] In order to make the purposes, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application are described clearly and completely. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0053] In order to better understand the above technical solutions, the above technical solutions will be described in detail in combination with the accompanying drawings and specific embodiments.

[0054] As shown in the figure, the flood inundation range prediction method provided by the embodiment of the present application comprises: Figure 1

[0055] S1: When a new real-time SAR image is acquired, a new basic inundation range is determined according to the real-time SAR image and water surface fluctuation data.

[0056] S2: A current real-time water body volume is determined according to the new basic inundation range and a terrain digital elevation model.

[0057] S3: An actual water body increment is determined according to the current real-time water body volume and a previous real-time water body volume.

[0058] S4: A dynamic coefficient is updated according to the actual water body increment and a precipitation amount in a revisit period, the revisit period being a period of acquiring SAR images.

[0059] S5: A predicted water body increment is determined according to an acquired predicted precipitation amount and the updated dynamic coefficient.

[0060] S6: A flood inundation prediction range is determined according to the predicted water body increment and the terrain digital elevation model, with the new basic inundation range as a starting inundation range.

[0061] ​In the embodiment, a new basic inundation range is determined according to the newly acquired real-time SAR image and water surface fluctuation data, at this time, the influence of the water surface fluctuation data is considered when the inundation range is determined, and finally the new basic inundation range obtained is the real inundation range, the new basic inundation range obtained is more accurate, the basic inundation range is positioned in the digital terrain model, and the current real-time water volume under the basic inundation range can be calculated according to the data of the digital terrain model, the water volume is the water volume when the new real-time SAR image is acquired; according to the current real-time water volume and the previous real-time water volume (i.e. the water volume when the real-time SAR image is acquired last time), the actual water volume increment can be determined; the precipitation in the revisit period is counted, since the actual water volume increment of the monitoring area is closely related to the precipitation of the area, and presents a positive correlation, but as the precipitation increases, the water absorption capacity of the land gradually decreases, and the ground water gradually increases. As the ground water increases and the water level rises, the precipitation gathered in the depression encounters less resistance, and the gathering speed gradually increases, so in unit time, the precipitation is also gradually increased into the water volume of the water body in the inundation range, so the dynamic coefficient needs to be dynamically updated as the precipitation and precipitation time increase, so that the predicted water volume increment obtained according to the predicted precipitation in the meteorological data and the real-time updated dynamic data is more accurate. After knowing the relatively accurate predicted water volume increment after a certain time in the future, the new flood inundation range is gradually searched upwards in the digital terrain model based on the starting inundation range, until the hollow volume between the new flood inundation range and the starting inundation range is consistent with the predicted water volume increment, then the new flood inundation range is determined as the flood inundation prediction range, so that the flood inundation range can be predicted more accurately in the revisit period, and the evolution of the flood inundation range can be predicted in time, and the timeliness and accuracy of the prediction are taken into account, so that the disaster area can be warned in advance, and the loss of flood disaster can be reduced.

[0062] The following describes each step in detail.

[0063] S1: When a new real-time SAR image is acquired, a new basic inundation range is determined according to the real-time SAR image and water surface fluctuation data.

[0064] S110: When a new real-time SAR image is acquired, real-time water level data of a preset time length is intercepted based on the acquisition time point of the real-time SAR image, and the real-time water level data is water level data collected by a water level sensor.

[0065] Specifically, the water level sensor is arranged around the natural water body, for example, arranged at the edge of the river bank, and arranged at a certain height in the vertical direction on the river bank, so that the water level sensor has a larger monitoring range and can monitor the water level data from the ground of the river bank to a specified distance above the ground. The specified distance can be 0.5 m or 1 m, etc.

[0066] SAR is a kind of radar that actively emits microwaves through a radar antenna to generate images by receiving echo signals reflected by ground objects. The calm water surface forms a mirror reflection, and the echo intensity is extremely low, which appears black in the synthetic aperture radar image. The rough ground surface or vegetation scattering is relatively strong, which appears bright in the synthetic aperture radar image, and thus the water area is identified. Due to the influence of strong wind on the water surface, the water surface will fluctuate, causing the roughness of the water surface to change, which affects the authenticity of the SAR image. Therefore, before analyzing the SAR image, the influence of water surface fluctuation on the SAR image needs to be considered. In a short period of time, the difference in water surface fluctuation is not large. In order to accurately obtain the water level fluctuation at the time of obtaining the real-time SAR image, a time window with the time as the starting point can be intercepted. The data in the time window is closest to the water surface fluctuation corresponding to the real-time SAR image obtained at the time. The current water level fluctuation is analyzed through the intercepted real-time water level data. On the one hand, this can reduce the amount of analysis data, and on the other hand, it can also improve the accuracy of the analysis result. The water level sensor can monitor a water level data at each moment. By statistically arranging these water level data in time sequence on the time axis, the continuous fluctuation of the water surface water level can be obtained, and the water level fluctuation curve can be formed. From the curve, a plurality of water level peak values and a plurality of water level valley values can be picked up.

[0067] S120: According to the intercepted real-time water level data, a plurality of water level peak-valley difference values are obtained, and a water level fluctuation average value is determined.

[0068] Specifically, using an adjacent group of peak values and valley values, a water level peak-valley difference value can be obtained. Through a plurality of peak-valley values, a plurality of water level peak-valley difference values can be obtained. Then, an average value is taken to obtain a water level peak-valley difference average value, which is the water level fluctuation average value. This value is the water surface fluctuation caused by strong wind blowing the water surface or water surface flow. The water level fluctuation average value represents the degree of water surface fluctuation.

[0069] S130: According to the water level fluctuation average value and the real-time SAR image, a new basic submerged range is determined. As shown in the figure, the specific steps of the new basic submerged range are as follows. Figure 2

[0070] S131: According to the real-time SAR image and a preset threshold value, a first water body identification area is determined.

[0071] ​Specifically, receiving the echo signal reflected by the ground object can generate a high-resolution image, different objects or surfaces with different roughness have different backscattering intensities of microwaves, by the backscattering intensity and a preset threshold, a threshold segmentation method is used to determine the water body and the non-water body, for example, the preset threshold is set as-15dB, the area with the backscattering intensity greater than-15dB is determined as the non-water body, and the area with the backscattering intensity less than or equal to-15dB is determined as the water body. Accordingly, the first water body identification area in the real-time SAR image can be distinguished.

[0072] S132: determining a fluctuation increment according to the water level fluctuation mean value and the unit water level fluctuation value. The determination of the fluctuation increment is specifically as follows.

[0073] According to the unit water level fluctuation value, the radar incidence angle, the radar wavelength, the wave number and the dielectric constant of the natural water body, the backscattering intensity corresponding to the unit water level fluctuation value is obtained.

[0074] Specifically, when the water surface fluctuates, the increment of the backscattering intensity is the result of the joint action of the water surface roughness spectrum and the reflection coefficient. The roughness spectrum affects the scattering path and scattering angle of light by changing the microstructure and macrostructure of the water surface; the reflection coefficient determines the proportion of light energy participating in scattering and the interference degree of reflected light to scattered light.

[0075] The backscattering intensity corresponding to the unit water level fluctuation value is:

[0076]

[0077]

[0078] wherein, represents the backscattering intensity corresponding to the unit water level fluctuation value; represents the water surface roughness spectrum; R represents the reflection coefficient; represents the dielectric constant of the natural water body; θ represents the radar incidence angle; represents the wave number; h represents the unit water level fluctuation value; λ represents the radar wavelength.

[0079] According to the water level fluctuation mean value and the unit water level fluctuation value, a fluctuation multiple is determined.

[0080] According to the fluctuation multiple and the backscattering intensity corresponding to the unit water level fluctuation value, the backscattering intensity corresponding to the water level fluctuation mean value is determined.

[0081] ​​​Specifically, as the water level fluctuation value increases, the backscattering intensity also increases, and the water level fluctuation value and the backscattering intensity corresponding to a unit water level fluctuation value increase at approximately the same rate, so after the backscattering intensity corresponding to a unit water level fluctuation value is calculated, the ratio between the water level fluctuation average and the unit water level fluctuation value can be calculated to obtain the fluctuation multiple, and then the backscattering intensity corresponding to the water level fluctuation average is obtained by multiplying the fluctuation multiple by the backscattering intensity corresponding to a unit water level fluctuation value.

[0082] According to the backscattering intensity corresponding to the water level fluctuation average and the standard backscattering intensity, the fluctuation increment is determined. The fluctuation increment is obtained by subtracting the standard backscattering intensity from the backscattering intensity corresponding to the water level fluctuation average.

[0083] The fluctuation increment is:

[0084] ,

[0085] wherein, the fluctuation increment, the water level fluctuation average, and h represents a unit water level fluctuation value; the backscattering intensity corresponding to a unit water level fluctuation value; the standard backscattering intensity.

[0086] Exemplarily, when the water surface is calm, h=0, only specular reflection, and the standard backscattering intensity ≈-25dB. Assuming the radar parameters: C-band (λ=5.6cm), VV polarization, incident angle θ=30°, and the water surface medium: fresh water (dielectric constant =80, Fresnel reflection coefficient R≈0.95), then ≈112rad / m, when the unit water level fluctuation value h=1cm, , ≈-16.8dB. If the water level fluctuation average is also 1cm, the fluctuation increment is -16.8-(-25)=8.2dB. If the water level fluctuation average is 1.5cm, the fluctuation increment is -16.8×1.5-(-25)=-0.2dB.

[0087] S133: Obtain a corrected image according to the backscattering intensity of the real-time SAR image and the fluctuation increment.

[0088] Specifically, the corrected backscattering intensity is obtained by subtracting the fluctuation increment from the backscattering intensity of the real-time SAR image, and then the corrected image is formed, and in the corrected image, the area of the dark color region is increased.

[0089] S134: Determine a second water body recognition region according to the corrected image and a preset threshold value.

[0090] Specifically, according to the backscattering intensity in the modified image and a preset threshold, a second water body recognition area is obtained. Wherein, the area with backscattering intensity less than or equal to the preset threshold is determined as water body, constituting the second water body recognition area.

[0091] S135: superimpose and fuse the first water body recognition area and the second water body recognition area to obtain a water body coverage area.

[0092] Specifically, when superimposing the first water body recognition area and the second water body recognition area, it is equivalent to finding the union of the two areas. Further, when there is a highlight area (i.e. an area with backscattering intensity greater than the preset threshold) inside the two water body areas, the area of the highlight area is calculated. If the area of the highlight area is less than a preset area, it means that the area may be a water body that has not been recognized or a non-water body area surrounded by the water body area, and the highlight area can be directly taken as the water body coverage area. When the area of the highlight area is greater than or equal to the preset area, it means that the area is a larger non-water body area surrounded by the water body area, and the boundary of the highlight area is separately outlined as a non-water body area. In addition, when part of the boundary of the two water body areas is not coherent, if the length of the incoherent position is less than a preset length, the incoherent position is directly connected; if the length of the incoherent position is greater than or equal to the preset length, the incoherent state is maintained, and then the edge burr of the water body coverage area is smoothed. The obtained water body coverage area takes into account both the first water body recognition area and the second water body recognition area. On the one hand, it can ensure that the recognized water body coverage area is based on the original natural water body position (i.e. the position of the first water body recognition area) for diffusion, avoiding the problem of deviation of the recognized water body coverage area from the natural water body position, which may cause significant errors in the recognition result. On the other hand, the second water body recognition area obtained after modification may be overcorrected, resulting in the loss of part of the water body. However, the lost part may be the area covered by the first water body recognition area. After superimposing the two areas, the error caused by overcorrection can be reduced, and the accuracy of the recognition result can be improved.

[0093] S136: determine a new basic inundation range according to the water body coverage area.

[0094] Specifically, in order to better display the flood inundation range, the water body coverage area can be highlighted and the boundary of the water body coverage area can be outlined to better display the new basic inundation range.

[0095] S2: determine a current real-time water body volume according to the new basic inundation range and the terrain digital elevation model.

[0096] Specifically, the SAR image and the terrain digital elevation model are placed in the same spatial coordinate system, the submerged range obtained by analyzing the SAR image is equivalent to the projection of a certain area in the terrain digital elevation model on the horizontal plane, the obtained basic submerged range is moved up and down in the terrain digital elevation model, when the boundary of the basic submerged range coincides with the surface of the virtual entity in the terrain digital elevation model, the spatial position of the basic submerged range in the terrain digital elevation model is obtained, and the volume of the non-virtual entity covered by the basic submerged range in the terrain digital elevation model is calculated, that is, the volume of the water body, and then the current real-time water body volume is obtained.

[0097] S3: determining an actual water body increment according to the current real-time water body volume and the previous real-time water body volume. Specifically, the actual water body increment is obtained by subtracting the previous real-time water body volume from the current real-time water body volume.

[0098] S4: updating a dynamic coefficient according to the actual water body increment and the precipitation in a revisit period, the revisit period being the period of obtaining the SAR image.

[0099] Specifically, the water body increment and the precipitation present a positive correlation relationship, the revisit period of the SAR can be several hours or several days, in order to reduce the prediction error, a plurality of radars in a multi-satellite networking system can be used to monitor the same area respectively, or the task of flood monitoring can be set as a priority execution task, and the revisit period can be shortened to several hours or even about half an hour, therefore, within several hours or half an hour, the relationship between the water body increment and the precipitation can be fitted as the water body increment Wherein, A represents a dynamic coefficient, and P represents a precipitation. With the increase of the precipitation, the collection speed of the ground water to the natural water area or the low-lying area is improved, and the collection amount in a unit time is increased, because the ground soil has a certain water absorption capacity at the initial stage of rainfall, but with the increase of the precipitation, the ground soil water absorption speed gradually increases until saturation, or the ground water absorption speed is less than the rainfall speed, at which time the ground water appears, the ground water increases, submerges small obstacles on the ground or under the scouring of the ground water and the rainfall, and a flow channel is naturally formed on the ground. The subsequent rainfall flows along the flow channel formed in the early stage, and the flow speed is improved. There are other factors that affect the dynamic coefficient A. The dynamic coefficient A is in a state of change. The dynamic coefficient A does not change too much between two adjacent revisit periods, that is, the dynamic coefficient A has a certain continuity and inheritance. The actual water body increment obtained newly and the precipitation in the revisit period are used to update the dynamic coefficient A, and the updated dynamic coefficient A is used to predict the water body increment. The water body increment obtained has a certain error, but the error is improved compared with the result obtained by the traditional method or the experience estimation. The error is within an acceptable range in a short time, and the error is not accumulated. After a new SAR image is obtained again, the dynamic coefficient A is updated again, and the accumulated error in the previous period is eliminated. That is to say, the accumulated error is limited in a revisit period, so a compensation term can be added in the fitted relationship to further improve Wherein, K is a compensation coefficient, and t is a time length after a new real-time SAR image is obtained. In a time period in which the precipitation increases or remains unchanged in a unit time, the compensation coefficient K takes a positive value, and K is a constant value. In a time period in which the precipitation gradually decreases in a unit time, the compensation coefficient K takes a negative value, and K is a constant value. When the dynamic coefficient is updated, the compensation term can be ignored, and A is updated only by using the improved fitted relationship before the improvement. The improved fitted relationship can further reduce the prediction error in the revisit period, so that the prediction result is closer to the true value.

[0100] S5: The predicted water body increment is determined according to the obtained predicted precipitation and the updated dynamic coefficient.

[0101] Specifically, the predicted precipitation after a period of time can be directly obtained from the meteorological data, and the accuracy of the short-time prediction is high. For example, the prediction within several hours or within a day is relatively accurate, and the predicted water body increment calculated accordingly is also relatively accurate.

[0102] S6: The new basic submergence range is used as a starting submergence range, and the flood submergence prediction range is determined according to the predicted water body increment and the terrain digital elevation model.

[0103] Specifically, based on the initial inundation range, a reference plane is established with the level surface where the initial inundation range is located as a starting plane. The reference plane is moved in the digital terrain model from the starting plane until the volume between the reference plane and the initial inundation range that is connected to the initial inundation range is equal to the value of the predicted water body increment divided by the water density, so as to determine the area where the reference plane intersects with the digital terrain model and is connected to the initial inundation range as the flood inundation prediction range.

[0104] As shown in the Figure 3 flood inundation prediction system provided by the embodiment of the present application comprises:

[0105] The basic inundation range updating module 100 is configured to determine a new basic inundation range according to the real-time SAR image and the water surface fluctuation data when the new real-time SAR image is acquired.

[0106] The water body increment determination module 200 is configured to determine a current real-time water body volume according to the new basic inundation range and the digital terrain model, and determine an actual water body increment according to the current real-time water body volume and a previous real-time water body volume.

[0107] The dynamic coefficient updating module 300 is configured to update a dynamic coefficient according to the actual water body increment and a precipitation amount in a revisit period, the revisit period being a period for acquiring the SAR image.

[0108] The water body increment prediction module 400 is configured to determine a predicted water body increment according to the acquired predicted precipitation amount and the updated dynamic coefficient.

[0109] The flood inundation prediction module 500 is configured to determine a flood inundation prediction range according to the predicted water body increment and the digital terrain model, with the new basic inundation range as an initial inundation range.

[0110] In the embodiment, the flood inundation prediction system has similar beneficial effects to the flood inundation prediction method described above, and thus will not be described again here.

[0111] The electronic device provided by the embodiment of the present application comprises a memory and a processor. The memory is configured to store a computer program. The processor is configured to implement the flood inundation prediction method described above when the computer program is executed.

[0112] The computer readable storage medium provided by the embodiment of the present application has a computer program stored thereon. When the computer program is executed by a processor, the flood inundation prediction method described above is implemented.

[0113] In the present embodiment, the electronic device and the computer readable storage medium have similar advantages to the flood inundation area prediction method described above, and will not be described again here.

[0114] An electronic device that can be a server or a client of the present application will now be described, which is an example of a hardware device that can be applied to various aspects of the present application. The electronic device is intended to represent a wide variety of digital electronic computing devices, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computing devices. The electronic device can also represent a wide variety of mobile devices, such as personal digital assistants, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown here, their connections, and their functions, as well as the software implemented by the electronic device, are meant only to be examples and are not intended to limit the present application as described and / or claimed herein.

[0115] The electronic device includes a computing unit that can perform various appropriate actions and processes in accordance with a computer program stored in a read-only memory (ROM) or a computer program loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for device operation can also be stored. The computing unit, the ROM, and the RAM are connected to each other through a bus. An input / output (I / O) interface is also connected to the bus.

[0116] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by a computer program instructing relevant hardware, and the program can be stored in a computer readable storage medium. When the program is executed, it can include the processes of the above-mentioned embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM). In the present application, the units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., they can be located in one place or distributed on multiple network units. Some or all of the units can be selected to achieve the purpose of the present embodiment. In addition, the functional units in each embodiment of the present application can be integrated into one processing unit, or each unit can exist physically, or two or more units can be integrated into one unit. The integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0117] It is to be noted that, in the present text, relational terms such as first and second and the like can be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0118] The above examples are merely used to illustrate the technical solutions of the present application, but not to limit it; although the present application has been described in detail with reference to the foregoing examples, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A flood inundation extent prediction method, characterized by, The method comprises the following steps: when a new real-time SAR image is acquired, a new basic inundation range is determined according to the real-time SAR image and water surface fluctuation data; a current real-time water body volume is determined according to the new basic inundation range and a terrain digital elevation model; an actual water body increment is determined according to the current real-time water body volume and a previous real-time water body volume; a dynamic coefficient is updated according to the actual water body increment and a precipitation amount in a revisit period, the revisit period being a period for acquiring the SAR image; a predicted water body increment is determined according to the acquired predicted precipitation amount and the updated dynamic coefficient; a flood inundation prediction range is determined according to the predicted water body increment and the terrain digital elevation model, with the new basic inundation range as a starting inundation range.

2. The flood inundation mapping method of claim 1, wherein, Water body increment where A represents a dynamic coefficient and P represents the amount of precipitation.

3. The flood inundation mapping method of claim 1, wherein, The step of determining the new basic inundation range according to the real-time SAR image and the water surface fluctuation data when the new real-time SAR image is acquired comprises the following steps: when the new real-time SAR image is acquired, real-time water level data of a preset time length is intercepted at a time point of acquisition of the real-time SAR image, the real-time water level data being water level data collected by a water level sensor; a plurality of water level peak-valley difference values are obtained according to the intercepted real-time water level data, and a water level fluctuation average value is determined; the new basic inundation range is determined according to the water level fluctuation average value and the real-time SAR image.

4. The flood inundation mapping method of claim 3, wherein, The step of determining the new basic inundation range according to the water level fluctuation average value and the real-time SAR image comprises the following steps: a first water body identification area is determined according to the real-time SAR image and a preset threshold value; a fluctuation increment is determined according to the water level fluctuation average value and a unit water level fluctuation value; a corrected image is obtained according to a backscattering intensity of the real-time SAR image and the fluctuation increment; a second water body identification area is determined according to the corrected image and the preset threshold value; the first water body identification area and the second water body identification area are superimposed and fused to obtain a water body coverage area; the new basic inundation range is determined according to the water body coverage area.

5. The flood inundation mapping method of claim 4, wherein, The step of determining the fluctuation increment according to the water level fluctuation average value and the unit water level fluctuation value comprises the following steps: a backscattering intensity corresponding to the unit water level fluctuation value is obtained according to the unit water level fluctuation value, a radar incident angle, a radar wavelength, a wave number and a dielectric constant of a natural water body; a fluctuation multiple is determined according to the water level fluctuation average value and the unit water level fluctuation value; a backscattering intensity corresponding to the water level fluctuation average value is determined according to the fluctuation multiple and the backscattering intensity corresponding to the unit water level fluctuation value; the fluctuation increment is determined according to the backscattering intensity corresponding to the water level fluctuation average value and a standard backscattering intensity.

6. The flood inundation mapping method of claim 5, wherein, The backscattering intensity corresponding to the unit water level fluctuation value is: ， ， ， wherein, represents the backscattering intensity corresponding to the unit water level fluctuation value; represents the water surface roughness spectrum; R represents the reflection coefficient; represents the dielectric constant of the natural water body; θ represents the radar incidence angle; represents the wave number; h represents the unit water level fluctuation value; λ represents the radar wavelength.

7. The flood inundation mapping method of claim 5, wherein, The fluctuation increment is: ， wherein, represents a fluctuation increment, represents a mean value of water level fluctuation, and h represents a unit water level fluctuation value; represents a backscattering intensity corresponding to a unit water level fluctuation value; represents a standard backscattering intensity.

8. A flood inundation mapping system, comprising: The method comprises the following steps: a basic inundation range updating module is configured to determine a new basic inundation range according to a real-time SAR image and water surface fluctuation data when a new real-time SAR image is acquired; a water body increment determination module is configured to determine a current real-time water body volume according to the new basic inundation range and a terrain digital elevation model, and to determine an actual water body increment according to the current real-time water body volume and a previous real-time water body volume. a dynamic coefficient updating module, configured to update a dynamic coefficient according to an actual water body increment and a precipitation in a revisit period, the revisit period being a period of acquiring the SAR image; a water body increment predicting module, configured to determine a predicted water body increment according to the predicted precipitation and the updated dynamic coefficient; a flood inundation predicting module, configured to determine a flood inundation prediction range according to the predicted water body increment and a digital terrain model, with a new basic inundation range as a starting inundation range.

9. An electronic device, comprising: comprising a memory and a processor; the memory, configured to store a computer program; the processor, configured to implement the flood inundation range prediction method according to any one of claims 1 to 7 when the computer program is executed.

10. A computer-readable storage medium, characterized in that, the storage medium has a computer program stored thereon, and the computer program, when executed by a processor, implements the flood inundation range prediction method according to any one of claims 1 to 7.

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