Flood inundation range prediction method and system, electronic equipment and storage medium

Abstract

The invention provides a flood inundation range prediction method and system, electronic equipment and a storage medium, and relates to the technical field of data processing. The method comprises the steps that when a new real-time SAR image is acquired, a new basic submerging range is determined according to the real-time SAR image and water surface fluctuation data; according to the new foundation submerging range and the terrain digital elevation model, the current real-time water body amount is determined; determining an actual water body increment according to the current real-time water body quantity and the previous real-time water body quantity; updating the dynamic coefficient according to the actual water increment and the precipitation in the revisit period; determining a predicted water increment according to the obtained predicted precipitation and the updated dynamic coefficient; and determining a flood inundation prediction range by taking the new basic inundation range as an initial inundation range according to the predicted water increment and the terrain digital elevation model. The evolution of the flood inundation range can be predicted in time in the revisit period, the timeliness and accuracy of prediction are considered, and early warning is carried out on the affected area 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 floods 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, due to the limitation of the revisit period of the SAR images, the flood inundation area cannot be monitored within the revisit period, 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: 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; A current real-time water body volume is determined according to the new basic inundation area 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 within a revisit period, the revisit period being a period for obtaining SAR images; A predicted water body increment is determined according to the obtained predicted precipitation 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 area as a starting inundation area.

[0006] Optionally, the water body increment Wherein, A represents the dynamic coefficient, and P represents the precipitation.

[0007] Optionally, when the new real-time SAR image is obtained, the new basic inundation area is determined according to the real-time SAR image and the water surface fluctuation data, comprising: 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; 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; According to the water level fluctuation mean value and the real-time SAR image, a new basic inundation range is determined.

[0008] Optionally, the determining of the new basic inundation range according to the water level fluctuation mean value and the real-time SAR image comprises: According to the real-time SAR image and a preset threshold, a first water body recognition area is determined; According to the water level fluctuation mean value and a unit water level fluctuation value, a fluctuation increment is determined; According to the backscattering intensity of the real-time SAR image and the fluctuation increment, a corrected image is obtained; According to the corrected image and the preset threshold, a second water body recognition area is determined; The first water body recognition area and the second water body recognition area are superimposed and fused to obtain a water body coverage area; According to the water body coverage area, the new basic inundation range is determined.

[0009] Optionally, the determining of the fluctuation increment according to the water level fluctuation mean value and the unit water level fluctuation value comprises: According to the unit water level fluctuation value, a radar incidence 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; According to the water level fluctuation mean value and the unit water level fluctuation value, a fluctuation multiple is determined; 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; According to the backscattering intensity corresponding to the water level fluctuation mean value and a standard backscattering intensity, the fluctuation increment is determined.

[0010] Optionally, 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 a water surface roughness spectrum; R represents a reflection coefficient; represents a dielectric constant of a natural water body; θ represents a radar incidence angle; represents the wave number; h represents the unit water level fluctuation value; λ represents the radar wavelength.

[0011] Optionally, the fluctuation increment is: , in, represents the fluctuation increment, represents the mean value of water level fluctuation, and h represents the unit water level fluctuation value; Indicates the backscattering intensity corresponding to the unit water level fluctuation value; Indicates the standard backscatter intensity.

[0012] In a second aspect, the present invention further provides a flood inundation range prediction system, comprising: A basic flooding range updating module is used to determine a new basic flooding range based on the real-time SAR image and water surface fluctuation data when a new real-time SAR image is acquired; A water increment determination module is used to determine the current real-time water volume based on the new basic inundation range and the terrain digital elevation model; and is also used to determine the actual water increment based on the current real-time water volume and the previous real-time water volume; A dynamic coefficient updating module is used to update the dynamic coefficient according to the actual water body increment and the precipitation in the revisit period, wherein the revisit period is the period for acquiring SAR images; A water body increment prediction module is used to determine the predicted water body increment based on the obtained predicted precipitation and the updated dynamic coefficient; The flood inundation prediction module is used to determine the flood inundation prediction range based on the new basic inundation range as the starting inundation range and the predicted water body increment and terrain digital elevation model.

[0013] In a third aspect, the present invention provides an electronic device comprising a memory and a processor; The memory is used to store computer programs; The processor is configured to implement the flood inundation range prediction method as described in the first aspect when executing the computer program.

[0014] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the flood inundation range prediction method as described in the first aspect is implemented.

[0015] The present invention provides a flood inundation range prediction method, system, electronic device, and storage medium. Compared with the existing technology, it has the following advantages: According to the newly acquired real-time SAR image and water surface fluctuation data, a new basic submerged range is determined, the basic submerged range is located in the digital terrain model, and the current real-time water volume under the basic submerged range can be calculated according to the data of the digital terrain model; according to the current real-time water volume and the previous real-time water volume, the actual water volume increment can be determined; the precipitation in the revisit period is counted, and the dynamic coefficient needs to be dynamically updated with the increase of the precipitation and the precipitation time, so that the predicted water volume increment obtained by the subsequent predicted precipitation in the meteorological data and the real-time updated dynamic data is more accurate, and on the basis of the starting submerged range, a new flood submerged range is searched in the digital terrain model, until the hollow volume between the new flood submerged range and the starting submerged range is consistent with the predicted water volume increment, the flood submerged prediction range is determined, so that the flood submerged range can be more accurately predicted in the revisit period, the evolution of the flood submerged range is predicted in time, 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 is reduced. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0017] Figure 1 A flowchart of a flood submerged range prediction method provided by the embodiment of the present application is shown in the figure. Figure 2 An analysis flowchart of a new basic submerged range provided by the embodiment of the present application is shown in the figure. Figure 3 A structure diagram of a flood submerged range prediction system provided by the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical scheme and advantages of the embodiments of the present application more clear, the technical scheme in the embodiments of the present application is described clearly and completely. Obviously, the described embodiments are some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0019] In order to better understand the above technical solutions, the above technical solutions will be described in detail in combination with the drawings in the specification and specific embodiments.

[0020] As Figure 1As shown, the flood inundation range prediction method provided by the embodiment of the application comprises: S1: 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.

[0021] S2: determining a current real-time water body volume according to the new basic inundation range and a terrain digital elevation model.

[0022] S3: determining an actual water body increment according to the current real-time water body volume and a previous real-time water body volume.

[0023] S4: updating a dynamic coefficient 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.

[0024] S5: determining a predicted water body increment according to the acquired predicted precipitation amount and the updated dynamic coefficient.

[0025] S6: taking the new basic inundation range as a starting inundation range, and determining a flood inundation prediction range according to the predicted water body increment and the terrain digital elevation model.

[0026] 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 increases, 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.

[0027] The following will describe each step in detail.

[0028] 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.

[0029] 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.

[0030] 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 0.5m or 1m, etc.

[0031] SAR is a kind of active microwave radar, which actively emits microwaves through the radar antenna, receives the echo signals reflected by the ground objects to generate images. 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 produce fluctuations, resulting in changes in the roughness of the water surface, 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 of water surface fluctuation is not big. 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 point as the starting point can be intercepted. The data in the time window is most similar to the water surface fluctuation corresponding to the real-time SAR image at the time of obtaining the real-time SAR image. 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 on the time axis in chronological order, 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.

[0032] 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.

[0033] 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, the average value is taken to obtain the water level peak-valley difference average value, which is the water level fluctuation average value. The value is the water surface fluctuation caused by the strong wind blowing the water surface or the water surface flowing. The water level fluctuation average value represents the degree of water surface fluctuation.

[0034] 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

[0035] S131: According to the real-time SAR image and a preset threshold, a first water body identification area is determined.

[0036] ​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 using a threshold segmentation method, the water body and the non-water body are determined by the backscattering intensity and a preset threshold. For example, the preset threshold is set to-15 dB. The area with a backscattering intensity greater than-15 dB is determined as a non-water body, and the area with a backscattering intensity less than or equal to-15 dB is determined as a water body. Accordingly, the first water body identification area in the real-time SAR image can be determined.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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; and θ represents the radar incidence angle. represents the wave number; h represents the unit water level fluctuation value; and λ represents the radar wavelength.

[0041] According to the water level fluctuation mean value and the unit water level fluctuation value, a fluctuation multiple is determined.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] The fluctuation increment is: , Wherein, represents the fluctuation increment, represents the water level fluctuation average, and h represents a unit water level fluctuation value; represents the backscattering intensity corresponding to a unit water level fluctuation value; represents the standard backscattering intensity.

[0046] 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 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.

[0047] S133: Obtain a corrected image according to the backscattering intensity of the real-time SAR image and the fluctuation increment.

[0048] 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.

[0049] S134: Determine a second water body recognition region according to the corrected image and a preset threshold.

[0050] Specifically, according to the backscattering intensity in the modified image and a preset threshold, a second water body recognition region is obtained. Wherein, the region with the backscattering intensity less than or equal to the preset threshold is determined as the water body, and constitutes the second water body recognition region.

[0051] S135: superimpose and fuse the first water body recognition region and the second water body recognition region to obtain a water body coverage region.

[0052] Specifically, when the first water body recognition region and the second water body recognition region are superimposed, it is equivalent to find the union of the two regions. Further, when there is a highlight region (i.e. a region with backscattering intensity greater than the preset threshold) inside the two water body regions, the area of the highlight region is calculated. If the area of the highlight region is less than a preset area, it means that the region may be a water body that has not been recognized or a non-water body region surrounded by the water body region, and the highlight region can be directly taken as the water body coverage region. When the area of the highlight region is greater than or equal to the preset area, it means that the region is a larger non-water body region surrounded by the water body region, and the boundary of the highlight region is separately outlined as a non-water body region. In addition, when part of the boundary of the two water body regions is not continuous, if the length of the non-continuous position is less than a preset length, the non-continuous position is directly connected. When the length of the non-continuous position is greater than or equal to the preset length, the non-continuous state is maintained, and then the edge burr of the water body coverage region is smoothed. The obtained water body coverage region takes into account both the first water body recognition region and the second water body recognition region. On the one hand, it can ensure that the recognized water body coverage region is based on the original natural water body position (i.e. the position of the first water body recognition region) for diffusion, avoiding the problem that the recognized water body coverage region deviates from the natural water body position and causes obvious errors in the recognition result. On the other hand, the second water body recognition region obtained after modification may be overcorrected, resulting in the loss of part of the water body. However, the lost part may be the region covered by the first water body recognition region. After the two regions are superimposed, the error caused by overcorrection can be reduced, and the accuracy of the recognition result can be improved.

[0053] S136: determine a new basic inundation range according to the water body coverage region.

[0054] Specifically, in order to better display the flood inundation range, the water body coverage region can be highlighted and the boundary of the water body coverage region can be outlined to better display the new basic inundation range.

[0055] S2: determine a current real-time water body volume according to the new basic inundation range and the digital terrain model.

[0056] 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.

[0057] 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.

[0058] 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.

[0059] Specifically, there is a positive correlation between the water body increment and the precipitation, 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 precipitation. With the increase of precipitation, the speed of ground water converging to natural water area or low-lying area increases, and the amount of convergence in unit time increases, because the ground soil has a certain water absorption capacity at the beginning of rainfall, but as the precipitation increases, 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 increases and submerges small obstacles on the ground or under the scouring of ground water and rainfall, and a converging channel is naturally formed on the ground, and the subsequent rainfall converges along the converging channel formed in the early stage, and the converging speed is improved. There may be other factors that affect the dynamic coefficient A, which is in a state of change. The dynamic coefficient A does not change much between two adjacent revisit periods, that is, the dynamic coefficient A has a certain continuity and inheritance. The actual water body increment 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, and the water body increment obtained has a certain error, but the error has been improved compared with the traditional method or the result estimated according to experience. The error is within an acceptable range in a short time, and the error is not cumulative. After a new SAR image is obtained again, the dynamic coefficient A will be updated again, and the cumulative error in the previous period is eliminated. That is, the cumulative error in a revisit period is limited, so a compensation term can be added in the fitted relationship to further improve wherein K is a compensation coefficient, and t is the time length after a new real-time SAR image is obtained. In a time period in which the precipitation increases or remains unchanged in 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 unit time, the compensation coefficient K takes a negative value, and K is a constant value. When updating the dynamic coefficient, the compensation term can be ignored, and A is updated only by using the 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.

[0060] S5: determining the predicted water body increment according to the obtained predicted precipitation and the updated dynamic coefficient.

[0061] Specifically, the predicted precipitation after a period of time can be directly obtained from the meteorological data, and the accuracy of short-term prediction is high, for example, the prediction within a few hours or within a day is relatively accurate, and the predicted water body increment calculated accordingly is also relatively accurate.

[0062] S6: taking the new basic submergence range as the initial submergence range, determining the flood submergence prediction range according to the predicted water body increment and the digital terrain model.

[0063] Specifically, based on the initial inundation range, a reference plane can be established with the horizontal plane where the initial inundation range is located as the starting plane. In the terrain digital elevation model, the reference plane is moved from the starting plane until the volume between the reference plane and the initial inundation range connected to the initial inundation range is equal to the value of the predicted water body increment divided by the water density, thereby determining that the area where the reference plane intersects with the terrain digital elevation model and is connected to the initial inundation range is the flood inundation prediction range.

[0064] like Figure 3 As shown, the embodiment of the present application provides a flood inundation range prediction system, including: The basic flooding range updating module 100 is used to determine a new basic flooding range according to the real-time SAR image and water surface fluctuation data when a new real-time SAR image is acquired.

[0065] The water volume increment determination module 200 is used to determine the current real-time water volume based on the new basic inundation range and the terrain digital elevation model; and is also used to determine the actual water volume increment based on the current real-time water volume and the previous real-time water volume.

[0066] The dynamic coefficient updating module 300 is used to update the dynamic coefficient according to the actual water body increment and the precipitation in the revisit period, where the revisit period is the period for acquiring SAR images.

[0067] The water body increment prediction module 400 is used to determine the predicted water body increment based on the obtained predicted precipitation and the updated dynamic coefficient.

[0068] The flood inundation prediction module 500 is used to determine the flood inundation prediction range based on the new basic inundation range as the starting inundation range and the predicted water body increment and the terrain digital elevation model.

[0069] In this embodiment, the beneficial effects of the flood inundation range prediction system are similar to the beneficial effects of the above-mentioned flood inundation range prediction method, and will not be repeated here.

[0070] An electronic device provided in an embodiment of the present application includes a memory and a processor; the memory is used to store a computer program; and the processor is used to implement the flood inundation range prediction method described above when executing the computer program.

[0071] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the flood inundation range prediction method described above is implemented.

[0072] In this embodiment, the beneficial effects of the electronic device and the computer-readable storage medium are similar to the beneficial effects of the above-mentioned flood inundation range prediction method, and are not repeated here.

[0073] 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 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.

[0074] 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.

[0075] Those of ordinary skill in the related art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing relevant hardware through a computer program, 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 embodiment methods. The storage medium can be a magnetic disc, an optical disc, a read-only memory (ROM), or a random access memory (RAM), etc. In the present application, the units described as separate components can or can not be physically separated, 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 according to actual needs to achieve the purpose of the present application embodiment scheme. In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0076] 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.

[0077] The above examples are merely used to illustrate the technical solutions of the present application, but not to limit it; even though 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 make modifications to the technical solutions recorded in the foregoing examples, or make equivalent replacements to some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A flood inundation range prediction method, characterized in that: include: When a new real-time SAR image is acquired, a new basic inundation range is determined based on the real-time SAR image and the water surface fluctuation data; Determine the current real-time water volume based on the new base inundation extent and terrain digital elevation model; Determine the actual water volume increment based on the current real-time water volume and the previous real-time water volume; updating the dynamic coefficient based on the actual water volume increment and the precipitation within the revisit period, wherein the revisit period is the period for acquiring SAR images; Determine the predicted water body increment based on the obtained predicted precipitation and the updated dynamic coefficient; Taking the new basic inundation range as the starting inundation range, the flood inundation prediction range is determined according to the predicted water body increment and the terrain digital elevation model.

2. The flood inundation range prediction method according to claim 1, wherein: Water body increase , where A represents the dynamic coefficient and P represents the precipitation.

3. The flood inundation range prediction method according to claim 1, wherein: When acquiring a new real-time SAR image, determining a new basic inundation range according to the real-time SAR image and the water surface fluctuation data includes: When a new real-time SAR image is acquired, based on the acquisition time point of the real-time SAR image, real-time water level data of a preset time length is intercepted, where the real-time water level data is the water level data collected by the water level sensor; Based on the intercepted real-time water level data, multiple water level peak-valley differences are obtained, and the mean water level fluctuation is determined; A new basic inundation range is determined based on the water level fluctuation mean and the real-time SAR image.

4. The flood inundation range prediction method according to claim 3, wherein: Determining a new basic flooding range based on the water level fluctuation mean and the real-time SAR image includes: Determining a first water body identification area based on the real-time SAR image and a preset threshold; Determine the fluctuation increment based on the mean value of water level fluctuation and the unit water level fluctuation value; Obtaining a corrected image according to the backscatter intensity and fluctuation increment of the real-time SAR image; determining a second water body identification area according to the corrected image and a preset threshold; Superimposing and fusing the first water body identification area and the second water body identification area to obtain a water body coverage area; Determine the new basic inundation range based on the water coverage area.

5. The flood inundation range prediction method according to claim 4, characterized in that: Determining the fluctuation increment according to the water level fluctuation mean and the unit water level fluctuation value includes: Obtaining a backscattering intensity corresponding to the unit water level fluctuation value according to the unit water level fluctuation value, the radar incident angle, the radar wavelength, the wave number, and the dielectric constant of the natural water body; Determining a fluctuation multiple based on the water level fluctuation mean and the unit water level fluctuation value; Determining the backscattering intensity corresponding to the mean water level fluctuation value according to the fluctuation multiple and the backscattering intensity corresponding to the unit water level fluctuation value; The fluctuation increment is determined based on the backscattering intensity corresponding to the mean water level fluctuation and the standard backscattering intensity.

6. The flood inundation range prediction method according to claim 5, characterized in that: The backscattering intensity corresponding to the unit water level fluctuation value is: ， ， ， in, Indicates 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 natural water; θ represents the radar incident angle; represents the wave number; h represents the unit water level fluctuation value; λ represents the radar wavelength.

7. The flood inundation range prediction method according to claim 5, characterized in that: The fluctuation increment is: ， in, represents the fluctuation increment, represents the mean value of water level fluctuation, and h represents the unit water level fluctuation value; Indicates the backscattering intensity corresponding to the unit water level fluctuation value; Represents the standard backscatter intensity.

8. A flood inundation range prediction system, characterized in that: include: A basic flooding range updating module is used to determine a new basic flooding range based on the real-time SAR image and water surface fluctuation data when a new real-time SAR image is acquired; A water increment determination module is used to determine the current real-time water volume based on the new basic inundation range and the terrain digital elevation model; and is also used to determine the actual water increment based on the current real-time water volume and the previous real-time water volume; A dynamic coefficient updating module is used to update the dynamic coefficient according to the actual water body increment and the precipitation in the revisit period, wherein the revisit period is the period for acquiring SAR images; A water body increment prediction module is used to determine the predicted water body increment based on the obtained predicted precipitation and the updated dynamic coefficient; The flood inundation prediction module is used to determine the flood inundation prediction range based on the new basic inundation range as the starting inundation range and the predicted water body increment and terrain digital elevation model.

9. An electronic device, characterized in that: including memory and processor; The memory is used to store computer programs; The processor is configured to implement the flood inundation range prediction method according to any one of claims 1 to 7 when executing the computer program.

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

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