An integrated measurement method for water depth distribution and three-dimensional surface velocity field in urban flooded areas

Abstract

The present invention belongs to the field of fluid measurement technology and proposes an integrated measurement method for water depth distribution and three-dimensional surface velocity field in urban flooded areas. The method comprises: using two cameras to capture the dynamic water surface of flood propagation in the flooded area from different perspectives, extracting, matching and removing errors from images captured by the two cameras at time t and time t+1; identifying tracer particles in the images at time t and time t+1, matching the tracer particles in the images at time t and time t+1 respectively by using coordinate fitting and a matching probability method, obtaining the three-dimensional coordinates of the feature points and the tracer particles at time t and time t+1 respectively, and jointly constituting the water depth distribution at time t and time t+1; and calculating the three-dimensional velocity distribution of the tracer particles at time t by combining the three-dimensional coordinates of the tracer particles at time t and time t+1 and the identification and matching of the tracer particles at time t and time t+1 by the same camera, thereby realizing the integrated measurement of water depth distribution and three-dimensional surface velocity field in urban flooded areas.

Description

Technical Field

[0001] The present invention relates to the field of fluid measurement technology, and in particular to a method for integrating water depth distribution and three-dimensional surface velocity field measurement in urban flooded areas. Background Art

[0002] At present, the main methods for monitoring water levels in urban flood-prone areas are: (1) water gauges. This method relies entirely on manual single-point water level monitoring, which has low accuracy and low efficiency; (2) various water level sensors, such as bubble water level meters, ultrasonic water level meters, radar water level meters, laser water level meters, electronic water gauges, etc. These sensors can only realize single-point water level monitoring and are easily affected by external conditions (such as temperature and humidity). When the water level changes greatly, the measurement accuracy decreases; (3) image processing and machine learning. At present, this type of method can still only measure water levels at a single point, and water level monitoring is heavily dependent on water gauges or other water level markers. It is impossible to monitor the water depth distribution of the entire flow area. At the same time, there are strict requirements on the quality of image shooting and the data set for training neural networks, and the accuracy and stability cannot be guaranteed.

[0003] At present, the main methods for monitoring surface velocity in urban flood areas are: (1) floating objects or debris on the water surface, which are mainly used to manually measure the surface velocity of flood water during floods, with low accuracy and low efficiency; (2) various types of velocity sensors, such as radio wave velocity meters, ADV (Acoustic Doppler Velocimetry), etc., which can only monitor the flow velocity at a single point, and the measurement accuracy of the instrument is easily affected by the environment; (3) PIV (Particle Image Velocimetry) / PTV (Particle Tracking Velocimetry) methods, which are currently commonly used to carry out two-dimensional surface velocity measurements, and are rarely used for three-dimensional velocity monitoring in the context of urban floods. Summary of the Invention

[0004] The purpose of the present invention is to overcome the deficiencies of existing urban flood area water regime monitoring technology or measurement equipment and to provide an integrated measurement method for urban flood area water depth distribution and three-dimensional surface velocity field.

[0005] The present invention solves the technical problem and adopts the following technical solution:

[0006] The method for measuring the integrated water depth distribution and three-dimensional surface velocity field in urban flooded areas includes the following steps:

[0007] Prepare a water tank, urban building model blocks, calibration plates, two cameras, and tracer particles, and place gates in the water tank to control flooding in the flood-prone area.

[0008] Install two cameras at different locations according to environmental conditions and ensure that the shooting areas of both cameras cover the required shooting range;

[0009] The urban building model blocks were installed in the water tank, and the camera calibration was carried out using a calibration plate. Tracer particles were pre-seeded in the shooting area and continuously supplied during the flood evolution process.

[0010] Two cameras are used to capture the dynamic water surface of floodwaters in flood-prone areas from different perspectives. Feature points are extracted, matched, and error-corrected for the images taken by the two cameras at time t and time t+1.

[0011] The tracer particles in the images at time t and time t+1 are identified respectively. At the same time, the tracer particles in the images at time t and time t+1 are matched respectively by combining coordinate fitting and matching probability method. The three-dimensional coordinates of the feature points and tracer particles at time t and time t+1 are obtained respectively based on the camera calibration results, thereby jointly constituting the water depth distribution at time t and time t+1.

[0012] The three-dimensional coordinates of the tracer particles at time t and time t+1 and the identification and matching of the tracer particles at time t and time t+1 by the same camera are combined to calculate the three-dimensional velocity distribution of the tracer particles at time t, realizing the integrated measurement of the water depth distribution and three-dimensional surface velocity field in urban flood areas.

[0013] As a further optimization, the camera calibration work is carried out through the calibration board, and the method is as follows:

[0014] Use the camera to take pictures of the black and white grid calibration plate in different orientations, and use the calibration program and the taken pictures to calculate the intrinsic parameter matrices K1 and K2 of camera 1 and camera 2 and the extrinsic parameter matrices P1 and P2 corresponding to the same world coordinate system.

[0015] As a further optimization, the method for extracting and matching feature points of the images taken by the two cameras at time t is as follows:

[0016] The scale-invariant feature transformation method is used to extract and match the feature points of the images taken at time t by camera 1 and camera 2, and the coordinates of the matching feature points (u1, v1) and (u2, v2) of camera 1 and camera 2 are obtained respectively.

[0017] As a further optimization, the method for removing feature points from the images taken by the two cameras at time t is as follows:

[0018] Assume that the same point in camera 1 and camera 2 satisfies the following relationship:

[0019]

[0020] The H matrix is ​​solved by the random sampling consistency check method, and the matching feature points are checked using the H matrix. The matching points that meet the H matrix are retained, and those that do not meet the H matrix are eliminated.

[0021] As a further optimization, the method for identifying the tracer particles in the image at time t is:

[0022] The RGB images taken by camera 1 and camera 2 at time t are converted into YCRCB images. In the YCRCB mode, particles are identified by the size of their color components, and then the coordinates of the particle center are determined using the circular object recognition method.

[0023] As a further optimization, the method for matching the tracer particles in the image at time t is:

[0024] After obtaining the coordinates of the feature points (u1, v1) and (u2, v2) in the images of camera 1 and camera 2 at time t, polynomial fitting is performed on the relationship between u1 and u2, and between v1 and v2, and the coordinates of the tracer particles identified in camera 1 (u1, v1) are calculated using the fitted relationship. 1p ,v 1p ) is transformed, and the transformed particle coordinates (u1′ p ,v1′ p ) and the particle coordinates in camera 2 (u 2p ,v 2p ) Matching is performed using the matching probability method.

[0025] As a further optimization, the method for obtaining the three-dimensional coordinates of the feature points and tracer particles at time t based on the camera calibration results is:

[0026] In a dual-camera system, the relationship between the object point and the two cameras is expressed as follows:

[0027]

[0028]

[0029] The matrix M composed of the parameters m is obtained by the intrinsic parameter matrices K1 and K2 and the extrinsic parameter matrices P1 and P2 obtained in the previous camera calibration work, that is, M1 = K1*P1, M2 = K2*P2;

[0030] Combining the above matrices, we get the following system of equations:

[0031]

[0032] In the above equations, only the three-dimensional coordinates of the matching points (X w , Y w , Z w ) is an unknown number. By solving the above equations, the three-dimensional coordinates of the characteristic points and tracer particles at time t are obtained.

[0033] As a further optimization, the method for calculating the three-dimensional flow velocity distribution of the tracer particles at time t by combining the three-dimensional coordinates of the tracer particles at time t and time t+1 and the identification and matching of the tracer particles at time t and time t+1 by the same camera is as follows:

[0034] After converting the RGB images at time t and time t+1 taken by camera 1 or camera 2 into YCRCB images, the particles are identified based on their color components. The coordinates of the particle center are determined using the circular object recognition method, and the tracer particles at time t and time t+1 are matched using the matching probability method. The three-dimensional coordinates of the tracer particles at time t and time t+1 and the corresponding time difference can be combined to calculate the three-dimensional flow rate of the tracer particles.

[0035] As a further optimization, the water in the tank was dyed milky white with titanium dioxide, and the tracer particles were red polypropylene spheres with a diameter of 10 mm and a density of 0.92 g / cm 3 .

[0036] The beneficial effects of the present invention are: through the above-mentioned integrated measurement method of water depth distribution and three-dimensional surface velocity field in urban flood areas, integrated monitoring of water depth distribution and three-dimensional surface velocity field in urban flood areas can be carried out, which not only solves the previous limitation of only being able to monitor water level or flow velocity at a single point, but also can carry out water level and flow velocity monitoring at the same time, and at the same time expands surface flow velocity monitoring from two dimensions to three dimensions. For the complex water flow movement monitoring under the background of urban floods, the present invention improves the efficiency of measuring relevant hydraulic parameters, enriches the monitoring information of hydraulic parameters, and greatly reduces manpower, material and financial costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 Flowchart of a method for integrating water depth distribution and three-dimensional surface velocity field measurement in urban flooded areas according to an embodiment of the present invention;

[0038] Figure 2 Schematic diagram of an experimental device in an embodiment of the present invention;

[0039] Figure 3 Schematic diagram of tracer particles in an embodiment of the present invention;

[0040] Figure 4 Schematic diagram of a calibration plate in an embodiment of the present invention;

[0041] Figure 5 is the original picture taken by the camera 1 at t=2s in the embodiment of the present invention;

[0042] Figure 6 is the original picture taken by the camera 2 at t=2s in the embodiment of the present invention;

[0043] Figure 7is the final matching result of the feature points of camera 1 and camera 2 at t=2s in the embodiment of the present invention;

[0044] Figure 8 The result after the tracer particles of camera 1 and camera 2 are matched at t=2s in the embodiment of the present invention;

[0045] Figure 9 The water depth distribution result at t=2s in the embodiment of the present invention;

[0046] Figure 10 This is the integrated restoration result of the water depth distribution and the three-dimensional surface velocity field at t=2s in the embodiment of the present invention. DETAILED DESCRIPTION

[0047] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0048] Example

[0049] This embodiment provides a method for measuring the integrated water depth distribution and three-dimensional surface velocity field in urban flood areas. Figure 1 , wherein the method comprises the following steps:

[0050] S1. Prepare a water flume, a model of a city building, a calibration plate, two cameras, and tracer particles. Place gates in the flume to control flooding in the floodplain.

[0051] S2. Install the two cameras at different locations according to environmental conditions and ensure that the shooting areas of both cameras cover the required shooting range;

[0052] S3. Install the urban building model block in the water tank, perform camera calibration using a calibration plate, and pre-seed tracer particles in the capture area. Tracer particles are continuously supplied during the flooding process.

[0053] S4. Use two cameras to capture the dynamic water surface of the flood zone from different perspectives. Perform feature point extraction, matching, and error correction on the images captured by the two cameras at time t and time t+1, respectively.

[0054] S5. Identify the tracer particles in the images at time t and time t+1, and match the tracer particles in the images at time t and time t+1 by combining coordinate fitting and matching probability method. Then, obtain the three-dimensional coordinates of the feature points and tracer particles at time t and time t+1 based on the camera calibration results, and thus jointly construct the water depth distribution at time t and time t+1;

[0055] S6. Combine the three-dimensional coordinates of the tracer particles at time t and time t+1 and the identification and matching of the tracer particles at time t and time t+1 by the same camera to calculate the three-dimensional velocity distribution of the tracer particles at time t, thereby realizing the integrated measurement of the water depth distribution and three-dimensional surface velocity field in urban flood areas.

[0056] In this embodiment, the evolution of flood in the flood-prone area can be photographed by two cameras with different shooting angles, and the camera 1 and the camera 2 are calibrated by a black and white grid calibration plate and a calibration program in MATLAB, thereby obtaining the intrinsic parameter matrices K1 and K2 of the camera 1 and the camera 2 and the extrinsic parameter matrices P1 and P2 corresponding to the same world coordinate system. Then, the feature points of the pictures taken by the cameras 1 and the camera 2 at time t are selected and matched by the scale-invariant feature transformation method, and the feature points that are incorrectly matched are eliminated by the random sampling consistency test method. Based on the coordinates (u1, v1) and (u2, v2) of the feature points in the pictures of the cameras 1 and the camera 2 at time t after eliminating the erroneous points, polynomial fitting is performed on the relationships between u1 and u2, and between v1 and v2, respectively. The coordinates (u1, v1) of the tracer particles identified in the camera 1 are obtained by the fitted relationship. 1p ,v 1p ) is transformed, and the transformed particle coordinates (u1′ p ,v1′ p ) and the particle coordinates in camera 2 (u 2p ,v 2p ) using the matching probability method. The three-dimensional coordinates of the feature points and tracer particles at time t are then calculated using the matching information of the feature points and tracer particles and the camera calibration parameters. This ultimately forms the flood depth distribution at time t. The flood depth distribution at time t+1 is obtained using the same method.

[0057] After calculating the three-dimensional coordinates of the tracer particles at time t and time t+1, the tracer particles in the images taken by camera 1 or camera 2 at time t and time t+1 are identified and matched. After matching, the three-dimensional flow velocity of the particles is calculated based on the three-dimensional coordinate information of the tracer particles. Finally, the water depth distribution and three-dimensional surface flow velocity data of the flooded area at time t are obtained.

[0058] In this embodiment, the experimental device provided is as shown in the attached Figure 2As shown, the experimental water tank consists of a reservoir area, a downstream flood evolution area, buildings in the flood evolution area, and a flood control gate in the middle. The water tank is 22.8 m long, 1 m wide, and 1.09 m high. The water tank is constructed of tempered glass. To enhance the accuracy and stability of the measurement method described in this embodiment, it is necessary to have a strong color difference between the flood, the tracer particles, and the flood movement background. During the experiment, the water body was dyed with titanium dioxide to make it an opaque white liquid. The tracer particles were red polypropylene spheres with a diameter of 10 mm and a density of 0.92 g / cm 3 , as attached Figure 3 As shown, the side wall of the water tank is also treated with a black smooth car film. Adjust the shooting angle and position of camera 1 and camera 2 according to the measurement range to be captured. The camera used in this embodiment is a Nikon D7500, with a shooting resolution of 1920×1080 and a shooting frame rate of 60 frames / s. Then make a black and white grid calibration plate. The size of the calibration plate and the size of the internal grid can be selected according to the experimental conditions. The calibration plate in this embodiment is 600mm×500mm in size, with an internal grid size of 100mm. The calibration plate photo is shown in the attached figure. Figure 4 shown.

[0059] After preparing the above materials, begin camera calibration. Move the calibration plate within the camera's field of view and capture approximately 20 images containing the calibration plate. Then, use the calibration program in MATLAB to calibrate the cameras, saving the intrinsic parameter matrices K1 and K2 for Camera 1 and Camera 2, as well as the extrinsic parameter matrices P1 and P2 corresponding to the same world coordinate system. Next, accumulate water to a certain depth in a tank and seed the surface with pre-prepared tracer particles, ensuring that the tracer particles are distributed as evenly as possible. Turn on Camera 1 and Camera 2 to capture the scene in advance. Then, open the gates in the tank to simulate the flood propagation process during urban flooding. After recording the entire flood propagation process, turn off Camera 1 and Camera 2.

[0060] Here, the water surface at t = 2s is selected for analysis, where the original images taken by camera 1 and camera 2 are shown in the attached figure. Figure 5 and attached Figure 6 As shown in the attached figure, the matching results after extracting the image feature points and removing the wrong matching points are shown in the attached figure. Figure 7 As shown in the attached figure, the tracer particles in the images of camera 1 and camera 2 are matched by coordinate fitting and matching probability method. Figure 8 The final water depth distribution is shown in the attached Figure 9 As shown in the figure, by matching the probability method and the previously calculated three-dimensional coordinates of the tracer particles, the final calculated three-dimensional velocity of the tracer particles is superimposed on the water depth distribution. Figure 10 shown.

[0061] It should be pointed out that here, the camera calibration work is carried out through the calibration plate. The method is: use the camera to take pictures of the black and white grid calibration plate in different orientations, and take about 20 pictures. Then use the calibration program and the taken pictures to calculate the intrinsic parameter matrices K1 and K2 of camera 1 and camera 2 and the extrinsic parameter matrices P1 and P2 corresponding to the same world coordinate system.

[0062] The method for extracting and matching feature points of the images taken by the two cameras at time t is as follows:

[0063] The scale-invariant feature transformation method is used to extract and match the feature points of the images taken at time t by camera 1 and camera 2, and the coordinates of the matching feature points (u1, v1) and (u2, v2) of camera 1 and camera 2 are obtained respectively.

[0064] In this embodiment, the method for performing feature point error removal on the images taken by two cameras at time t is as follows:

[0065] Assume that the same point in camera 1 and camera 2 satisfies the following relationship:

[0066]

[0067] Then, the H matrix is ​​solved by the random sampling consistency check method, and the matching feature points are checked using the H matrix. The matching points that meet the H matrix are retained, and those that do not are eliminated.

[0068] It should be added that the method for identifying the tracer particles in the image at time t is:

[0069] The RGB images taken by camera 1 and camera 2 at time t are converted into YCRCB images. In the YCRCB mode, particles are identified by the size of their color components, and then the coordinates of the particle center are determined using the circular object recognition method.

[0070] In addition, the method for matching the tracer particles in the image at time t is:

[0071] After obtaining the coordinates of the feature points (u1, v1) and (u2, v2) in the images of camera 1 and camera 2 at time t, polynomial fitting is performed on the relationship between u1 and u2, and between v1 and v2, and the coordinates of the tracer particles identified in camera 1 (u1, v1) are calculated using the fitted relationship. 1p ,v 1p ) is transformed, and the transformed particle coordinates (u1′ p ,v1′ p ) and the particle coordinates in camera 2 (u 2p ,v 2p ) Matching is performed using the matching probability method.

[0072] The method for obtaining the three-dimensional coordinates of the feature points and tracer particles at time t based on the camera calibration results is:

[0073] In a dual-camera system, the relationship between the object point and the two cameras is expressed as follows:

[0074]

[0075]

[0076] The matrix M composed of the parameters m can be obtained by the intrinsic parameter matrices K1 and K2 and the extrinsic parameter matrices P1 and P2 obtained in the previous camera calibration work, that is, M1 = K1*P1, M2 = K2*P2;

[0077] Combining the above matrices, we get the following system of equations:

[0078]

[0079] In the above equations, only the three-dimensional coordinates of the matching points (X w , Y w , Z w ) is an unknown number. By solving the above equations, the three-dimensional coordinates of the characteristic points and tracer particles at time t are obtained.

[0080] It should be noted that the method for calculating the three-dimensional flow velocity distribution of the tracer particles at time t by combining the three-dimensional coordinates of the tracer particles at time t and time t+1 and the identification and matching of the tracer particles at time t and time t+1 by the same camera is:

[0081] After converting the RGB images at time t and time t+1 taken by camera 1 or camera 2 into YCRCB images, the particles are identified based on their color components. The coordinates of the particle center are determined using the circular object recognition method, and the tracer particles at time t and time t+1 are matched using the matching probability method. The three-dimensional coordinates of the tracer particles at time t and time t+1 and the corresponding time difference can be combined to calculate the three-dimensional flow rate of the tracer particles.

[0082] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for measuring the integrated water depth distribution and three-dimensional surface velocity field in urban flooded areas, characterized in that: The steps include: Prepare a water tank, urban building model blocks, calibration plates, two cameras, and tracer particles, and place gates in the water tank to control flooding in the flood-prone area. Install two cameras at different locations according to environmental conditions and ensure that the shooting areas of both cameras cover the required shooting range; The urban building model blocks were installed in the water tank, and the camera calibration was carried out using a calibration plate. Tracer particles were pre-seeded in the shooting area and continuously supplied during the flood evolution process. Two cameras are used to capture the dynamic water surface of floodwaters in flood-prone areas from different perspectives. Feature points are extracted, matched, and error-corrected for the images taken by the two cameras at time t and time t+1. The tracer particles in the images at time t and time t+1 are identified respectively. At the same time, the tracer particles in the images at time t and time t+1 are matched respectively by combining coordinate fitting and matching probability method. The three-dimensional coordinates of the feature points and tracer particles at time t and time t+1 are obtained respectively based on the camera calibration results, thereby jointly constituting the water depth distribution at time t and time t+1. The three-dimensional coordinates of the tracer particles at time t and time t+1 are combined with the identification and matching of the tracer particles at time t and time t+1 by the same camera to calculate the three-dimensional velocity distribution of the tracer particles at time t, thus realizing the integrated measurement of the water depth distribution and three-dimensional surface velocity field in urban flood areas. The method for carrying out camera calibration through the calibration board is as follows: Use the camera to take pictures of the black and white grid calibration plate in different orientations, and use the calibration program and the taken pictures to calculate the intrinsic parameter matrices K1 and K2 of camera 1 and camera 2 and the extrinsic parameter matrices P1 and P2 corresponding to the same world coordinate system; The method for calculating the three-dimensional flow velocity distribution of the tracer particles at time t by combining the three-dimensional coordinates of the tracer particles at time t and time t+1 and the identification and matching of the tracer particles at time t and time t+1 by the same camera is as follows: After converting the RGB images at time t and time t+1 taken by camera 1 or camera 2 into YCRCB images, the particles are identified based on their color components. The coordinates of the particle center are determined using the circular object recognition method, and the tracer particles at time t and time t+1 are matched using the matching probability method. The three-dimensional coordinates of the tracer particles at time t and time t+1 and the corresponding time difference can be combined to calculate the three-dimensional flow rate of the tracer particles.

2. The method for measuring the integrated water depth distribution and three-dimensional surface velocity field in urban flooded areas according to claim 1 is characterized in that: The method for extracting and matching feature points of the images taken by two cameras at time t is as follows: The scale-invariant feature transformation method is used to extract and match the feature points of the images taken at time t by camera 1 and camera 2, and the coordinates of the matching feature points (u1, v1) and (u2, v2) of camera 1 and camera 2 are obtained respectively.

3. The method for measuring the integrated water depth distribution and three-dimensional surface velocity field of urban flooded areas according to claim 2 is characterized in that: The method for performing feature point error removal on the images taken by two cameras at time t is as follows: Assume that the same point in camera 1 and camera 2 satisfies the following relationship: The H matrix is ​​solved by the random sampling consistency test method, and the matching feature points are tested using the H matrix. The matching points that meet the H matrix are retained, and those that do not meet the H matrix are eliminated.

4. The method for measuring the integrated water depth distribution and three-dimensional surface velocity field in urban flooded areas according to claim 1 is characterized in that: The method for identifying the tracer particles in the image at time t is: The RGB images taken by camera 1 and camera 2 at time t are converted into Ycrcb images. In the Ycrcb mode, particles are identified by the size of their color components, and then the coordinates of the particle center are determined using the circular object recognition method.

5. The method for measuring the integrated water depth distribution and three-dimensional surface velocity field of urban flooded areas according to claim 1 is characterized in that: The method for matching the tracer particles in the image at time t is: After obtaining the coordinates of the feature points (u1, v1) and (u2, v2) in the images of camera 1 and camera 2 at time t, polynomial fitting is performed on the relationship between u1 and u2, and between v1 and v2, and the coordinates of the tracer particles identified in camera 1 (u1, v1) are calculated using the fitted relationship. 1p ,v 1p ) is transformed, and the transformed particle coordinates (u′ 1p ,v′ 1p ) and the particle coordinates in camera 2 (u 2p ,v 2p ) Matching is performed using the matching probability method.

6. The method for measuring the integrated water depth distribution and three-dimensional surface velocity field of urban flooded areas according to claim 1 is characterized in that: The method for obtaining the three-dimensional coordinates of the feature points and tracer particles at time t based on the camera calibration results is: In a dual-camera system, the relationship between the object point and the two cameras is expressed as follows: The matrix M composed of the parameters m is obtained by the intrinsic parameter matrices K1 and K2 and the extrinsic parameter matrices P1 and P2 obtained in the previous camera calibration work, that is, M1 = K1*P1, M2 = K2*P2; Combining the above matrices, we get the following system of equations: In the above equations, only the three-dimensional coordinates of the matching points (X w , Y w , Z w ) is an unknown number. By solving the above equations, the three-dimensional coordinates of the characteristic points and tracer particles at time t are obtained.

7. The method for measuring the integrated water depth distribution and three-dimensional surface velocity field in urban flooded areas according to claim 1 is characterized in that: The water in the tank was dyed milky white with titanium dioxide, and the tracer particles were red polypropylene spheres with a diameter of 10 mm and a density of 0.92 g / cm 3 .

Images