Method and device for rapid determination of parameters of urban waterlogging model based on deep learning

Abstract

The application provides a kind of urban waterlogging model parameter quick determination method and device based on deep learning, processor constructs each research area storm waterlogging model, and each storm waterlogging model is rated and checked;Then construct deep learning model, train deep learning model by model input material data and the model parameter of storm waterlogging model;The input material data of target research area is input to the trained deep learning model, and the model parameter of the storm waterlogging model of target research area is obtained.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of machine learning, and particularly relates to a deep learning-based urban waterlogging model parameter rapid determination method and device. BACKGROUND

[0002] In the aspect of urban rainstorm waterlogging simulation, a physical mechanism model based on a traditional hydrological and hydrodynamic model has been widely applied to waterlogging risk assessment. The physical mechanism model has a clear physical mechanism and can provide accurate simulation results. However, the model parameters in the physical mechanism model depend on the method of manual debugging. The method of manually debugging model parameters is time-consuming and laborious, and is highly dependent on the experience and technology of the debugging personnel. The simulation accuracy of the model is affected, resulting in low timeliness and accuracy of the model simulation, which cannot meet the timeliness requirements of urban rainstorm waterlogging prediction and early warning. How to overcome the time-consuming problem of parameter calibration and verification of the physical mechanism model and improve the timeliness of simulation and prediction and early warning is a key problem to be solved. SUMMARY

[0003] The present application provides a deep learning-based urban waterlogging model parameter rapid determination method and device. The deep learning model is applied to the determination of the model parameters of the urban rainstorm waterlogging model with a clear physical mechanism. The model parameters of the urban rainstorm waterlogging model can be rapidly and accurately determined, thereby improving the accuracy and timeliness of urban rainstorm waterlogging early warning.

[0004] In a first aspect, the present application provides a deep learning-based urban waterlogging model parameter rapid determination method. The method comprises the following steps: obtaining underlying surface data, pipe network data, rainfall data and measured hydrological data of a plurality of research areas; constructing a rainstorm waterlogging model of each research area according to the underlying surface data, the pipe network data and the rainfall data; calibrating and verifying the corresponding rainstorm waterlogging model according to the rainfall data and the measured hydrological data of each research area, obtaining a model parameter database, wherein the model parameter database comprises optimized model parameters of each rainstorm waterlogging model; constructing a deep learning model, constructing an input data database according to the underlying surface data, the pipe network data and the rainfall data of each research area; training the deep learning model with the input data database as the independent variable and the model parameter database as the dependent variable; inputting the underlying surface data, the pipe network data and the rainfall data of a target research area into the trained deep learning model to obtain the model parameters of the rainstorm waterlogging model of the target research area.

[0005] In a second aspect, the present application provides a device for quickly determining parameters of an urban waterlogging model based on deep learning, comprising: an acquisition unit for acquiring underlying surface data, pipe network data, rainfall data and measured hydrological data of multiple study areas; a processing unit for constructing a rainstorm waterlogging model for each study area based on the underlying surface data, the pipe network data and the rainfall data; and, calibrating and verifying the corresponding rainstorm waterlogging model based on the rainfall data and measured hydrological data of each study area to obtain a model parameter database, wherein the model parameter database includes optimized model parameters of each rainstorm waterlogging model; and, constructing a deep learning model, constructing an input data database based on the underlying surface data, pipe network data and rainfall data of each study area; and, training the deep learning model using the input data database as an independent variable and the model parameter database as a dependent variable; and, inputting the underlying surface data, pipe network data and rainfall data of the target study area into the trained deep learning model to obtain model parameters of the rainstorm waterlogging model of the target study area.

[0006] In a third aspect, the present application provides an electronic device comprising a processor and a memory, wherein the memory stores a computer program, and when the processor calls the computer program in the memory, it executes the method as described in any one of the first aspects.

[0007] It can be seen that in an embodiment of the present application, the processor obtains the underlying surface data, pipe network data, rainfall data and measured hydrological data of multiple study areas, and constructs a rainstorm waterlogging model for each study area based on the underlying surface data, pipe network data and rainfall data; calibrates and verifies the corresponding rainstorm waterlogging model based on the rainfall data and measured hydrological data of each study area to obtain a model parameter database, which includes the optimized model parameters of each rainstorm waterlogging model; constructs a deep learning model, and constructs an input data database based on the underlying surface data, pipe network data and rainfall data of each study area; trains the deep learning model with the input data database as the independent variable and the model parameter database as the dependent variable; inputs the underlying surface data, pipe network data and rainfall data of the target study area into the trained deep learning model to obtain the model parameters of the rainstorm waterlogging model of the target study area. It can be seen that in this application, the deep learning model is applied to the parameter determination of the urban rainstorm waterlogging model with a clear physical mechanism. Compared with the traditional manual debugging method, the model parameters of the urban rainstorm waterlogging model can be quickly and accurately determined, thereby improving the accuracy and timeliness of urban rainstorm waterlogging warnings. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to make the technical scheme of the present application or the prior art clearer, the accompanying drawings needed in the description of the embodiments or the prior art will be briefly introduced. Obviously, the accompanying drawings in the description are only some embodiments of the present application, and those skilled in the art can obtain other accompanying drawings without creative effort based on these accompanying drawings.

[0009] Figure 1 A structural schematic diagram of an electronic device provided by an embodiment of the present application is shown in the figure.

[0010] Figure 2 A flowchart of a deep learning-based urban waterlogging model parameter rapid determination method provided by an embodiment of the present application is shown in the figure.

[0011] Figure 3 A comparison diagram of measured hydrological data and simulated hydrological data of a first rainstorm waterlogging model of a first research area provided by an embodiment of the present application is shown in the figure.

[0012] Figure 4 A functional unit block diagram of a deep learning-based urban waterlogging model parameter rapid determination device provided by an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0013] In order to make the technical scheme of the present application or the prior art clearer, the accompanying drawings needed in the description of the embodiments or the prior art will be briefly introduced. Obviously, the accompanying drawings in the description are only some embodiments of the present application, and those skilled in the art can obtain other accompanying drawings without creative effort based on these accompanying drawings.

[0014] The terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned accompanying drawings are used to distinguish different objects, and are not used to describe a specific order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units is not limited to the listed steps or units, but can optionally include steps or units not listed or can optionally include other steps or units inherent to the process, method, product or device.

[0015] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0016] In the embodiments of this application, "and / or" describes the relationship between associated objects and indicates that three relationships can exist. For example, "A and / or B" can represent the following three situations: A exists alone; A and B exist simultaneously; and B exists alone. A and B can be singular or plural.

[0017] In the embodiments of the present application, the symbol " / " can indicate that the preceding and following objects are in an "or" relationship. In addition, the symbol " / " can also represent a division sign, that is, performing a division operation. For example, A / B can mean A divided by B.

[0018] In the embodiments of the present application, "at least one item" or similar expressions refers to any combination of these items, including any combination of single items or plural items, and refers to one or more, and multiple refers to two or more. For example, at least one item (item) of a, b, or c can represent the following seven situations: a, b, c, a and b, a and c, b and c, a, b, and c. Among them, each of a, b, and c can be an element or a set containing one or more elements.

[0019] In the embodiments of this application, "equal to" can be used in conjunction with "greater than" and is applicable to the technical solution adopted when "greater than" is used, and can also be used in conjunction with "less than" and is applicable to the technical solution adopted when "less than" is used. When "equal to" is used in conjunction with "greater than", it should not be used in conjunction with "less than"; when "equal to" is used in conjunction with "less than", it should not be used in conjunction with "greater than".

[0020] In terms of urban rainstorm waterlogging simulation, it mainly includes physical mechanism models based on hydrodynamics and artificial intelligence technologies (such as machine learning and deep learning). The physical mechanism model has a clear physical mechanism and can provide accurate simulation results, but the calibration and verification of model parameters is time-consuming and cannot meet the timeliness requirements of urban rainstorm waterlogging prediction and warning; artificial intelligence technology has powerful data processing capabilities and high computing efficiency, but does not have a physical mechanism.

[0021] One solution is to combine artificial intelligence technology with physical mechanism models, and apply artificial intelligence technology to the determination of model parameters of the physical mechanism model of urban rainstorm waterlogging, so as to achieve rapid and accurate simulation of urban rainstorm waterlogging.

[0022] Based on the above ideas, this application provides a method and device for quickly determining the parameters of an urban waterlogging model based on deep learning. The deep learning model is applied to the parameter determination of an urban rainstorm waterlogging model with a clear physical mechanism, which can realize the rapid and accurate determination of the model parameters of the urban rainstorm waterlogging model, thereby improving the accuracy and timeliness of urban rainstorm waterlogging warnings.

[0023] The following specific embodiments describe in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0024] See also Figure 1 , Figure 1 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. For the sake of convenience, only the parts related to the embodiment of the present application are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present application. The electronic device 1 can be any terminal device including a mobile phone, computer, PDA, POS, car computer, etc. Figure 1 As shown, electronic device 1 includes memory 20, processor 10, communication bus 40, communication interface 30, and one or more programs 21. One or more programs 21 are stored in memory 20 and configured to be executed by processor 10. One or more programs 21 include instructions for executing any step in the following method embodiments. In specific implementations, processor 10 is used to execute any step in the following method embodiments, and when performing data transmission such as sending, it can optionally call communication interface 30 to complete the corresponding operation.

[0025] In the embodiment of the present application, the processor 10 included in the computer device may have the function corresponding to any method step in this embodiment.

[0026] Those skilled in the art will understand that Figure 1 The computer device structure shown in the figure does not constitute a limitation to the computer device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0027] See also Figure 2 , Figure 2 This is a flow chart of a method for quickly determining parameters of an urban waterlogging model based on deep learning provided in an embodiment of the present application. The method is applied to Figure 1 The processor 10 shown, the method includes:

[0028] In step S201, the processor obtains underlying surface data, pipe network data, rainfall data, and measured hydrological data of multiple study areas.

[0029] In some embodiments, the underlying surface data includes terrain data, elevation data, and land use types, and the land use types include roads, buildings, and green spaces. The pipe network data includes pipeline data, manhole data, and the connection method between the pipeline and the manhole. The pipeline data includes the spatial distribution of the pipeline network, the length of the pipeline, the cross-sectional shape of the pipeline, the diameter of the pipe, the upstream and downstream bottom elevations, and the manhole data includes the depth and size of the manhole. The rainfall data includes the rainfall amount of the measured field. The measured hydrological data includes the runoff data of the measured field of the pipeline, and the monitoring water level of the manhole and the waterlogging point during the measured rainfall field. The rainfall data and the measured hydrological data of the same study area have a corresponding relationship with the measured fields, and the rainfall data and the measured hydrological data of the same study area include data of at least one measured field, and the start time and test duration of the same measured field are the same.

[0030] The underlying surface data can be obtained from high-resolution remote sensing images using satellite remote sensing technology. Topographic data, elevation data, and land use types can then be extracted using software such as InfoWorks ICM and GIS. Furthermore, the underlying surface data, pipe network data, rainfall data, and measured hydrological data for each study area in this embodiment can also be obtained from local water authorities. The underlying surface data and pipe network data for each study area must be constrained to the same coordinate system.

[0031] Among them, the rainfall data and measured hydrological data of each study area are a collection of test data of multiple measured sessions. The rainfall data of each measured session corresponds to the measured hydrological data of one measured session. For example, the rainfall data and measured hydrological data of the first study area include rainfall data and measured hydrological data of 100 sessions in 12 months of 2023. The start time of rainfall data and measured hydrological data of the same session is the same as the test time. For example, the start time of rainfall data of the first measured session is 8:00 on January 5, and the test duration is 1 hour. Then the start time of measured hydrological data of the first measured session is 8:00 on January 5, and the test duration is 1 hour.

[0032] In step S202 , the processor constructs a rainstorm waterlogging model for each study area based on the underlying surface data, the pipe network data, and the rainfall data.

[0033] In some embodiments, constructing a rainstorm waterlogging model for each study area based on the underlying surface data, the pipe network data, and the rainfall data includes: inputting the underlying surface data, the pipe network data, and the rainfall data into InfoWorks ICM software to obtain a rainstorm waterlogging model for each study area.

[0034] After inputting underlying surface data, pipe network data, and rainfall data into InfoWorks ICM, InfoWorks ICM first constructs a one-dimensional drainage network model. This model then calculates the amount of water collected in the catchment area and simulates the flow within the drainage network. The specific calculation process includes the following formula:

[0035]

[0036] Among them, A is the cross-sectional area of ​​the pipe; t is time; Q is the flow rate; x is the length of the pipe along the direction of water flow; g is the acceleration of gravity; h is the water depth; S0 is the slope of the pipe bottom; and K is the flow resistance coefficient.

[0037] Then, a two-dimensional urban land surface hydrodynamic model is constructed and coupled with a one-dimensional drainage network model to simulate the complex water exchange process between the network and the surface. The formulas are:

[0038]

[0039] Where h is the water depth; u and v are the velocity components in the x and y directions respectively; S 0，x 、S 0，y are the bottom slope components in the x and y directions respectively; S f，x 、S f，y are the friction components in the x and y directions respectively; q 1D is the outflow per unit area; u 1D 、v 1D q 1D Velocity components in the x and y directions.

[0040] In step S203 , the processor calibrates and verifies the corresponding rainstorm waterlogging model based on the rainfall data and measured hydrological data of each study area to obtain a model parameter database.

[0041] The model parameter database includes optimized model parameters of each rainstorm waterlogging model.

[0042] The model parameters include but are not limited to: fixed runoff coefficients of various underlying surfaces, initial loss values, initial infiltration rates, stable infiltration rates, attenuation coefficients, pipe Manning coefficients, surface Manning coefficients, etc.

[0043] The fixed runoff coefficient refers to the proportion of rainfall converted to runoff on different land types. For example, the runoff coefficient of urban roads may be high (usually between 0.7 and 0.9), while the runoff coefficient of green spaces may be low (perhaps 0.1 to 0.3).

[0044] Initial loss refers to the amount of water absorbed by the underlying surface at the beginning of rainfall. It is often used to describe the initial loss of water at the start of a rain event. For example, soil with a high initial loss value can absorb a significant amount of water, while hardened surfaces such as roads have a lower initial loss value.

[0045] Initial infiltration rate refers to the rate at which water penetrates into the soil at the beginning of a rain event. This value is influenced by factors such as soil type, moisture content, and rainfall intensity. Generally, the initial infiltration rate is high because the top layer of soil is loose and can quickly absorb water.

[0046] Steady-state infiltration rate refers to the rate at which water penetrates into the soil after a period of rainfall. As the soil becomes saturated, the rate of infiltration slows down and reaches a steady state. The steady-state infiltration rate is lower than the initial infiltration rate.

[0047] Decay coefficient is used to describe the change in infiltration rate over time during a rain event. It is influenced by factors such as soil type and moisture content. A higher decay coefficient indicates that the infiltration rate decreases more rapidly over time.

[0048] Manning coefficient for pipe is used to describe the frictional properties of water flow within a pipe, affecting the velocity and flow rate of water in the pipe. The Manning coefficient is influenced by factors such as pipe material, roughness, and shape. For example, smooth pipes (such as steel pipes) have a lower Manning coefficient, while rougher pipes (such as brick pipes) have a higher Manning coefficient. A higher Manning coefficient indicates greater resistance to water flow and lower flow velocity.

[0049] Manning coefficient for ground surface is used to describe the frictional properties of the ground surface, mainly affecting the velocity of surface runoff and the distribution of water flow. In storm simulation, a higher Manning coefficient for the ground surface indicates greater friction and slower water flow, while a lower Manning coefficient means faster water flow.

[0050] In some embodiments, the model parameter database is obtained by calibrating and verifying the corresponding stormwater flooding model according to the rainfall data and the measured hydrological data of each study area, including:

[0051] Step a, for a single study area, inputting at least one measured field of the rainfall data into the stormwater flooding model to obtain at least one simulated hydrological data;

[0052] Step b, calculating the simulation accuracy of the stormwater flooding model according to the at least one simulated hydrological data and at least one measured hydrological data;

[0053] Step c, if the simulation accuracy meets the preset simulation accuracy, determining the model parameters of the stormwater flooding model as target model parameters, and writing the target model parameters into the model parameter database;

[0054] Step d: If the simulation accuracy does not meet the preset simulation accuracy, adjust the model parameters of the rainstorm waterlogging model and re-execute step a "inputting the rainfall data of at least one measured event into the rainstorm waterlogging model to obtain at least one simulated hydrological data."

[0055] Among them, the judgment of simulation accuracy includes two indicators: Nash efficiency coefficient (NSE) and relative error. In some embodiments, the simulation accuracy includes Nash efficiency coefficient, and the calculation formula of Nash efficiency coefficient is as follows:

[0056]

[0057] Among them, NSE is the Nash efficiency coefficient, Yi is the measured hydrological data of the i-th measured field, is the simulated hydrological data of the ith measured event, is the average value of the measured hydrological data of all measured sessions, n is the total number of measured sessions, and the value range of NSE is (-∞,1). The closer NSE is to 1, the higher the accuracy.

[0058] The relative error (ε) refers to the ratio of the absolute error to the measured hydrological data, and the absolute error (E) refers to the absolute value of the difference between the measured hydrological data and the simulated hydrological data. The calculation formula is ε = E / measured hydrological data = |measured hydrological data - simulated hydrological data| / measured hydrological data.

[0059] Among them, the Nash efficiency coefficient is higher than 0.60, and the absolute value of the relative error is less than 10%.

[0060] For this embodiment, for example, the first study area includes two measured rainfall information as shown in Table 1 below:

[0061] Table 1 shows the two measured rainfall information in the first study area provided in the embodiment of this application.

[0062]

[0063] The measured rainfall data of the first rainstorm waterlogging model of the first study area is input to obtain the first hydrological simulation data. Figure 3 (a) Input the measured rainfall data of the second rainstorm waterlogging model into the first rainstorm waterlogging model to obtain the second hydrological simulation data, see Figure 3 (b)

[0064] The relative error and Nash efficiency coefficient are calculated based on the first hydrological simulation data, the second hydrological simulation data, the first measured hydrological data, and the second measured hydrological data. If the relative error is less than 10% and the Nash efficiency coefficient is greater than 0.6, it indicates that the model parameter 1 currently used in the first rainstorm waterlogging model meets the simulation accuracy requirements, and the simulation parameter 1 can be directly determined as the target simulation parameter of the first rainstorm waterlogging model. If the relative error is greater than 10% and / or the Nash efficiency coefficient is less than 0.6, it indicates that the model parameter 1 currently used in the first rainstorm waterlogging model does not meet the simulation accuracy requirements, the model parameter 1 used in the first rainstorm waterlogging model is adjusted to model parameter 2, and the rainfall data of the measured session 1 is again input into the adjusted first rainstorm waterlogging model to obtain the third hydrological simulation data, and the rainfall data of the measured session 2 is input into the adjusted first rainstorm waterlogging model to obtain the fourth hydrological simulation data. The simulation accuracy is calculated based on the third hydrological simulation data, the fourth hydrological simulation data, the first measured hydrological data, and the second measured hydrological data until the simulation accuracy meets the preset simulation accuracy requirements.

[0065] In step S204, the processor constructs a deep learning model.

[0066] In some embodiments, constructing a deep learning model includes: using the Keras library in the Python language to construct a convolutional neural network (CNN) model, the CNN model including an input layer, a first convolutional layer, a second convolutional layer, a first pooling layer, a second pooling layer, a flattening layer, a first fully connected layer, a second fully connected layer, a third fully connected layer, a Dropout layer, and an output layer; wherein the input layer, the first convolutional layer, the first pooling layer, the second convolutional layer, the second pooling layer, the flattening layer, the first fully connected layer, the second fully connected layer, the third fully connected layer, the Dropout layer, and the output layer are connected in sequence; the input layer is used to input the input data database; the output layer is used to output the model parameters of the rainstorm waterlogging model for each study area.

[0067] Among them, the flattening layer is used to convert the output of the convolution and pooling layers into a one-dimensional vector to facilitate transmission to the fully connected layer. The number of neurons in the flattening layer is 256.

[0068] Among them, the number of neurons in the first fully connected layer, the second fully connected layer, and the third fully connected layer are 64, 256, and 512, respectively.

[0069] Among them, the Dropout layer is used to reduce the overfitting of the model and enhance the generalization ability. The random inactivation rate of the Dropout layer is set to 0.1.

[0070] In the specific implementation, in a one-dimensional convolutional neural network, the previous one-dimensional convolutional layer is forward propagated to the neuron input of the next convolutional layer. The convolution process is as follows:

[0071]

[0072] Where: Input to the kth neuron of the lth convolutional layer; is the bias of the kth neuron in the lth convolutional layer; is the output of the i-th neuron in the (l-1)th convolutional layer; is the convolution kernel from the i-th neuron in the (l-1)th convolutional layer to the k-th neuron in the l-th convolutional layer; ConvlD represents a one-dimensional convolution operation;

[0073] for The feature vector of the k-th neuron in the l-th convolutional layer obtained after the activation function F and the downsampling function SS is pooled as follows:

[0074]

[0075] Finally, a deep convolutional neural network model is constructed by adding a fully connected layer.

[0076] In some embodiments, the first convolutional layer and the second convolutional layer are each followed by an activation function, and the activation function is a ReLU function, and the ReLU function has the following form:

[0077]

[0078] Among them, R(x) is the output value of the activation function, and x is the input value of the activation function, that is, the output value of the previous connection layer.

[0079] In step S205 , the processor constructs an input data database based on the underlying surface data, pipe network data, and rainfall data of each study area.

[0080] In some embodiments, after generating an input data database and a model parameter database, and before training the deep learning model using the input data database as an independent variable and the model parameter database as a dependent variable, the method further includes: normalizing the input data database and the model parameter database; wherein the normalization calculation formula is as follows:

[0081]

[0082] Among them, X i is the normalized value, x is the initial value, X max With X min The maximum and minimum values ​​for each database respectively.

[0083] In step S206 , the processor trains the deep learning model using the input data database as an independent variable and the model parameter database as a dependent variable.

[0084] In step S207, the processor inputs the underlying surface data, pipe network data, and rainfall data of the target study area into the trained deep learning model to obtain model parameters of the rainstorm waterlogging model of the target study area.

[0085] It can be seen that in an embodiment of the present application, the processor obtains the underlying surface data, pipe network data, rainfall data and measured hydrological data of multiple study areas, and constructs a rainstorm waterlogging model for each study area based on the underlying surface data, pipe network data and rainfall data; calibrates and verifies the corresponding rainstorm waterlogging model based on the rainfall data and measured hydrological data of each study area to obtain a model parameter database, which includes the optimized model parameters of each rainstorm waterlogging model; constructs a deep learning model, and constructs an input data database based on the underlying surface data, pipe network data and rainfall data of each study area; trains the deep learning model with the input data database as the independent variable and the model parameter database as the dependent variable; inputs the underlying surface data, pipe network data and rainfall data of the target study area into the trained deep learning model to obtain the model parameters of the rainstorm waterlogging model of the target study area. It can be seen that in this application, the deep learning model is applied to the parameter determination of the urban rainstorm waterlogging model with a clear physical mechanism. Compared with the traditional manual debugging method, the model parameters of the urban rainstorm waterlogging model can be quickly and accurately determined, thereby improving the accuracy and timeliness of urban rainstorm waterlogging warnings.

[0086] The above mainly introduces the solution of the embodiment of the present application from the perspective of the execution process on the method side. It is understandable that, in order to realize the above functions, the controller includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiment provided herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0087] The embodiment of the present application can divide the controller into functional units according to the above method example. For example, each functional unit can be divided according to each function, or two or more functions can be integrated into one processing module. The above integrated unit can be implemented in the form of hardware or in the form of a software program module. It should be noted that the division of units in the embodiment of the present application is schematic and is only a logical functional division, and other division methods can be used in actual implementation.

[0088] In the case of integrated units, see Figure 4 , Figure 4 This is a functional unit block diagram of a device for quickly determining parameters of an urban waterlogging model based on deep learning provided in an embodiment of the present application. The device 4 for quickly determining parameters of an urban waterlogging model includes:

[0089] An acquisition unit 401 is used to acquire underlying surface data, pipe network data, rainfall data, and measured hydrological data for multiple study areas;

[0090] Processing unit 402 is used to construct a rainstorm waterlogging model for each study area based on the underlying surface data, the pipe network data, and the rainfall data; and calibrate and verify the corresponding rainstorm waterlogging model based on the rainfall data and measured hydrological data of each study area to obtain a model parameter database, wherein the model parameter database includes optimized model parameters of each rainstorm waterlogging model; and construct a deep learning model, and construct an input data database based on the underlying surface data, pipe network data, and rainfall data of each study area; and train the deep learning model using the input data database as an independent variable and the model parameter database as a dependent variable; and input the underlying surface data, pipe network data, and rainfall data of the target study area into the trained deep learning model to obtain model parameters of the rainstorm waterlogging model of the target study area.

[0091] It can be seen that in an embodiment of the present application, the processor obtains the underlying surface data, pipe network data, rainfall data and measured hydrological data of multiple study areas, and constructs a rainstorm waterlogging model for each study area based on the underlying surface data, pipe network data and rainfall data; calibrates and verifies the corresponding rainstorm waterlogging model based on the rainfall data and measured hydrological data of each study area to obtain a model parameter database, which includes the optimized model parameters of each rainstorm waterlogging model; constructs a deep learning model, and constructs an input data database based on the underlying surface data, pipe network data and rainfall data of each study area; trains the deep learning model with the input data database as the independent variable and the model parameter database as the dependent variable; inputs the underlying surface data, pipe network data and rainfall data of the target study area into the trained deep learning model to obtain the model parameters of the rainstorm waterlogging model of the target study area. It can be seen that in this application, the deep learning model is applied to the parameter determination of the urban rainstorm waterlogging model with a clear physical mechanism. Compared with the traditional manual debugging method, the model parameters of the urban rainstorm waterlogging model can be quickly and accurately determined, thereby improving the accuracy and timeliness of urban rainstorm waterlogging warnings.

[0092] In some embodiments, the underlying surface data includes terrain data, elevation data and land use types, and the land use types include roads, buildings and green spaces; the pipe network data includes pipeline data, manhole data and the connection method between pipelines and manholes, and the pipeline data includes the spatial distribution of the pipe network, pipeline length, pipeline cross-sectional shape, pipe diameter, upstream and downstream bottom elevations, and the manhole data includes the depth and size of the manhole; the rainfall data includes the rainfall amount of the measured times; the measured hydrological data includes the runoff data of the measured times of the pipeline, and the monitoring water level of the manholes and waterlogging points during the measured rainfall; wherein, there is a corresponding relationship between the rainfall data and the measured hydrological data of the same study area, and the rainfall data and the measured hydrological data of the same study area include data of at least one measured time, and the start time and test duration of the same measured time are the same.

[0093] In some embodiments, the processing unit 402 constructs a rainstorm waterlogging model for each study area based on the underlying surface data, the pipe network data, and the rainfall data, including: inputting the underlying surface data, the pipe network data, and the rainfall data into InfoWorks ICM software to obtain the rainstorm waterlogging model for each study area.

[0094] In some embodiments, the processing unit 402 calibrates and verifies the corresponding rainstorm waterlogging model based on the rainfall data and measured hydrological data of each study area to obtain a model parameter database, including: for a single study area, inputting the rainfall data of at least one measured session into the rainstorm waterlogging model to obtain at least one simulated hydrological data; calculating the simulation accuracy of the rainstorm waterlogging model based on the at least one simulated hydrological data and at least one measured hydrological data; if the simulation accuracy meets the preset simulation accuracy, determining the model parameters of the rainstorm waterlogging model as target model parameters and writing them into the model parameter database; if the simulation accuracy does not meet the preset simulation accuracy, adjusting the model parameters of the rainstorm waterlogging model, and re-executing the step of "inputting the rainfall data of at least one measured session into the rainstorm waterlogging model to obtain at least one simulated hydrological data".

[0095] In some embodiments, the simulation accuracy includes a Nash efficiency coefficient, and the calculation formula of the Nash efficiency coefficient is as follows:

[0096]

[0097] Among them, NSE is the Nash efficiency coefficient, Yi is the measured hydrological data of the i-th measured field, is the simulated hydrological data of the ith measured event, is the average value of the measured hydrological data of all measured sessions, n is the total number of measured sessions, and the value range of NSE is (-∞,1].

[0098] In some embodiments, before the processing unit 402 trains the deep learning model using the input data database as an independent variable and the model parameter database as a dependent variable, the method further includes: normalizing the input data database and the model parameter database; wherein the normalization calculation formula is as follows:

[0099]

[0100] Among them, X i is the normalized value, x is the initial value, X max With X min They are the maximum and minimum values ​​of a single database respectively.

[0101] In some embodiments, constructing a deep learning model includes: using the Keras library in the Python language to construct a convolutional neural network (CNN) model, the CNN model including an input layer, a first convolutional layer, a second convolutional layer, a first pooling layer, a second pooling layer, a flattening layer, a first fully connected layer, a second fully connected layer, a third fully connected layer, a Dropout layer, and an output layer; wherein the input layer, the first convolutional layer, the first pooling layer, the second convolutional layer, the second pooling layer, the flattening layer, the first fully connected layer, the second fully connected layer, the third fully connected layer, the Dropout layer, and the output layer are connected in sequence; the input layer is used to input the input data database; the output layer is used to output the model parameters of the rainstorm waterlogging model for each study area.

[0102] In some embodiments, the first convolutional layer and the second convolutional layer are each followed by an activation function, and the activation function is a ReLU function, and the ReLU function has the following form:

[0103]

[0104] Among them, R(x) is the output value of the activation function, and x is the input value of the activation function, that is, the output value of the previous connection layer.

[0105] An embodiment of the present application provides a computer-readable storage medium having a computer program / instruction stored thereon. When the computer program / instruction is executed by a processor, the steps of the method described in any possible embodiment are implemented.

[0106] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0107] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0108] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the above-mentioned units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0109] The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0110] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0111] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes a number of instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the above-mentioned methods of each embodiment of the present application. The aforementioned memory includes: various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0112] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program. The program can be stored in a computer-readable memory, and the memory can include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0113] The above is a detailed introduction to the embodiments of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for those skilled in the art, according to the idea of ​​the present application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for quickly determining parameters of urban waterlogging models based on deep learning, characterized in that: The method comprises: Obtain underlying surface data, pipe network data, rainfall data and measured hydrological data for multiple study areas; Constructing a rainstorm waterlogging model for each study area according to the underlying surface data, the pipe network data, and the rainfall data; Calibrate and verify the corresponding rainstorm waterlogging model based on rainfall data and measured hydrological data of each study area to obtain a model parameter database, wherein the model parameter database includes optimized model parameters of each rainstorm waterlogging model; Build a deep learning model and construct an input data database based on the underlying surface data, pipeline network data, and rainfall data for each study area; Training the deep learning model using the input data database as an independent variable and the model parameter database as a dependent variable; Input the underlying surface data, pipeline network data, and rainfall data of the target study area into the trained deep learning model to obtain the model parameters of the rainstorm waterlogging model of the target study area; The calibrating and verifying the corresponding rainstorm waterlogging model based on the rainfall data and measured hydrological data of each study area to obtain a model parameter database includes: for a single study area, inputting the rainfall data of at least one measured event into the rainstorm waterlogging model to obtain at least one simulated hydrological data; calculating the simulation accuracy of the rainstorm waterlogging model based on the at least one simulated hydrological data and at least one measured hydrological data; if the simulation accuracy meets the preset simulation accuracy, determining the model parameters of the rainstorm waterlogging model as target model parameters and writing them into the model parameter database; if the simulation accuracy does not meet the preset simulation accuracy, adjusting the model parameters of the rainstorm waterlogging model and re-performing the step of "inputting the rainfall data of at least one measured event into the rainstorm waterlogging model to obtain at least one simulated hydrological data"; The simulation accuracy includes the Nash efficiency coefficient, and the calculation formula of the Nash efficiency coefficient is as follows: Among them, NSE is the Nash efficiency coefficient, Y i is the measured hydrological data of the ith measurement session, is the simulated hydrological data of the ith measured event, is the average value of the measured hydrological data of all measured sessions, n is the total number of measured sessions, and the value range of NSE is (-∞,1].

2. The method according to claim 1, characterized in that The underlying surface data includes terrain data, elevation data and land use types, and the land use types include roads, buildings and green spaces; The pipe network data includes pipeline data, inspection well data and the connection mode between pipeline and inspection well. The pipeline data includes the spatial distribution of the pipe network, pipeline length, pipeline cross-sectional shape, pipe diameter, upstream and downstream bottom elevations. The inspection well data includes the depth and size of the inspection well. The rainfall data includes the rainfall amount of the measured sessions; The measured hydrological data include measured runoff data of pipelines, and water level monitoring data of inspection wells and waterlogging points during measured rainfall events; Among them, there is a corresponding relationship between the measured times of rainfall data and measured hydrological data in the same study area, and the rainfall data and measured hydrological data in the same study area include data of at least one measured time, and the start time and test duration of the same measured time are the same.

3. The method according to claim 1, characterized in that The constructing of a rainstorm waterlogging model for each study area based on the underlying surface data, the pipe network data, and the rainfall data includes: The underlying surface data, the pipe network data, and the rainfall data are input into InfoWorks ICM software to obtain a rainstorm waterlogging model for each study area.

4. The method according to claim 1, wherein Before training the deep learning model using the input data database as an independent variable and the model parameter database as a dependent variable, the method further includes: performing normalization processing on the input data database and the model parameter database; The normalized calculation formula is as follows: in, is the normalized value, X is the initial value, and They are the maximum and minimum values ​​of a single database respectively.

5. The method according to claim 1, wherein The construction of the deep learning model includes: A convolutional neural network (CNN) model is constructed using the Keras library in Python. The CNN model includes an input layer, a first convolutional layer, a second convolutional layer, a first pooling layer, a second pooling layer, a flattening layer, a first fully connected layer, a second fully connected layer, a third fully connected layer, a Dropout layer, and an output layer. Among them, the input layer, the first convolutional layer, the first pooling layer, the second convolutional layer, the second pooling layer, the flattening layer, the first fully connected layer, the second fully connected layer, the third fully connected layer, the Dropout layer and the output layer are connected in sequence; An input layer, used for inputting the input data database; The output layer is used to output the model parameters of the rainstorm waterlogging model for each study area.

6. The method according to claim 5, characterized in that The first convolutional layer and the second convolutional layer are each followed by an activation function, and the activation function is a ReLU function, and the ReLU function form is as follows: Among them, R(x) is the output value of the activation function, and x is the output value of the previous connection layer of the activation function.

7. A device for quickly determining parameters of urban waterlogging models based on deep learning, characterized in that: include: Acquisition unit, used to obtain underlying surface data, pipe network data, rainfall data and measured hydrological data of multiple study areas; A processing unit is configured to construct a rainstorm waterlogging model for each study area based on the underlying surface data, the pipe network data, and the rainfall data; and calibrate and verify the corresponding rainstorm waterlogging model based on the rainfall data and measured hydrological data of each study area to obtain a model parameter database, wherein the model parameter database includes optimized model parameters of each rainstorm waterlogging model; and construct a deep learning model, construct an input data database based on the underlying surface data, pipe network data, and rainfall data of each study area; and train the deep learning model using the input data database as an independent variable and the model parameter database as a dependent variable; and input the underlying surface data, pipe network data, and rainfall data of the target study area into the trained deep learning model to obtain model parameters of the rainstorm waterlogging model of the target study area. In terms of calibrating and verifying the corresponding rainstorm waterlogging model based on the rainfall data and measured hydrological data of each study area to obtain a model parameter database, the processing unit is further configured to: for a single study area, input the rainfall data of at least one measured event into the rainstorm waterlogging model to obtain at least one simulated hydrological data; calculate the simulation accuracy of the rainstorm waterlogging model based on the at least one simulated hydrological data and at least one measured hydrological data; if the simulation accuracy meets the preset simulation accuracy, determine the model parameters of the rainstorm waterlogging model as target model parameters and write them into the model parameter database; if the simulation accuracy does not meet the preset simulation accuracy, adjust the model parameters of the rainstorm waterlogging model and re-execute the step of "inputting the rainfall data of at least one measured event into the rainstorm waterlogging model to obtain at least one simulated hydrological data"; The simulation accuracy includes the Nash efficiency coefficient, and the calculation formula of the Nash efficiency coefficient is as follows: Among them, NSE is the Nash efficiency coefficient, Y i is the measured hydrological data of the ith measurement session, is the simulated hydrological data of the ith measured event, is the average value of the measured hydrological data of all measured sessions, n is the total number of measured sessions, and the value range of NSE is (-∞,1].

8. An electronic device, characterized in that: The method comprises a processor and a memory, wherein a computer program is stored in the memory, and when the processor calls the computer program in the memory, the method according to any one of claims 1 to 6 is executed.

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