River channel Manning roughness coefficient prediction method based on plant morphological characteristics

Abstract

The invention relates to a river channel Manning roughness coefficient prediction method based on plant morphological characteristics. The method comprises the following steps: collecting physical attribute data and vegetation morphological characteristic parameter data of a target river channel; based on the physical attribute data, retrieving and matching through a preset river model database to obtain river model parameters, and constructing a river basic model; analyzing an influence rule of the morphological characteristic parameters on a Manning roughness coefficient, and establishing a corresponding influence relationship; optimizing and adjusting the basic model according to the relation to form a prediction model; and performing prediction by using the model, and outputting a Manning roughness coefficient result. By adopting the method, plant morphological factors can be effectively integrated, the prediction accuracy and practicability are improved, and the limitation of ignoring vegetation influence in a traditional method is solved.

Description

Technical Field

[0001] This invention belongs to the field of water conservancy engineering technology, and in particular relates to a method for predicting the Manning roughness coefficient of river channels based on plant morphological characteristics. Background Technology

[0002] With the development of water conservancy engineering technology, a technique for calculating the Manning roughness coefficient of rivers based on hydraulic principles has emerged. This technique can quantitatively assess the resistance characteristics of rivers through hydraulic formulas, thus providing key parameters for flood control, river regulation, and ecological restoration. Traditional techniques typically involve directly substituting measured hydraulic elements (such as flow velocity, water depth, and slope) into the Manning formula for calculation, or assigning values ​​based on empirical roughness tables derived from river material. This approach is relatively straightforward but relies heavily on extensive historical data. Current traditional methods have significant problems: First, they do not fully consider the dynamic biological characteristics and morphological changes of river vegetation on water flow resistance, resulting in severely insufficient prediction accuracy in rivers with dense vegetation or significant seasonal variations. Second, the empirical assignment method is highly subjective and lacks adaptive adjustment capabilities, making it difficult to adapt to complex and changing natural river environments, thus limiting the effectiveness and reliability of the Manning coefficient in practical engineering applications. Summary of the Invention

[0003] Therefore, it is necessary to provide a method for predicting the roughness coefficient of river channels based on plant morphological characteristics that can solve the above problems.

[0004] Firstly, this application provides a method for predicting the Manning roughness coefficient of river channels based on plant morphological characteristics, including:

[0005] Collect physical attribute data of the target river channel and morphological characteristic parameter data of the vegetation on the target river channel. The physical attribute data includes average water flow velocity data, hydraulic radius data and river channel slope data. The morphological characteristic parameter data includes tree height data, crown width data, leaf area data, branch density data, branch stiffness data, growth rate data, vegetation growth location data and vegetation type data.

[0006] Based on physical attribute data, the river channel model parameters are obtained by searching and matching through a pre-set river channel model database, and a basic river channel model is constructed based on the river channel model parameters.

[0007] Based on the basic river channel model, the influence of morphological characteristic parameters on the Manning roughness coefficient of the river channel is analyzed, and the corresponding influence relationship between morphological characteristic parameters and the Manning roughness coefficient of the river channel is established.

[0008] Based on the corresponding influence relationships, the basic model of the river channel is optimized and adjusted to obtain the prediction model of the Manning roughness coefficient of the river channel.

[0009] Based on the physical attribute data of the target river channel and the morphological characteristic parameters of the vegetation on the target river channel, the Manning roughness coefficient prediction model is used to predict the Manning roughness coefficient of the river channel.

[0010] In one embodiment, based on physical attribute data, a search and matching process is performed through a pre-set river channel model database to obtain river channel model parameters. Then, based on these parameters, a basic river channel model is constructed, including:

[0011] Based on average flow velocity data, hydraulic radius data, and river slope data, the target river is divided into segments using preset segmentation rules to obtain multiple characteristic river segments.

[0012] For the average flow velocity data, hydraulic radius data and channel slope data corresponding to each characteristic river section, a multi-dimensional weighted retrieval model is used to search and match in the preset river channel model database to obtain the channel model parameters of each characteristic river section.

[0013] Based on the channel model parameters of each characteristic river segment, the Manning roughness coefficient of each characteristic river segment is calculated, and the channel model parameters and Manning roughness coefficient of each characteristic river segment are integrated to obtain the basic channel model.

[0014] In one embodiment, based on a basic river channel model, the influence of morphological characteristic parameters on the Manning roughness coefficient of the river channel is analyzed to establish the corresponding influence relationship between morphological characteristic parameters and the Manning roughness coefficient of the river channel, including:

[0015] Based on the river channel basic model, the morphological feature parameter data of each characteristic river segment are matched to the corresponding characteristic river segment according to the vegetation growth location data to obtain the morphological feature parameter dataset of each characteristic river segment.

[0016] For the morphological feature parameter dataset of each characteristic river section, based on the vegetation type data, tree height data, crown width data, leaf area data, branch density data, branch stiffness data, and growth rate data are classified into the corresponding vegetation type to form a subset of morphological parameters of characteristic river section-vegetation type.

[0017] Using the analytic hierarchy process (AHP), a weighted judgment matrix was constructed for each subset of morphological parameters of a characteristic river section and vegetation type, with tree height, crown width, leaf area, branch density, branch stiffness, and growth rate as indicators.

[0018] The influence weights of tree height, crown width, leaf characteristics, branch density, branch hardness, growth rate and the Manning roughness coefficient of the corresponding characteristic river section are calculated using the weight judgment matrices.

[0019] For each type of vegetation, within the corresponding characteristic river section, linear regression analysis was performed on tree height data, crown width data, leaf area data, branch density, branch stiffness and growth rate data with the Manning roughness coefficient of the corresponding characteristic river section to obtain the preliminary influence relationship curves of each single morphological parameter.

[0020] Based on the influence weight values, the preliminary influence relationship curves of each individual morphological parameter are weighted and fused to generate a comprehensive influence law model of multiple morphological parameters.

[0021] A comprehensive influence model integrating multiple morphological parameters of all characteristic river sections was developed, and a corresponding influence relationship was formed by constructing a mapping table of morphological characteristic parameters, vegetation type, and Manning roughness coefficient.

[0022] In one embodiment, the basic river channel model is optimized and adjusted according to the corresponding influence relationship to obtain a prediction model for the Manning roughness coefficient of the river channel, including:

[0023] Based on the corresponding influence relationship, a morphological correction function for the Manning roughness coefficient is generated for each characteristic river segment;

[0024] Within the basic river channel model, the morphological correction function of each characteristic river segment is coupled with the Manning roughness coefficient of the corresponding characteristic river segment to obtain the optimized Manning roughness coefficient of the corresponding characteristic river segment.

[0025] Using the structure of the basic river channel model as a framework, the optimized Manning roughness coefficients of each characteristic river segment are updated to the corresponding characteristic river segment, forming a prediction model for the Manning roughness coefficient of the river channel.

[0026] In one embodiment, the Manning roughness coefficient for each characteristic river segment is calculated based on the channel model parameters of each characteristic river segment, including:

[0027] For each segmented characteristic river section, extract the hydraulic radius data, river slope data, and average flow velocity data from the corresponding physical attribute data.

[0028] Based on the extracted hydraulic radius data, river slope data, and average flow velocity data, the Manning roughness coefficient is calculated using the following formula:

[0029]

[0030] Where i is the feature river segment number, taking values ​​from 1, 2, ..., n, and n is the total number of feature river segments divided into the target river channel, R i S is the hydraulic radius, in meters (m). i V represents the riverbed slope. i The average flow velocity is expressed in m / s, n. base,i Let be the Manning roughness coefficient of the i-th characteristic river segment.

[0031] In one embodiment, based on the corresponding influence relationship, a morphological correction function for the Manning roughness coefficient of each characteristic river segment is generated, including:

[0032] For the target river section, based on the comprehensive influence law model of multi-morphological parameters in the corresponding influence relationship, a dynamic response function is constructed with standardized parameters such as tree height, crown width, leaf area, branch density, branch hardness, and growth rate as input variables.

[0033] The dynamic response function is multiplied by a dynamic adjustment factor determined based on vegetation type and growing season to generate the Manning roughness coefficient morphological correction function for the target characteristic river segment.

[0034] In one embodiment, the method further includes:

[0035] Obtain the actual Manning roughness coefficient of the target river channel;

[0036] The deviation between the actual Manning roughness coefficient and the predicted Manning roughness coefficient of the river channel is calculated to obtain the prediction deviation result.

[0037] Based on the deviation results, the influence weight values ​​of the multi-morphological parameter comprehensive influence law model are adjusted to obtain the updated multi-morphological parameter comprehensive influence law model. The updated multi-morphological parameter comprehensive influence law model is used to subsequently construct a mapping relationship table of morphological feature parameters-vegetation type-Manning roughness coefficient to form the corresponding influence relationship.

[0038] Secondly, this application also provides a device for predicting the Manning roughness coefficient of river channels based on plant morphological characteristics, comprising:

[0039] The river channel data acquisition module is used to collect physical attribute data of the target river channel and morphological characteristic parameter data of the vegetation on the target river channel. The physical attribute data includes average water flow velocity data, hydraulic radius data, and river channel slope data. The morphological characteristic parameter data includes tree height data, crown width data, leaf area data, branch density data, branch stiffness data, growth rate data, vegetation growth location data, and vegetation type data.

[0040] The river model construction module is used to obtain river model parameters based on physical attribute data by searching and matching through a preset river model database, and to construct a basic river model based on the river model parameters.

[0041] The influence relationship establishment module is used to establish the corresponding influence relationship between morphological feature parameters and the Manning roughness coefficient of the river channel based on the basic river channel model by analyzing the influence law of morphological feature parameters on the Manning roughness coefficient of the river channel.

[0042] The prediction model optimization module is used to optimize and adjust the basic river channel model according to the corresponding influence relationship, so as to obtain the prediction model of the Manning roughness coefficient of the river channel.

[0043] The Manning roughness coefficient prediction module is used to predict the Manning roughness coefficient of a river channel based on the physical attribute data of the target river channel and the morphological characteristic parameter data of the vegetation on the target river channel, using the river channel Manning roughness coefficient prediction model to obtain the prediction result of the river channel Manning roughness coefficient.

[0044] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-mentioned method for predicting the roughness coefficient of river channels based on plant morphological characteristics.

[0045] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method for predicting the roughness coefficient of a river channel based on plant morphological characteristics.

[0046] The aforementioned method for predicting the Manning roughness coefficient of a river channel based on plant morphology features collects physical attribute data and vegetation morphology parameter data of the target river channel. It then uses a pre-set river channel model database to retrieve and match river model parameters, constructing a basic river channel model. The method analyzes the influence of morphological parameters on the Manning roughness coefficient, establishing corresponding influence relationships. Based on these relationships, the basic model is optimized and adjusted to form a prediction model, thus achieving the prediction of the Manning roughness coefficient. By systematically integrating dynamic factors of vegetation morphology into the traditional hydraulic model and introducing multi-dimensional vegetation parameter quantitative analysis during the model construction stage, the method derives the influence of vegetation growth changes on water flow resistance. This effectively overcomes the problems of insufficient prediction accuracy and poor adaptability caused by ignoring the influence of vegetation, improving the accuracy and practicality of the prediction results and providing more reliable decision support for water conservancy projects in complex river environments. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a flowchart of a method for predicting the Manning roughness coefficient of a river channel based on plant morphological characteristics, according to the present invention.

[0049] Figure 2This is a structural diagram of a river channel roughness coefficient prediction device based on plant morphological characteristics according to the present invention. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0051] In one embodiment, such as Figure 1 As shown, a method for predicting the Manning roughness coefficient of a river channel based on plant morphological characteristics is provided. This embodiment illustrates the application of this method to a terminal. It is understood that this method can also be applied to a server, or to a system including both a terminal and a server, and can be implemented through the interaction between the terminal and the server. In the implementation environment, the hardware architecture mainly includes terminal devices (such as mobile measurement terminals, embedded sensors, or drones), server clusters, and network communication components. The terminal devices are responsible for collecting physical attribute data of the target river channel (such as average flow velocity, hydraulic radius, and river channel slope) and vegetation morphological characteristic parameter data (such as tree height, crown width, leaf area, branch stiffness, and density), and uploading the data to the server in real time via wireless or wired networks. The server is equipped with a high-performance processor and a large-capacity memory to store a preset river channel model database and to execute multi-dimensional weight retrieval, model construction, and optimization algorithms. In application scenarios, when water conservancy projects require real-time assessment of river resistance characteristics, terminal devices collect data on-site at the river level. After receiving the data, the server automatically retrieves and matches river model parameters, constructs a basic model, and analyzes the influencing patterns based on morphological characteristic parameters. Through interaction between the terminal and the server, dynamic prediction and result feedback of the Manning roughness coefficient are achieved, thereby supporting flood control and ecological restoration decisions. The entire system effectively integrates dynamic vegetation factors through hardware collaboration, improving prediction accuracy and adaptability. In this embodiment, the method includes the following steps:

[0052] S01, collect physical attribute data of the target river channel and morphological characteristic parameter data of vegetation on the target river channel. The physical attribute data includes average water flow velocity data, hydraulic radius data and river channel slope data. The morphological characteristic parameter data includes tree height data, crown width data, leaf area data, branch density data, branch hardness data, growth rate data, vegetation growth location data and vegetation type data.

[0053] The data collection includes physical attribute data of the target river channel and morphological characteristic parameter data of the vegetation on the target river channel. The physical attribute data includes average flow velocity data, hydraulic radius data, and river channel slope data obtained on-site by hydrological measuring instruments (such as current meters, depth gauges, and slope sensors), which are used to quantify the basic hydraulic characteristics of the river channel. The morphological characteristic parameter data covers tree height data, crown width data, leaf area data, branch density data, branch hardness data, growth rate data, vegetation growth location data, and vegetation type data collected through remote sensing monitoring, on-site surveys, or growth recording equipment, aiming to comprehensively capture the biological morphological dynamics of vegetation and its spatial distribution.

[0054] S02. Based on physical attribute data, the river model parameters are obtained by searching and matching through a preset river model database, and a basic river model is constructed based on the river model parameters.

[0055] Specifically, intelligent retrieval and matching can be performed by accessing a preset river model database (containing standard models of different river segment types (such as straight and meandering river segments), storing parameters including the typical range of average flow velocity, hydraulic radius, and river slope) to obtain river model parameters suitable for the target river. The preset river model database is a multi-dimensional dataset storing historical river hydraulic characteristics. The retrieval and matching process can use a weight optimization algorithm to adapt to the dynamic changes in physical attribute data. The river model parameters include basic hydraulic elements such as river geometry and material coefficients. Based on these parameters, a basic river model is constructed. This model integrates the parameter calculation results (Manning roughness coefficient) of each characteristic river segment to form a structured representation, providing a standardized framework for subsequent vegetation impact analysis.

[0056] S03, based on the basic river channel model, establishes the corresponding influence relationship between morphological characteristic parameters and the Manning roughness coefficient of the river channel by analyzing the influence law of morphological characteristic parameters on the Manning roughness coefficient of the river channel.

[0057] Specifically, the dynamic influence of vegetation morphological characteristic parameters (including tree height, crown width, leaf area, branch density, branch stiffness, growth rate, vegetation growth location, and vegetation type data) on the Manning roughness coefficient of river channels can be analyzed. (For example, by using the controlled variable method, fixing other variables besides single morphological parameters and observing the trend of roughness coefficient changes), the correlation between each morphological parameter and the Manning roughness coefficient can be quantified, and a quantitative correspondence between morphological characteristic parameters and the Manning roughness coefficient of river channels can be established. This relationship is represented in the form of a mapping table or model, providing a theoretical basis for subsequent prediction model optimization.

[0058] S04. Based on the corresponding influence relationship, the basic model of the river channel is optimized and adjusted to obtain the prediction model of the Manning roughness coefficient of the river channel.

[0059] Specifically, based on the established correlation between morphological characteristic parameters and the Manning roughness coefficient of the river channel (this correlation is quantified by a multi-morphological parameter comprehensive influence law model and mapping relationship table), the basic river channel model is dynamically optimized and adjusted (prioritizing characteristic river segments with roughness coefficient deviation ≥5%, and correcting them segment by segment according to vegetation type). By generating a morphological correction function for the Manning roughness coefficient of each characteristic river segment, and coupling this function with the corresponding Manning roughness coefficient of the characteristic river segment in the basic model, the optimized Manning roughness coefficient is obtained. Using the structure of the basic river channel model as a framework, the optimization results of each characteristic river segment are integrated and updated to form a prediction model for the Manning roughness coefficient of the river channel that can comprehensively reflect the dynamic influence of vegetation morphology.

[0060] S05. Based on the physical attribute data of the target river channel and the morphological characteristic parameter data of the vegetation on the target river channel, the Manning roughness coefficient prediction model of the river channel is used to make predictions and obtain the prediction results of the Manning roughness coefficient of the river channel.

[0061] Among them, based on the physical attribute data of the target river channel and the morphological characteristic parameter data of the vegetation on the target river channel, the optimized Manning roughness coefficient prediction model can be used for comprehensive prediction. By coupling the influence relationship between the basic river channel model and the morphological characteristic parameters, the dynamic calculation of the Manning roughness coefficient is realized, and the prediction result of the Manning roughness coefficient of the river channel reflecting the dynamic change characteristics of vegetation morphology is output.

[0062] The aforementioned method for predicting the Manning roughness coefficient of a river channel based on plant morphological characteristics collects physical attribute data and vegetation morphological parameter data of the target river channel. Based on the physical attribute data, a basic river channel model is constructed by searching and matching a pre-set river channel model database. The influence of morphological parameters on the Manning roughness coefficient is analyzed, corresponding influence relationships are established, and the basic model is optimized and adjusted accordingly to form a prediction model. By directly integrating the dynamic morphological parameters of vegetation, this method overcomes the limitations of traditional empirical assignment methods, which are highly subjective and ignore the biological characteristics of vegetation. It achieves data-driven adaptive prediction of the Manning roughness coefficient, significantly improving the prediction accuracy and reliability in rivers with dense vegetation or seasonal variations, effectively solving the problems of insufficient prediction and poor adaptability.

[0063] In one embodiment, based on physical attribute data, a search and matching process is performed through a pre-set river channel model database to obtain river channel model parameters. Then, based on these parameters, a basic river channel model is constructed, including:

[0064] S11. Based on average flow velocity data, hydraulic radius data, and river slope data, the target river is divided into river segments using preset segmentation rules to obtain multiple characteristic river segments.

[0065] S12, For the average flow velocity data, hydraulic radius data and river slope data corresponding to each characteristic river section, a multi-dimensional weighted retrieval model is used to search and match in the preset river model database to obtain the river model parameters of each characteristic river section.

[0066] S13. Based on the channel model parameters of each characteristic river segment, the Manning roughness coefficient of each characteristic river segment is calculated, and the channel model parameters and Manning roughness coefficient of each characteristic river segment are integrated to obtain the basic channel model.

[0067] For example, based on the collected physical attribute data of the target river channel (including average flow velocity, hydraulic radius, and river slope), the river channel is divided into segments using preset segmentation rules (thresholds: average flow velocity difference ≥ 0.2 m / s, hydraulic radius difference ≥ 0.5 m, or river slope difference ≥ 0.1%). For instance, the target river channel is divided into multiple characteristic segments based on the gradient of hydraulic parameter changes, ensuring that the physical characteristics within each segment are consistent. For the physical attribute data corresponding to each characteristic segment, a multi-dimensional weighted retrieval model is used (weights: average flow velocity ≥ 0.4, hydraulic radius ≥ 0.5 m, etc.). 3. (River slope 0.3) Similarity matching is performed in the preset river model database. This model calculates the river model parameters (such as riverbed material coefficient) that best match the historical database by assigning different weight values ​​to dimensions such as average flow velocity, hydraulic radius, and river slope, and obtains personalized parameters for each characteristic river segment. Based on these river model parameters, the Manning formula is applied to calculate the basic Manning roughness coefficient of each characteristic river segment. By integrating the model parameters of all characteristic river segments and the calculated Manning roughness coefficient, a structured basic river model is formed, providing a basic framework for subsequent vegetation impact analysis.

[0068] In one embodiment, based on a basic river channel model, the influence of morphological characteristic parameters on the Manning roughness coefficient of the river channel is analyzed to establish the corresponding influence relationship between morphological characteristic parameters and the Manning roughness coefficient of the river channel, including:

[0069] S21. Based on the river channel basic model, the morphological feature parameter data of each characteristic river segment is matched to the corresponding characteristic river segment according to the vegetation growth location data to obtain the morphological feature parameter dataset of each characteristic river segment.

[0070] S22, for the morphological feature parameter dataset of each characteristic river section, based on the vegetation type data, the tree height data, crown width data, leaf area data, branch density data, branch hardness data, and growth rate data are classified into the corresponding vegetation type to form a characteristic river section-vegetation type morphological parameter subset.

[0071] S23. Using the analytic hierarchy process, the tree height, crown width, leaf area, branch density and hardness, and growth rate of each characteristic river section-vegetation type morphological parameter subset are used as indicators to construct the weight judgment matrix corresponding to each characteristic river section-vegetation type morphological parameter subset.

[0072] S24, through each weight judgment matrix, calculate the influence weight values ​​of tree height dimension, crown width dimension, leaf area dimension, branch density dimension, branch hardness dimension, growth rate dimension and the Manning roughness coefficient of the corresponding characteristic river section.

[0073] S25. For each type of vegetation, in the corresponding characteristic river section, linear regression analysis was performed on tree height data, crown width data, leaf area data, branch density data, branch hardness data, and growth rate data with the Manning roughness coefficient of the corresponding characteristic river section to obtain the preliminary influence relationship curve of each single morphological parameter.

[0074] S26. Based on the influence weight values, the preliminary influence relationship curves of each single morphological parameter are weighted and fused to generate a comprehensive influence law model of multiple morphological parameters.

[0075] S27 integrates the multi-morphological parameter comprehensive influence law model of all characteristic river sections, and forms the corresponding influence relationship by constructing a mapping relationship table of morphological characteristic parameters-vegetation type-Manning roughness coefficient.

[0076] Specifically, based on the characteristic river segments defined by the river channel basic model, the collected morphological characteristic parameter data, including tree height, crown width, leaf area, branch density, branch stiffness, and growth rate, are matched to the corresponding characteristic river segments according to vegetation growth location data, forming a morphological characteristic parameter dataset for each river segment. Using vegetation type data as the classification basis, the parameters in the dataset are categorized into different vegetation types, constructing a subset of morphological parameters for each characteristic river segment and vegetation type, ensuring the systematic organization of the data. Using the analytic hierarchy process (AHP), with tree height, crown width, leaf area, branch density, branch stiffness, and growth rate as evaluation indicators, a weight judgment matrix is ​​constructed for each subset. The influence weight values ​​of each morphological dimension on the Manning roughness coefficient are calculated through the matrix, quantifying the importance of the parameters. For each vegetation type within its corresponding river segment, linear regression analysis was performed on single morphological parameters and the Manning roughness coefficient to generate preliminary influence curves. These curves were then weighted and fused based on influence weights to form a comprehensive influence model of multiple morphological parameters. By integrating the comprehensive model across all river segments, a mapping table of morphological characteristic parameters, vegetation types, and the Manning roughness coefficient was constructed, achieving a structured expression of influence relationships. Through data matching, weight analysis, and model fusion, the impact of vegetation dynamics on water flow resistance can be effectively captured.

[0077] In one embodiment, the basic river channel model is optimized and adjusted according to the corresponding influence relationship to obtain a prediction model for the Manning roughness coefficient of the river channel, including:

[0078] S31, Based on the corresponding influence relationship, generate the Manning roughness coefficient morphology correction function for each characteristic river segment;

[0079] S32, within the basic river channel model, the morphological correction function of each characteristic river segment is coupled with the Manning roughness coefficient of the corresponding characteristic river segment to obtain the optimized Manning roughness coefficient of the corresponding characteristic river segment.

[0080] S33 uses the structure of the basic river model as a framework to update the optimized Manning roughness coefficient of each characteristic river segment to the corresponding characteristic river segment, forming a prediction model for the Manning roughness coefficient of the river channel.

[0081] For example, based on the established mapping relationship between morphological feature parameters, vegetation type, and Manning roughness coefficient, a morphological correction function for the Manning roughness coefficient of each characteristic river segment can be generated. This function is achieved by constructing a dynamic response function with standardized parameters of tree height, crown width, leaf area, and growth rate as input variables, and multiplying it with a dynamic adjustment factor determined based on vegetation type and growing season. Within the basic river channel model, the morphological correction function of each characteristic river segment is coupled with the basic Manning roughness coefficient of the corresponding river segment, such as through mathematical superposition or weighted integration, to obtain the optimized Manning roughness coefficient. Using the structure of the basic river channel model as a framework, these optimized coefficients are updated to their respective characteristic river segments to form the final prediction model of the Manning roughness coefficient of the river channel. This ensures that the model can dynamically reflect the impact of vegetation morphology changes on water flow resistance, improving the practicality and accuracy of the prediction.

[0082] In one embodiment, the Manning roughness coefficient for each characteristic river segment is calculated based on the channel model parameters of each characteristic river segment, including:

[0083] S41, for each segmented characteristic river section, extract the hydraulic radius data, river slope data and average flow velocity data from the corresponding physical attribute data;

[0084] S42. Based on the extracted hydraulic radius data, river slope data, and average flow velocity data, the Manning roughness coefficient is calculated using the following formula:

[0085]

[0086] Where i is the feature river segment number, taking values ​​from 1, 2, ..., n, and n is the total number of feature river segments divided into the target river channel, R i S is the hydraulic radius, in meters (m). i V represents the riverbed slope. i The average flow velocity is expressed in m / s, n.base,i Let be the Manning roughness coefficient of the i-th characteristic river segment.

[0087] Specifically, for each segmented characteristic river section, the corresponding hydraulic radius data, channel slope data, and average flow velocity data are extracted from the physical attribute data; based on a variation of the Manning formula... Where i is the characteristic river segment number, ranging from 1 to n, n is the total number of characteristic river segments, and R i S is the hydraulic radius, in meters (m). i V represents the riverbed slope (dimensionless). i The average flow velocity is expressed in m / s. The basic Manning roughness coefficient n of the i-th characteristic river segment is directly calculated by substituting the extracted three types of data. base,i .

[0088] In one embodiment, based on the corresponding influence relationship, a morphological correction function for the Manning roughness coefficient of each characteristic river segment is generated, including:

[0089] S51, for the target characteristic river section, based on the multi-morphological parameter comprehensive influence law model in the corresponding influence relationship, constructs a dynamic response function with standardized parameters such as tree height, crown width, leaf area, branch density, branch hardness and growth rate as input variables;

[0090] S52 multiplies the dynamic response function with a dynamic adjustment factor determined based on vegetation type and growing season to generate the Manning roughness coefficient morphological correction function for the target characteristic river segment.

[0091] For example, for a target characteristic river section, a dynamic response function can be constructed using the parameter-coefficient mapping relationship established by the multi-morphological parameter comprehensive influence law model. The input variables are standardized parameters such as tree height, crown width, leaf area, branch density, branch stiffness, and growth rate. This function quantifies the nonlinear relationship between morphological parameters and roughness coefficient through polynomial fitting or machine learning methods. A dynamic adjustment factor determined based on vegetation type and growing season (the dynamic adjustment factor ranges from 0.8 to 1.2, for example, 0.8 for deciduous vegetation in winter and 1.2 for evergreen vegetation in summer) is introduced. This factor is multiplied by the dynamic response function to generate the Manning roughness coefficient morphological correction function for the target characteristic river section, thereby realizing the dynamic response to the seasonal changes in vegetation biological characteristics.

[0092] In one embodiment, the method further includes:

[0093] S61, obtain the actual Manning roughness coefficient of the target river channel;

[0094] S62, calculate the deviation between the actual Manning roughness coefficient and the predicted Manning roughness coefficient of the river channel to obtain the prediction deviation result;

[0095] S63. Based on the deviation results, adjust the influence weight values ​​of the multi-morphological parameter comprehensive influence law model to obtain the updated multi-morphological parameter comprehensive influence law model. The updated multi-morphological parameter comprehensive influence law model is used to subsequently construct a mapping relationship table of morphological feature parameters, vegetation type, and Manning roughness coefficient to form the corresponding influence relationship.

[0096] Specifically, the actual Manning roughness coefficient of the target river channel can be obtained through on-site monitoring equipment (such as an acoustic Doppler current profiler). This coefficient can be back-calculated by substituting measured flow parameters into the Manning formula. The deviation between the actual coefficient and the predicted Manning roughness coefficient of the river channel is calculated, and the deviation can be quantified using absolute error or relative error formulas. Finally, based on the deviation results, the influence weight values ​​in the multi-morphological parameter comprehensive influence law model are adjusted through iterative optimization algorithms (such as gradient descent or reapplication of the analytic hierarchy process). The weights of dimensions such as tree height, crown width, leaf area, branch density, branch stiffness, and growth rate are dynamically calibrated to make the model output closer to the actual value, generating an updated multi-morphological parameter comprehensive influence law model. This model is subsequently used to reconstruct the mapping relationship table of morphological feature parameters, vegetation type, and Manning roughness coefficient, enabling the prediction model to continuously optimize and adapt to changes in the river channel environment, thereby improving the practicality and accuracy of the method.

[0097] The aforementioned method for predicting the Manning roughness coefficient of river channels based on plant morphology characteristics provides comprehensive input for model construction by collecting physical attribute data and vegetation morphology parameter data of the target river channel. Based on the physical attribute data, a pre-set river channel model database is used for retrieval and matching to obtain river channel model parameters, and a basic river channel model is constructed. On this basic model, the influence of morphological parameter on the Manning roughness coefficient is analyzed to establish corresponding influence relationships, such as using analytic hierarchy process (AHP) and linear regression to quantify the correlation between parameters like tree height and crown width and the roughness coefficient. The basic model is then optimized and adjusted according to these influence relationships to form a prediction model, which, combined with real-time data, outputs the prediction results. This method integrates dynamic factors of vegetation morphology into the hydraulic model, overcoming the subjective limitations of traditional empirical methods, achieving accurate quantification of river channel resistance, significantly improving prediction accuracy and practicality, and is suitable for complex environments with dense vegetation or significant seasonal variations.

[0098] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0099] Based on the same inventive concept, this application also provides a device for predicting the Manning roughness coefficient of a river channel based on plant morphology characteristics, used to implement the aforementioned method for predicting the Manning roughness coefficient of a river channel based on plant morphology characteristics. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of a device for predicting the Manning roughness coefficient of a river channel based on plant morphology characteristics provided below can be found in the limitations of the method for predicting the Manning roughness coefficient of a river channel based on plant morphology characteristics described above, and will not be repeated here.

[0100] In one exemplary embodiment, such as Figure 2 As shown, a device for predicting the Manning roughness coefficient of river channels based on plant morphological characteristics is provided, comprising:

[0101] The river data acquisition module 101 is used to collect physical attribute data of the target river and morphological characteristic parameter data of the vegetation on the target river. The physical attribute data includes average water flow velocity data, hydraulic radius data and river slope data. The morphological characteristic parameter data includes tree height data, crown width data, leaf area data, branch density data, branch hardness data, growth rate data, vegetation growth location data and vegetation type data.

[0102] The river model construction module 102 is used to obtain river model parameters by searching and matching through a preset river model database based on physical attribute data, and to construct a basic river model based on the river model parameters.

[0103] The influence relationship establishment module 103 is used to establish the corresponding influence relationship between morphological feature parameters and the Manning roughness coefficient of the river channel based on the river channel basic model by analyzing the influence law of morphological feature parameters on the Manning roughness coefficient of the river channel.

[0104] The prediction model optimization module 104 is used to optimize and adjust the basic river channel model according to the corresponding influence relationship to obtain the prediction model of the Manning roughness coefficient of the river channel.

[0105] The Manning roughness coefficient prediction module 105 is used to predict the Manning roughness coefficient of a river channel based on the physical attribute data of the target river channel and the morphological characteristic parameter data of the vegetation on the target river channel, using the river channel Manning roughness coefficient prediction model to obtain the prediction result of the river channel Manning roughness coefficient.

[0106] In one embodiment, the river channel model building module 102 is further configured to:

[0107] Based on average flow velocity data, hydraulic radius data, and river slope data, the target river is divided into segments using preset segmentation rules to obtain multiple characteristic river segments.

[0108] For the average flow velocity data, hydraulic radius data and channel slope data corresponding to each characteristic river section, a multi-dimensional weighted retrieval model is used to search and match in the preset river channel model database to obtain the channel model parameters of each characteristic river section.

[0109] Based on the channel model parameters of each characteristic river segment, the Manning roughness coefficient of each characteristic river segment is calculated, and the channel model parameters and Manning roughness coefficient of each characteristic river segment are integrated to obtain the basic channel model.

[0110] In one embodiment, the influence relationship establishment module 103 is further configured to:

[0111] Based on the river channel basic model, the morphological feature parameter data of each characteristic river segment are matched to the corresponding characteristic river segment according to the vegetation growth location data to obtain the morphological feature parameter dataset of each characteristic river segment.

[0112] For the morphological feature parameter dataset of each characteristic river section, based on the vegetation type data, tree height data, crown width data, leaf area data, branch density data, branch stiffness data, and growth rate data are classified into the corresponding vegetation type to form a subset of morphological parameters of characteristic river section-vegetation type.

[0113] Using the analytic hierarchy process (AHP), a weighted judgment matrix was constructed for each subset of morphological parameters of a characteristic river section and vegetation type, with tree height, crown width, leaf area, branch density, branch stiffness, and growth rate as indicators.

[0114] The influence weights of tree height, crown width, leaf area, branch density, branch hardness, growth rate and the Manning roughness coefficient of the corresponding characteristic river section are calculated using the weight judgment matrices.

[0115] For each type of vegetation, within the corresponding characteristic river section, linear regression analysis was performed on tree height data, crown width data, leaf area data, branch density data, branch stiffness data, and growth rate data with the Manning roughness coefficient of the corresponding characteristic river section to obtain the preliminary influence relationship curves of each single morphological parameter.

[0116] Based on the influence weight values, the preliminary influence relationship curves of each individual morphological parameter are weighted and fused to generate a comprehensive influence law model of multiple morphological parameters.

[0117] A comprehensive influence model integrating multiple morphological parameters of all characteristic river sections was developed, and a corresponding influence relationship was formed by constructing a mapping table of morphological characteristic parameters, vegetation type, and Manning roughness coefficient.

[0118] In one embodiment, the prediction model optimization module 104 is further configured to:

[0119] Based on the corresponding influence relationship, a morphological correction function for the Manning roughness coefficient is generated for each characteristic river segment;

[0120] Within the basic river channel model, the morphological correction function of each characteristic river segment is coupled with the Manning roughness coefficient of the corresponding characteristic river segment to obtain the optimized Manning roughness coefficient of the corresponding characteristic river segment.

[0121] Using the structure of the basic river channel model as a framework, the optimized Manning roughness coefficients of each characteristic river segment are updated to the corresponding characteristic river segment, forming a prediction model for the Manning roughness coefficient of the river channel.

[0122] In one embodiment, the river channel model building module 102 is further configured to:

[0123] For each segmented characteristic river section, extract the hydraulic radius data, river slope data, and average flow velocity data from the corresponding physical attribute data.

[0124] Based on the extracted hydraulic radius data, river slope data, and average flow velocity data, the Manning roughness coefficient is calculated using the following formula:

[0125]

[0126] Where i is the feature river segment number, taking values ​​from 1, 2, ..., n, and n is the total number of feature river segments divided into the target river channel, R i S is the hydraulic radius, in meters (m). i V represents the riverbed slope. i The average flow velocity is expressed in m / s, n. base,i Let be the Manning roughness coefficient of the i-th characteristic river segment.

[0127] In one embodiment, the prediction model optimization module 104 is further configured to:

[0128] For the target river section, based on the comprehensive influence law model of multi-morphological parameters in the corresponding influence relationship, a dynamic response function is constructed with standardized parameters such as tree height, crown width, leaf area, branch density, branch hardness, and growth rate as input variables.

[0129] The dynamic response function is multiplied by a dynamic adjustment factor determined based on vegetation type and growing season to generate the Manning roughness coefficient morphological correction function for the target characteristic river segment.

[0130] In one embodiment, a feedback optimization module 106 is further included, for:

[0131] Obtain the actual Manning roughness coefficient of the target river channel;

[0132] The deviation between the actual Manning roughness coefficient and the predicted Manning roughness coefficient of the river channel is calculated to obtain the prediction deviation result.

[0133] Based on the deviation results, the influence weight values ​​of the multi-morphological parameter comprehensive influence law model are adjusted to obtain the updated multi-morphological parameter comprehensive influence law model. The updated multi-morphological parameter comprehensive influence law model is used to subsequently construct a mapping relationship table of morphological feature parameters, vegetation type and Manning roughness coefficient to form the corresponding influence relationship.

[0134] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the method for predicting the roughness coefficient of a river channel based on plant morphological characteristics as described above.

[0135] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0136] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The components described as separate parts 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 the modules can be selected to achieve the purpose of this disclosure according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0137] The above-described embodiments are merely illustrative of several implementation methods of the embodiments of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the embodiments of this application, and these modifications and improvements all fall within the protection scope of the embodiments of this application.

Claims

1. A method for predicting the Manning roughness coefficient of river channels based on plant morphological characteristics, characterized in that, The method includes: Collect physical attribute data of the target river channel and morphological characteristic parameter data of the vegetation on the target river channel. The physical attribute data includes average water flow velocity data, hydraulic radius data and river channel slope data. The morphological characteristic parameter data includes tree height data, crown width data, leaf area data, branch density data, branch stiffness data, growth rate data, vegetation growth location data and vegetation type data. Based on the physical attribute data, the river model parameters are obtained by searching and matching through a preset river model database, and a basic river model is constructed according to the river model parameters. Based on the aforementioned river channel basic model, the influence of the morphological feature parameters on the Manning roughness coefficient of the river channel is analyzed, and the corresponding influence relationship between the morphological feature parameters and the Manning roughness coefficient of the river channel is established. Based on the corresponding influence relationship, the basic river channel model is optimized and adjusted to obtain the Manning roughness coefficient prediction model for the river channel. Based on the physical attribute data of the target river channel and the morphological characteristic parameter data of the vegetation on the target river channel, the Manning roughness coefficient prediction model of the river channel is used to make predictions and obtain the prediction results of the Manning roughness coefficient of the river channel.

2. The method according to claim 1, characterized in that, The process of obtaining river model parameters by searching and matching through a preset river model database based on the physical attribute data, and constructing a basic river model based on the river model parameters, includes: Based on the average flow velocity data, hydraulic radius data, and river slope data, the target river is divided into river segments using a preset segmentation rule to obtain multiple characteristic river segments. For the average flow velocity data, hydraulic radius data and channel slope data corresponding to each characteristic river section, a multi-dimensional weighted retrieval model is used to search and match in the preset river channel model database to obtain the channel model parameters for each characteristic river section. Based on the channel model parameters of each characteristic river segment, the Manning roughness coefficient of each characteristic river segment is calculated, and the channel model parameters and Manning roughness coefficient of each characteristic river segment are integrated to obtain the basic channel model.

3. The method according to claim 2, characterized in that, Based on the aforementioned river channel model, the influence of the morphological characteristic parameters on the Manning roughness coefficient of the river channel is analyzed to establish the corresponding influence relationship between the morphological characteristic parameters and the Manning roughness coefficient of the river channel, including: Based on the river channel basic model, the morphological feature parameter data is matched to the corresponding feature river segment according to the vegetation growth location data to obtain the morphological feature parameter dataset of each feature river segment. For the morphological feature parameter dataset of each characteristic river section, based on the vegetation type data, the tree height data, crown width data, leaf area data, branch density data, branch hardness data and growth rate data are classified into the corresponding vegetation type to form a characteristic river section-vegetation type morphological parameter subset. Using the analytic hierarchy process, the weight judgment matrix corresponding to the morphological parameter subset of each characteristic river section-vegetation type is constructed with tree height, crown width, leaf area, branch density, branch hardness and growth rate as indicators. Using the weight judgment matrices described above, calculate the influence weight values ​​of tree height dimension, crown width dimension, leaf area dimension, branch density dimension, branch hardness dimension and growth rate dimension on the Manning roughness coefficient of the corresponding characteristic river section. For each type of vegetation, within the corresponding characteristic river section, the tree height data, crown width data, leaf area data, branch density data, branch stiffness data, and growth rate data are respectively subjected to linear regression analysis with the Manning roughness coefficient of the corresponding characteristic river section to obtain the preliminary influence relationship curves of each single morphological parameter. Based on the aforementioned influence weight values, the preliminary influence relationship curves of each individual morphological parameter are weighted and fused to generate a comprehensive influence law model of multiple morphological parameters. A comprehensive influence model integrating multiple morphological parameters of all characteristic river sections is established, and the corresponding influence relationship is formed by constructing a mapping relationship table of morphological characteristic parameters, vegetation type, and Manning roughness coefficient.

4. The method according to claim 3, characterized in that, The optimization and adjustment of the river channel basic model based on the corresponding influence relationship yields a river channel Manning roughness coefficient prediction model, including: Based on the corresponding influence relationship, a morphological correction function for the Manning roughness coefficient is generated for each characteristic river segment; Within the basic river channel model, the morphological correction function of each characteristic river segment is coupled with the Manning roughness coefficient of the corresponding characteristic river segment to obtain the optimized Manning roughness coefficient of the corresponding characteristic river segment. Using the structure of the river channel basic model as a framework, the optimized Manning roughness coefficients of each characteristic river segment are updated to the corresponding characteristic river segment, forming the river channel Manning roughness coefficient prediction model.

5. The method according to claim 2, characterized in that, The Manning roughness coefficient for each characteristic river segment is calculated based on the river channel model parameters of each characteristic river segment, including: For each segmented characteristic river section, extract the hydraulic radius data, river slope data, and average flow velocity data from the corresponding physical attribute data. Based on the extracted hydraulic radius data, river slope data, and average flow velocity data, the Manning roughness coefficient is calculated using the following formula: Where i is the feature river segment number, taking values ​​from 1, 2, ..., n, and n is the total number of feature river segments divided into the target river channel, R i S is the hydraulic radius, in meters (m). i V represents the riverbed slope. i The average flow velocity is expressed in m / s, n. base,i Let be the Manning roughness coefficient of the i-th characteristic river segment.

6. The method according to claim 4, characterized in that, The generation of the Manning roughness coefficient morphology correction function for each characteristic river segment based on the corresponding influence relationship includes: For the target river section, based on the multi-morphological parameter comprehensive influence law model in the corresponding influence relationship, a dynamic response function is constructed with standardized parameters as input variables, such as tree height, crown width, leaf area, branch density, branch hardness and growth rate. The dynamic response function is multiplied by a dynamic adjustment factor determined based on vegetation type and growing season to generate the Manning roughness coefficient morphological correction function for the target characteristic river segment.

7. The method according to claim 3, characterized in that, The method further includes: Obtain the actual Manning roughness coefficient of the target river channel; The deviation between the actual Manning roughness coefficient and the predicted Manning roughness coefficient of the river channel is calculated to obtain the prediction deviation result; Based on the deviation results, the influence weight values ​​of the multi-morphological parameter comprehensive influence law model are adjusted to obtain an updated multi-morphological parameter comprehensive influence law model. The updated multi-morphological parameter comprehensive influence law model is used to subsequently construct a mapping relationship table of morphological feature parameters, vegetation type, and Manning roughness coefficient to form the corresponding influence relationship.

8. A device for predicting the Manning roughness coefficient of river channels based on plant morphological characteristics, characterized in that, The device includes: The river channel data acquisition module is used to collect physical attribute data of the target river channel and morphological characteristic parameter data of the vegetation on the target river channel. The physical attribute data includes average water flow velocity data, hydraulic radius data, and river channel slope data. The morphological characteristic parameter data includes tree height data, crown width data, leaf area data, branch density data, branch stiffness data, growth rate data, vegetation growth location data, and vegetation type data. The river channel model construction module is used to obtain river channel model parameters by searching and matching through a preset river channel model database based on the physical attribute data, and to construct a basic river channel model based on the river channel model parameters. The influence relationship establishment module is used to establish the corresponding influence relationship between the morphological feature parameters and the Manning roughness coefficient of the river channel based on the river channel basic model by analyzing the influence law of the morphological feature parameters on the Manning roughness coefficient of the river channel. The prediction model optimization module is used to optimize and adjust the river channel basic model according to the corresponding influence relationship to obtain the prediction model of the Manning roughness coefficient of the river channel. The Manning roughness coefficient prediction module is used to predict the Manning roughness coefficient of a river channel based on the physical attribute data of the target river channel and the morphological characteristic parameter data of the vegetation on the target river channel, using the river channel Manning roughness coefficient prediction model to obtain the prediction result of the river channel Manning roughness coefficient.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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