A Method for Selecting Drainage Facilities for Railways in Mountainous Areas Based on Hydrological Spatial Site Selection Analysis Model

Abstract

This invention provides a method for site selection of drainage facilities for mountain railways based on a hydrological spatial site selection analysis model, relating to the field of runoff pollution control technology. The method involves preprocessing collected data to obtain environmental data for mountain railways; constructing a three-dimensional hydrological spatial site selection analysis model using a topographic factor analysis layer, a runoff calculation layer, a runoff point screening layer, a spatial clustering analysis layer, a geometric identification classification layer, and a drainage facility recommendation layer; and using this model to perform spatial clustering dimensionality reduction analysis and surface runoff calculation on the mountain railway environmental data to obtain the site selection results for drainage facilities, thus completing the site selection for mountain railway drainage facilities. This invention solves the problem of accurate site selection for drainage facilities along mountain railways.

Description

Technical Field

[0001] This manual relates to the field of runoff pollution control technology, and in particular to a method for selecting the location of railway drainage facilities in mountainous areas based on a hydrological spatial location analysis model. Background Technology

[0002] Mountainous terrain is complex and climate is variable. Catchment areas are generally steep and have rapid flow, often resulting in heavy rainfall causing large amounts of rainwater to quickly concentrate in narrow mountain catchments. This leads to waterlogging that cannot be drained in time, causing roadbed erosion, landslides, and other water-related damage problems. Current drainage designs for mountain railways suffer from insufficient hydrological analysis, inadequate drainage system planning, and inadequate facilities. These problems directly contribute to low drainage efficiency and frequent water-related disasters. The formation mechanism of water damage to mountain railways is highly complex, with its spatiotemporal distribution controlled by multiple factors, including topography, geology, hydrology, and meteorology. Traditional drainage site selection methods mainly rely on manual topographic map interpretation and on-site surveys. However, mountainous terrain is often complex and varied. While manual surveys can determine facility locations, they are sometimes inefficient and lack precision, making them unsuitable for large-scale projects and difficult to store and interpret. Summary of the Invention

[0003] In view of the above-mentioned shortcomings in the prior art, the present invention provides a method for site selection of drainage facilities for railways in mountainous areas based on a hydrological spatial site selection analysis model, which solves the problem of the difficulty in accurately selecting the site for drainage facilities for railways in mountainous areas.

[0004] To achieve the aforementioned objectives, the technical solution adopted by this invention is: a site selection method for railway drainage facilities in mountainous areas based on a hydrological spatial site selection analysis model, comprising:

[0005] S1: Preprocess the collected data to obtain mountain railway environmental data;

[0006] S2: A three-dimensional hydrological spatial site selection analysis model is constructed by utilizing the topographic factor analysis layer, runoff calculation layer, dumping point screening layer, spatial cluster analysis layer, geometric identification and classification layer, and drainage facility recommendation layer.

[0007] S3: Using the three-dimensional hydrological spatial site selection analysis model, spatial clustering dimensionality reduction analysis and surface runoff calculation are performed on the mountain railway environmental data to obtain the site selection results of the mountain railway drainage facilities, thus completing the site selection of the mountain railway drainage facilities.

[0008] The beneficial effects of this invention are as follows: This invention provides a method for selecting the location of drainage facilities for mountain railways based on a hydrological spatial location analysis model. By preprocessing the collected data, mountain railway environmental data is obtained. Using the three-dimensional hydrological spatial location analysis model, spatial clustering dimensionality reduction analysis and surface runoff calculation are performed on the mountain railway environmental data to obtain the location results of drainage facilities for mountain railways. This improves the accuracy of the location selection of drainage facilities for mountain railways and its applicability under complex terrain conditions; reduces the probability of water accumulation in the railway subgrade, and significantly enhances the railway's resistance to water damage.

[0009] Further, S1 includes:

[0010] The digital elevation model data, land use type data, soil hydrological condition information and meteorological and rainfall data are converted into the same coordinate system and spatial resolution. Boundary gaps and overlapping areas are repaired through topological consistency checks to obtain the processed digital elevation model data, land use type data, soil hydrological condition information and meteorological and rainfall data.

[0011] The local window minimum filtering algorithm is used to filter the areas obscured by forest buildings in the digital elevation model data, and the exposed slope areas are smoothed and optimized. The elevation difference threshold method is used to remove abnormal elevation disturbances, and the optimized digital elevation model data is obtained.

[0012] Spatial distribution verification was performed on soil use type data, and joint classification was performed using soil texture information to obtain joint classification data.

[0013] Based on meteorological and rainfall data, an image is constructed using an inverse distance weighting method to obtain a regional rainfall spatial distribution map. Among them, the digital elevation model data, land use type data, soil hydrological condition information, and meteorological and rainfall data are collected data, while the optimized digital elevation model data, the joint classification data, and the regional rainfall spatial distribution map are mountain railway environmental data.

[0014] By performing coordinate unification, spatial correction, filtering optimization, and joint classification processing on multi-source heterogeneous data, high-precision mountain railway environmental data was constructed, providing a unified and accurate data foundation for subsequent hydrological analysis. In particular, the optimized DEM and joint classification data improved the accuracy of identifying micro-topography and land types, significantly enhancing the reliability and adaptability of the model input.

[0015] Further, S3 includes:

[0016] S310: Using the terrain factor analysis layer, terrain features are extracted from the optimized digital elevation model data to obtain the basic terrain factor map;

[0017] S320: Using the runoff calculation layer, based on the topographic basic factor map, joint classification data, soil hydrological condition information and regional rainfall spatial distribution map, slope correction is introduced for calculation to obtain the catchment unit, runoff data and initial dumping point information;

[0018] S330: Using the flood point screening layer, flood safety buffer analysis and feature removal are performed on the initial flood point information and railway alignment vector data to obtain a set of threatening flood points;

[0019] S340: Using a spatial clustering analysis layer, clustering and adaptive optimization are performed on the set of threatening spill points to obtain the spatial features of the clusters;

[0020] S350: Using the geometric recognition classification layer, principal component analysis is performed on the spatial features of the clusters, and the cluster morphology classification results are obtained by setting the variance explanation rate threshold;

[0021] S360: Using the drainage facility recommendation layer, based on the cluster morphology classification results, the runoff data, and the roadbed data, the standard parameters are back-calculated using the Manning equation to obtain the site selection results for drainage facilities on mountain railways, thus completing the site selection for drainage facilities on mountain railways; among them, the topographic factor analysis layer, runoff calculation layer, spillway screening layer, spatial cluster analysis layer, geometric identification classification layer, and drainage facility recommendation layer belong to the three-dimensional hydrological spatial site selection analysis model.

[0022] By introducing a multi-layered structure of a three-dimensional hydrological spatial analysis model, the entire process from terrain extraction and runoff simulation to facility recommendation is automated. By incorporating multi-scale analysis methods such as slope correction, spatial clustering, and principal component identification, the scientific rigor and applicability of drainage facility deployment under complex mountainous conditions are effectively improved.

[0023] Furthermore, the expression for the runoff data is:

[0024] ;

[0025] ;

[0026] ;

[0027] ;

[0028] in, Represents runoff data. This represents the total rainfall. This represents the dynamic initial loss coefficient, i.e., the coefficient at location. The initial damage rate from rainfall at the location, Represents the x-coordinate of the grid cell. Represents the ordinate of the grid cell. Indicates potential water storage capacity. This indicates the revised general conditions. Indicates humid conditions. Indicates drought conditions. Represents the natural constant. Indicates local slope. Represents the normalized vegetation index. This indicates that the expression for dynamic initial loss ratio is used to limit the range to ensure the rationality of its physical meaning and value range.

[0029] By constructing an improved SCS-CN runoff model that incorporates slope and vegetation factors, dynamic and accurate estimation of runoff under different geomorphic and surface conditions is achieved. This effectively overcomes the limitations of traditional fixed CN value models in mountainous applications and provides a more responsive flow input for facility design.

[0030] Furthermore, the expression for the actual buffer distance of the set of threatening dumping points is:

[0031] ;

[0032] in, Indicates the actual buffer distance. This indicates the threshold for safe distance for railway flood control. This represents the maximum slope within the study area. Indicates the local slope.

[0033] By incorporating the topographic slope factor into the calculation of the spillway buffer zone and constructing a dynamic buffer distance model, the selected threatening spillway points are made to better reflect the water flow risk distribution under real topographic conditions, thereby improving the physical rationality of the screening results and the safety boundary guarantee capability for engineering applications.

[0034] Further, S340 includes:

[0035] Using a spatial clustering analysis layer, the distance from each point to its smallest neighbor is calculated, and the distances are sorted in ascending order to form a K-distance curve. The abrupt gradient at the inflection point is selected as the neighborhood radius threshold for adaptive optimization.

[0036] Using the adaptively optimized neighborhood radius threshold, the set of threatening dumping points is clustered to obtain the clustering results;

[0037] The clustering results are then filtered a second time to remove invalid clusters located in tunnel and bridge structural regions, thus obtaining the spatial characteristics of the clusters.

[0038] By using the abrupt change point of the K-distance curve as the adaptive clustering radius threshold, the optimal clustering parameters are automatically determined. Combined with the structural shielding screening mechanism of the engineering section, the rationality of the spatial distribution of the clustering results is effectively improved, ensuring that the facility deployment focuses on the real risk catchment area and reducing the false identification rate.

[0039] Further, S350 includes:

[0040] Using a geometric recognition classification layer, principal component analysis is performed on the spatial features of the clusters to obtain the variance contribution rate of each principal component;

[0041] Based on the variance explanation rate threshold, the variance contribution rate of the principal components is analyzed to obtain linear and nonlinear clusters;

[0042] The least squares method is used to fit the points within the linear cluster to a straight line, extract the principal axis direction angle, and combine the railway alignment and drainage slope layout requirements to determine the layout location and direction of the linear drainage ditch.

[0043] For nonlinear clusters, the lowest elevation point is selected as the recommended location for the facility by traversing the point set within the cluster, thus obtaining the preferred centralized drainage facility; wherein, the layout location and direction of the linear drainage ditch and the centralized drainage facility belong to the cluster morphology classification result.

[0044] By using PCA principal component analysis to distinguish linear and nonlinear morphologies among clusters, and then matching different types of drainage facilities, a "cluster geometry recognition-facility matching" mechanism is constructed, achieving automated linkage from morphology identification to facility recommendation. This strategy can accurately guide the layout location and form of drainage ditches and culverts, improving the scientific nature and operability of engineering design. Attached Figure Description

[0045] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein:

[0046] Figure 1 This is an exemplary flowchart illustrating a method for selecting the location of drainage facilities for railways in mountainous areas based on a hydrological spatial location analysis model, according to some embodiments of this specification.

[0047] Figure 2 This is an exemplary schematic diagram of a digital elevation model of the study area according to some embodiments of this specification;

[0048] Figure 3(a) is an exemplary schematic diagram of surface runoff extraction results according to some embodiments of this specification;

[0049] Figure 3(b) is an exemplary schematic diagram of the pouring point extraction results shown according to some embodiments of this specification;

[0050] Figure 4 This is an exemplary schematic diagram showing the results of the study area catchment unit division according to some embodiments of this specification;

[0051] Figure 5 This is an exemplary schematic diagram showing the selection of pouring point ranges according to some embodiments of this specification;

[0052] Figure 6 This is an exemplary schematic diagram of a K-distance curve according to some embodiments of this specification;

[0053] Figure 7(a) is an exemplary schematic diagram illustrating the DBSACN clustering principle according to some embodiments of this specification;

[0054] Figure 7(b) is an exemplary schematic diagram illustrating the DBSACN clustering principle according to some embodiments of this specification;

[0055] Figure 8 This is an exemplary schematic diagram illustrating the principle of principal component analysis according to some embodiments of this specification;

[0056] Figure 9 This is an exemplary schematic diagram of railway alignment elevation according to some embodiments of this specification;

[0057] Figure 10 This is an exemplary schematic diagram showing the results of the pouring point screening according to some embodiments of this specification;

[0058] Figure 11 This is an exemplary schematic diagram of K-Distance, a clustering method for pouring points, as shown in some embodiments of this specification.

[0059] Figure 12 This is an exemplary schematic diagram illustrating the statistical analysis of pouring point clustering-PCA results according to some embodiments of this specification;

[0060] Figure 13 This is an exemplary schematic diagram of the results of pouring point clustering-PCA according to some embodiments of this specification;

[0061] Figure 14 This is an exemplary schematic diagram showing the predicted site selection results for drainage facilities according to some embodiments of this specification;

[0062] Figure 15 This is an exemplary schematic diagram comparing the predicted results shown in some embodiments of this specification with the actual location of the drainage facilities. Detailed Implementation

[0063] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0064] Example

[0065] Figure 1 This is an exemplary flowchart illustrating a method for selecting the location of drainage facilities for railways in mountainous areas based on a hydrological spatial site selection analysis model, according to some embodiments of this specification. Figure 1 As shown, the process includes the following steps. In some embodiments, the process may be executed by a processor.

[0066] S1: Preprocess the collected data to obtain mountain railway environmental data.

[0067] The collected data refers to environmental data related to the study area. For example, the collected data may include digital elevation model data, land use type data, soil hydrological condition information, railway alignment vector data, roadbed data, and meteorological and rainfall data. Specific collected data are shown in Table 1.

[0068] Digital elevation model data refers to data related to the digital elevation model (DEM) of the study area.

[0069] In some embodiments, the processor can use airborne laser (LiDAR) to acquire digital elevation model data of the study area environment.

[0070] Land use type data is data that categorizes land use.

[0071] In some embodiments, the processor can utilize remote sensing imagery and multi-period land use remote sensing to obtain a monitoring dataset as land use type data.

[0072] Soil hydrological conditions information is data related to the soil quality and hydrological quality of the study area.

[0073] In some embodiments, the processor can collect digitized results of soil surveys as soil hydrological condition information.

[0074] Railway alignment vector data is data on the spatial location and geometric attributes of railway lines expressed in vector form.

[0075] In some embodiments, the processor can acquire data provided by the engineering design department to obtain railway line position vector data.

[0076] Roadbed data is data that characterizes the spatial location and morphological features of the foundation structure along a railway line, including the roadbed's planar boundaries, elevation information, slope gradient, width, cut and fill attributes (such as fill areas and cut areas), reinforcement types, and structural characteristics.

[0077] Meteorological precipitation data are data related to the climate and rainfall conditions of the study area.

[0078] In some embodiments, the processor can collect information recorded by meteorological stations in and around the study area to obtain meteorological rainfall data.

[0079]

[0080] Mountain railway environmental data is preprocessed collected data. For example, mountain railway environmental data may include optimized digital elevation model data, joint classification data, regional rainfall spatial distribution maps, soil hydrological condition information, railway alignment vector data, and roadbed data.

[0081] In some embodiments, the processor can convert digital elevation model data, land use type data, soil hydrological condition information, and meteorological and rainfall data into the same coordinate system and spatial resolution. It then repairs boundary gaps and overlapping areas through topological consistency checks to obtain processed digital elevation model data, land use type data, soil hydrological condition information, and meteorological and rainfall data. A local window minimum filtering algorithm is used to filter areas obscured by forest buildings in the digital elevation model data, and bare slope areas are smoothed and optimized. Anomalies in elevation are removed using an elevation difference threshold method to obtain optimized digital elevation model data. Spatial distribution verification is performed on the soil use type data, and joint classification is conducted using soil texture information to obtain joint classification data. Based on the meteorological and rainfall data, an inverse distance weighting method is used to construct an image, resulting in a regional rainfall spatial distribution map.

[0082] In some embodiments, the processor can uniformly convert DEM, land use, soil hydrological properties and meteorological precipitation data into the same coordinate system (CGCS2000) and spatial resolution (0.2 m), and repair boundary gaps and overlapping areas through topological consistency checks to ensure that there are no dislocations or gaps between model input layers, thereby achieving high-precision coupling of multi-source data.

[0083] Optimized digital elevation model data is digital elevation model data after filtering and smoothing.

[0084] In some embodiments, the processor can perform slope filtering and anomaly removal processing on the digital elevation model data. For anomalies that may be generated in the airborne LiDAR point cloud in forest areas and building-occupied areas, a local window minimum filtering algorithm is introduced to smooth and optimize the exposed slope area. Anomalies are removed by using the elevation difference threshold method, thereby improving the accuracy of DEM in restoring micro-topographic structures and obtaining optimized digital elevation model data.

[0085] Joint classification data is data on the combined classification of soil use type data and soil texture information.

[0086] In some embodiments, the processor can verify the spatial distribution of land use type data such as forest land, bare land, and cultivated land based on remote sensing interpretation results and combined with field survey sample data of the study area. It can also introduce soil texture information from the soil census database and jointly classify it with land use types to improve the accuracy of CN value assignment in the subsequent SCS-CN model.

[0087] A regional rainfall spatial distribution map is image data showing the distribution of rainfall in each sub-region of a target area.

[0088] In some embodiments, the processor can incorporate daily rainfall data from multiple meteorological stations in and around the study area based on meteorological rainfall data, and construct a regional rainfall spatial distribution map using the inverse distance weighting (IDW) method, thereby transitioning from point observations to area data and providing more continuous and detailed rainfall input for surface runoff simulation.

[0089] In some embodiments, high-precision DEM data with a resolution of 0.2 m is generated from point clouds acquired by an airborne laser LiDAR, such as... Figure 2 As shown, the DEM data covers a projected area of ​​approximately 6.1 km². 2 It is a small watershed.

[0090] Based on high-precision DEM and machine learning algorithms, combined with DBSCAN density clustering and PCA principal component analysis, the site selection of 14 culverts and 2 drainage ditches along the mountain railway was successfully predicted. The location of the culverts matched the actual engineering layout with a 92% accuracy, verifying the applicability of the method under complex terrain conditions.

[0091] S2: A three-dimensional hydrological spatial site selection analysis model is constructed by utilizing the topographic factor analysis layer, runoff calculation layer, dumping point screening layer, spatial cluster analysis layer, geometric identification classification layer, and drainage facility recommendation layer.

[0092] A three-dimensional hydrological spatial location analysis model is as follows. For example, a three-dimensional hydrological spatial location analysis model may include a topographic factor analysis layer, a runoff calculation layer, a spillway selection layer, a spatial cluster analysis layer, a geometric identification and classification layer, and a drainage facility recommendation layer. The output of the topographic factor analysis layer serves as the input to the runoff calculation layer; the output of the runoff calculation layer serves as the input to the spillway selection layer and the drainage facility recommendation layer; the output of the spillway selection layer serves as the input to the spatial cluster analysis layer; the output of the spatial cluster analysis layer serves as the input to the geometric identification and classification layer; the output of the geometric identification and classification layer serves as the input to the drainage facility recommendation layer; and the output of the drainage facility recommendation layer serves as the final output of the three-dimensional hydrological spatial location analysis model.

[0093] The terrain factor analysis layer is used to extract terrain features from the optimized digital elevation model data to obtain the basic terrain factor map.

[0094] The runoff calculation layer is used to calculate the catchment units, runoff data, and initial dumping point information based on the topographic basic factor map, joint classification data, soil hydrological condition information, and regional rainfall spatial distribution map, with the introduction of slope correction.

[0095] In some embodiments, the processor can extract basic hydrological features from high-precision DEM data in ArcGIS software, mainly including eight steps: depression filling analysis, flow direction extraction, flow statistics, flow classification, river network grading, raster river network vectorization, pouring point drawing, and catchment area delineation. This process is based on the D8 flow direction algorithm, which determines the flow direction and calculates the runoff capacity by analyzing the elevation relationship between each raster cell and its eight neighboring raster cells. Combined with flow statistics, it further extracts the surface runoff distribution. Once the surface runoff is obtained, the endpoints of the runoff, i.e., pouring points, can be extracted. These points are often the convergence areas of the water flow and represent the main direction of precipitation flow towards low-lying areas or drainage systems, reflecting the relationship between water flow and parameters such as slope and surface type. By determining one or more pouring points (the outlet locations of the watershed), the boundaries of the entire watershed and its sub-watersheds can be automatically delineated, thus obtaining the catchment units within the study area.

[0096] In some embodiments, the processor can use ArcGIS hydrological analysis to comprehensively consider the influence of topographic factors such as slope, elevation, and ridge-valley lines on the direction of water flow and the convergence path, extracting information on surface runoff and spillway points, as well as the division of catchment areas. As shown in Figure 3(a), the surface runoff in the study area exhibits a network distribution pattern along both sides of the central black railway line, converging from higher elevation areas to lower elevation areas; the runoff distribution is mostly parallel or perpendicular to the railway line, indicating that the surface slope mainly changes in the direction perpendicular to the railway line; the runoff network is denser on the north side with more branches, while the runoff network is relatively sparser on the south side with fewer branches, consistent with the overall topography of the study area, which is lower in the northeast and higher in the southwest. The spillway point extraction results are shown in Figure 3(b), which are usually located at the end of tributaries or in areas where the runoff converges with the main stream. In areas with dense tributaries, the distribution density of spillway points is higher, indicating that these areas are concentrated scouring points of water flow or concentrated points in low-lying areas. Catchment units are divided based on the surface runoff and spillway point extraction results. A total of 23 catchment units are divided in the study area, such as Figure 4 As shown, the maximum area is 0.749 km². 2 Minimum area: 0.046 km² 2 The average area is 0.161 km². 2 The maximum slope is 39°, the minimum slope is 12°, and the average slope is 27°.

[0097] The flood point filtering layer is used to perform flood safety buffer analysis and feature removal on the initial flood point information and railway alignment vector data to obtain a set of threatening flood points.

[0098] The spatial clustering analysis layer is used to perform clustering and adaptive optimization on the set of threatening spill points to obtain the spatial features of the clusters.

[0099] The geometric recognition classification layer is used to perform principal component analysis on the spatial features of the clusters and obtain the cluster morphology classification results by setting the variance explanation rate threshold.

[0100] The drainage facility recommendation layer is used to obtain the site selection results for mountain railway drainage facilities by back-calculating the normative parameters through the Manning equation based on the cluster morphology classification results, the runoff data and the roadbed data, thus completing the site selection for mountain railway drainage facilities.

[0101] S3: Using the three-dimensional hydrological spatial site selection analysis model, spatial clustering dimensionality reduction analysis and surface runoff calculation are performed on the mountain railway environmental data to obtain the site selection results of the mountain railway drainage facilities, thus completing the site selection of the mountain railway drainage facilities.

[0102] The site selection results for drainage facilities along mountain railways are the chosen locations for these facilities.

[0103] In some embodiments, the processor may implement S3 based on the following steps.

[0104] S310: Using the terrain factor analysis layer, terrain features are extracted from the optimized digital elevation model data to obtain the basic terrain factor map.

[0105] The topographic basic factor map is a spatial distribution layer that reflects the basic topographic features of the study area, extracted from optimized digital elevation model (DEM) data. It mainly includes slope map, aspect map, flow direction map, ridge and valley line map, etc., and is used to depict the surface water flow path, confluence trend and geomorphic structure features.

[0106] In some embodiments, the processor can extract topographic features such as slope, flow direction, and ridges from optimized digital elevation model data to obtain a topographic base factor map. For example, based on high-precision DEM data, the D8 (Deterministic Eight-neighbor) algorithm is used to analyze the elevation difference between each pixel and its surrounding eight pixels, calculating the main flow direction of each raster cell. On this basis, the upstream flow received by each pixel is further statistically analyzed to generate a flow accumulation map, and the surface runoff path is extracted according to a set threshold.

[0107] S320: Using the runoff calculation layer, based on the aforementioned topographic basic factor map, joint classification data, soil hydrological condition information, and regional rainfall spatial distribution map, slope correction is introduced for calculation to obtain the catchment unit, runoff data, and initial dumping point information.

[0108] A catchment unit is a regional unit that includes the point where water flows converge.

[0109] Runoff data is data that reflects the magnitude of water flow.

[0110] In some embodiments, surface runoff estimation is based on a three-dimensional hydrological spatial location analysis model (an improved adaptive enhanced SCS-CN hydrological model). This model, building upon the traditional SCS-CN framework, incorporates several optimization strategies, including slope correction, soil texture adjustment, and dynamic initial loss mechanisms, to improve its accuracy and adaptability under complex mountainous terrain conditions. The model comprehensively considers multiple spatial factors such as soil permeability, land cover type, slope factor, and rainfall intensity, and, combined with regional geological survey results and recommended parameter tables, constructs a multi-factor-driven runoff response calculation method.

[0111] In some embodiments, the expression for runoff data can be:

[0112] ;

[0113] ;

[0114] ;

[0115] ;

[0116] in, Represents runoff data. This represents the total rainfall. This represents the dynamic initial loss coefficient, i.e., the coefficient at location. The initial damage rate from rainfall at the location, Represents the x-coordinate of the grid cell. Represents the ordinate of the grid cell. Indicates potential water storage capacity. This indicates the revised general conditions. Indicates humid conditions. Indicates drought conditions. Represents the natural constant. Indicates local slope. Represents the normalized vegetation index. This indicates that the expression for dynamic initial loss ratio is used to limit the range to ensure the rationality of its physical meaning and value range.

[0117] `clip(…, 0, 0.5)` is a cutoff function that restricts the result to the interval [0, 0.5]. If the value of η calculated by the linear function is less than 0, it is forced to be 0; if it is greater than 0.5, it is set to 0.5. This avoids extreme outliers and ensures that η is always within a reasonable range.

[0118] In some embodiments, the processor establishes a dynamic correction model for the CN value to characterize the combined impact of different land types, vegetation cover, and slope on runoff generation capacity. The NDVI (Normalized Difference Vegetation Index) is introduced as an indicator of land cover, and a two-factor adjustment mechanism is constructed, combined with the slope effect to form an adaptive CN value estimation formula. Considering that the fixed initial loss ratio (generally 0.2) in the traditional SCS-CN model is insufficient to reflect the differences in rainwater interception capacity of different land types (such as forest, bare land, and cultivated land) at the beginning of rainfall, this paper introduces a dynamic initial loss coefficient mechanism to differentiate the runoff generation response and construct an adaptive runoff generation model. This model forms a natural transition between forest, grassland, bare land, and hardened surfaces, effectively improving the accuracy of runoff generation estimation in response to land type changes.

[0119] Using the methods described above, the model can calculate surface runoff depth under complex terrain, providing key input parameters for subsequent drainage facility flow design. Combined with the spatiotemporal distribution data of catchment unit boundaries and rainfall intensity extracted from the high-precision DEM of the study area, the model ultimately achieves refined calculation of runoff in each sub-basin, ensuring that the results highly match the hydrological characteristics along the railway line.

[0120] In some embodiments, the processor can set the soil thickness in the study area to 0.5-1m; the bedrock is dolomite interbedded with shale, with well-developed fissures and smooth drainage; the bedrock in the study area is mainly dolomite, and according to the given empirical value

[20] , plus data and field investigation information, the rock layer permeability coefficient can be obtained as 4e-7cm / s, about 4μm / s. In summary, the soil hydrological classification in the study area is Class C. Combining the land use data interpreted from remote sensing images (75% forest land, 15% bare land, 10% cultivated land), soil texture category and slope and NDVI value extracted from high-resolution DEM, firstly, the CN value corresponding to the dry and wet conditions of each type is set according to the SCS-CN parameter recommendation table, and then, the slope + NDVI joint correction model constructed in this invention is used to spatially correct the CN value, improve the perception accuracy of the CN value for different surface runoff capacity, and obtain the corrected CN value according to formula (3). Surface runoff is calculated using the improved SCS-CN model (formula (4)). The results showed that among the 23 catchment units in the study area, the runoff ranged from 2.1 × 10³ m³ to 3.4 × 10⁴ m³, with an average runoff of 7.4 × 10³ m³. The maximum runoff occurred in the northern catchment unit (number 1), with a catchment area of ​​0.749 km², an average slope of 35°, and a CN value of 75 after slope correction. The minimum runoff was located in the southern catchment unit (number 13), with a catchment area of ​​0.046 km², an average slope of 28°, and a corrected CN value of 75. The spatial distribution of runoff showed a trend of higher runoff in the north and lower runoff in the south, with runoff depth ranging from 45 mm to 46 mm.

[0121] The initial dumping point information is the location information of the dumping point formed by the watershed outlet.

[0122] S330: Using the flood point screening layer, flood safety buffer analysis and feature removal are performed on the initial flood point information and railway alignment vector data to obtain a set of threatening flood points.

[0123] The threatening dumping point set is the set of dumping points within the buffer distance range.

[0124] In some embodiments, the processor can extract neighboring spillway points through spatial topology filtering and perform distance correction by combining topographic slope factors, retaining potential threat points with significant hydraulic connectivity, such as... Figure 5As shown, to ensure that the spatial distribution of the selected runoff points accurately represents the dynamic interaction between mountain runoff and railway alignment, railway construction site design drawings are overlaid, and runoff points in special sections such as tunnel entrances / exits and bridge piers are manually removed. The filtered point set will serve as input data for DBSCAN density clustering, providing spatial constraints for subsequent density clustering.

[0125] In some embodiments, the expression for the actual buffer distance of the threatening dumping point set can be:

[0126] ;

[0127] in, Indicates the actual buffer distance. This indicates the threshold for safe distance for railway flood control. This represents the maximum slope within the study area. Indicates the local slope.

[0128] S340: Using a spatial clustering analysis layer, clustering and adaptive optimization are performed on the set of threatening spill points to obtain the spatial features of the clusters.

[0129] Cluster spatial features are features that reflect the spatial location information of clusters.

[0130] In some embodiments, the processor can utilize a spatial clustering analysis layer to calculate the distance from each point to its smallest neighboring point and its nearest neighbor, arrange the distances in ascending order to form a K-distance curve, and select the abrupt gradient at the inflection point as an adaptively optimized neighborhood radius threshold. Using the adaptively optimized neighborhood radius threshold, the set of threatening spill points is clustered to obtain clustering results. The clustering results are then subjected to secondary screening to remove invalid clusters located in tunnel and bridge structural areas, thereby obtaining the spatial characteristics of the clusters.

[0131] The adaptively optimized neighborhood radius threshold is the threshold of the clustered neighborhood radius after adaptive optimization.

[0132] In some embodiments, the processor can adaptively estimate the neighborhood radius threshold (eps value) by introducing the K-distance curve inflection point method: specifically, the distance from each point to its minimum number of neighboring points (min_samples) is first calculated, and the distances are sorted in ascending order to form a K-distance curve. The abrupt gradient at the inflection point is selected as the optimal eps threshold. This method can effectively distinguish between high-density water flow convergence clusters and sparse isolated points, improving the physical rationality of the clustering results, such as... Figure 6 As shown.

[0133] The clustering results reflect the clustering of threat dumping points.

[0134] In some embodiments, the processor can utilize the DBSCAN clustering method to cluster and adaptively optimize the set of threatening spill points to obtain the spatial characteristics of the clusters. During the clustering process, once a point's neighborhood contains a sufficient number of other spill points, it is marked as a core point, triggering the cluster expansion process and gradually absorbing boundary points to construct the cluster structure, as shown in Figures 7(a) and 7(b). To improve engineering adaptability, the clustering results are further filtered by combining the DEM elevation profile data along the railway line and the design alignment layer, automatically eliminating invalid clusters located in structural areas such as tunnels and bridges, ensuring that the drainage facility site selection is concentrated only in the actual catchment threat area.

[0135] S350: Using the geometric recognition classification layer, principal component analysis is performed on the spatial features of the clusters, and the cluster morphology classification results are obtained by setting the variance explanation rate threshold.

[0136] The variance explained rate threshold is a threshold used to distinguish between linear and nonlinear clusters.

[0137] Cluster morphology classification results reflect the spatial characteristics of clusters and the drainage layout. For example, cluster morphology classification results can include the location and orientation of linear drainage ditches and centralized drainage facilities.

[0138] In some embodiments, the processor may utilize a geometric recognition classification layer to perform principal component analysis on the spatial features of the clusters to obtain the variance contribution rate of each principal component.

[0139] Based on the variance explanation rate threshold, the variance contribution rate of the principal components is analyzed to obtain linear and nonlinear clusters;

[0140] The least squares method is used to fit the points within the linear cluster to a straight line, extract the principal axis direction angle, and combine the railway alignment and drainage slope layout requirements to determine the layout location and direction of the linear drainage ditch.

[0141] For nonlinear clusters, the lowest elevation point is selected as the recommended location for the facility by traversing the point set within the cluster, thus obtaining the preferred centralized drainage facility; wherein, the layout location and direction of the linear drainage ditch and the centralized drainage facility belong to the cluster morphology classification result.

[0142] The variance contribution rate of a principal component is the ratio of the contribution of each principal component to the variance.

[0143] In some embodiments, such as Figure 9 and Figure 10As shown, to analyze the geometry of each cluster, the processor uses Principal Component Analysis (PCA) to perform dimensionality reduction analysis on the clustered clusters. PCA extracts key features by projecting data onto the direction of maximum variance and calculates the variance explained by the principal components. Unlike traditional PCA, which is usually used for global dimensionality reduction analysis, this paper limits PCA to a local scope within each cluster to construct the point set covariance matrix. If the variance explained by the first principal component is higher than a certain proportion, it indicates that most of the variation in the cluster is concentrated in the direction of that principal component, and the cluster's morphology tends to be linear; conversely, if the proportion is low, it indicates that the cluster has large variation in multiple directions, and the cluster exhibits a nonlinear structure. Using this method, a correspondence between geometric cluster types and drainage facility layout is further established: Linear clusters: The least squares method is used to fit a straight line to the point set within the cluster, extracting the principal axis direction angle, and combining the railway alignment and drainage slope layout requirements to determine the layout location and direction of the linear drainage ditch. Nonlinear clusters: Considering their multi-directional concentrated flow characteristics, centralized drainage facilities, such as culverts or drop structures, are prioritized for matching. The lowest elevation point within the cluster is selected as the recommended location for the facility by traversing the point set within the cluster, ensuring that water flow is preferentially diverted to the lowest point and reducing the risk of accumulation. For example... Figure 8 As shown. This method, through a dual discrimination mechanism of "geometric orientation recognition + drainage structure matching", not only improves the accuracy of cluster morphology determination, but also directly transforms spatial analysis results into facility layout parameters, opening up the engineering implementation path from spatial clustering to drainage facility selection, and effectively improving the rationality and operability of drainage site selection decisions in mountainous areas.

[0144] In some embodiments, the processor can plot a K-Distance diagram (e.g., by analyzing the distance changes from each pouring point to its three nearest neighbors) by analyzing the distance changes from each pouring point to its three nearest neighbors. Figure 11 As shown in the figure), the optimal distance parameter was determined to be the ordinate corresponding to the inflection point in the figure, i.e., eps = 20.41. Clustering was performed using the DBSCAN algorithm. Figure 13 The clustering results of spill points near the railway are displayed, with each color representing a cluster, resulting in a total of 16 clusters. Principal component analysis (PCA) was used to identify each cluster. Based on a dynamic threshold method, using a first principal component variance contribution rate >83% as the cutoff, 7 linear clusters and 5 nonlinear clusters (e.g., ...) were identified. Figure 12 As shown (excluding single-point clusters), this includes 5 linear clusters that intersect the railway at large angles (see...). Figure 13 (a) and two linear clusters intersecting the railway at small angles (see [reference]). Figure 13(b)). For linear clusters intersecting at small angles, the least squares method is used to fit straight lines, accurately simulating the location of drainage ditches to ensure smooth drainage and conformity to actual terrain requirements; while linear clusters intersecting at large angles, due to their spatial distribution characteristics, do not conform to the setting rules of roadbed drainage ditches and are classified as nonlinear clusters for processing. For clusters including multi-point clusters ( Figure 13 (c) in the middle and single-point clusters ( Figure 13 For the nonlinear clusters in (b) of the diagram, the lowest elevation point within the cluster is uniformly selected as the location for the culverts. The locations of drainage ditches and culverts along the railway line are determined, with a total of 14 culverts and 2 drainage ditches to be constructed.

[0145] S360: Using the drainage facility recommendation layer, based on the cluster morphology classification results, the runoff data and the roadbed data, the standard parameters are back-calculated using the Manning equation to obtain the site selection results for drainage facilities on mountain railways, thus completing the site selection for drainage facilities on mountain railways.

[0146] In some embodiments, the processor can calculate surface runoff using the SCS-CN model based on the cluster morphology classification results, the runoff data, and the roadbed data to obtain the surface runoff depth. Combining this with the previously defined catchment area, multiplying the two yields the surface runoff for each catchment unit. This allows for the calculation of culvert and drainage ditch dimensions based on the surface runoff. According to the clustering and PCA analysis results, a total of 14 culvert locations and 2 drainage ditch locations were identified in the study area. Figure 14 As shown. Based on the surface runoff data, the allowable flow velocity of the concrete masonry (4m / s) is taken as the critical flow velocity. At the same time, the three cases of no blockage, 1 / 3 blockage, and 2 / 3 blockage are considered to calculate the design flow of each drainage facility for flow design. (1) Culvert design: The lowest elevation point in the nonlinear cluster is selected as the culvert location, and the maximum runoff of the catchment unit is taken as the design flow. For example, the surface runoff of the catchment unit (number 4) under the condition of 100mm / h rainfall is 2.02m³ / s. The corresponding culvert size can be designed according to the Manning equation. At the same time, economic benefits are considered under the condition of ensuring safety. For example, a reinforced concrete circular pipe culvert is adopted, with a culvert diameter of 1.2 m and a flow capacity of 4.52 m³ / s, which can meet the water flow under the condition of 2 / 3 blockage of the culvert. (2) Drainage ditch design: For the linear cluster (angle with the railway line <30°), the center line of the drainage ditch is fitted and the longitudinal slope is calculated. Taking the catchment unit (No. 16) as an example, under the condition of 100 mm / h rainfall, the surface runoff is 2.55 m³ / s. According to Manning's equation, a trapezoidal cross-section drainage ditch (bottom width 0.8 m, depth 0.6 m, side slope ratio 1:1.5) can be designed with a flow capacity of 4.08 m³ / s, which can also meet the water flow requirements under 2 / 3 congestion conditions.

[0147] In some embodiments, the project site actually has 15 culverts and 6 drainage ditches, with runoff in the remaining areas drained through bridge drainage holes and tunnel gutter systems. Comparative analysis showed that the culvert locations predicted by the model were highly consistent with the actual site conditions, achieving an accuracy rate of 92%. Figure 15 As shown, the prediction error mainly stems from merging two adjacent culverts. However, in actual engineering, this area has two independent slope-type debris flow gullies, requiring separate culverts to ensure smooth flow of debris flow and prevent gully blockage. In contrast, the predicted drainage ditch layout differs from the actual layout, with only two locations matching the prediction. The remaining four implemented drainage ditches are not reflected in the prediction model, presumably due to the accuracy of local micro-topographic feature extraction and the setting of rainfall return period parameters. Comparing historical flood damage cases, the design flow of existing drainage facilities in the study area is generally 40% lower than the calculated value, and the new site covers 85% of the high-risk catchment area. Through GIS spatial overlay analysis, the optimized drainage system can reduce the probability of water accumulation in the railway subgrade and significantly improve the railway's resistance to flood damage. Furthermore, the design results match the field survey data by 92%, verifying the engineering applicability of the site selection method based on DEM and cluster analysis.

[0148] The main error in culvert prediction stems from the merging of gullies on adjacent slopes, which fails to reflect the actual independent layout requirements. The deviation in drainage ditch prediction is related to insufficient accuracy in the extraction of local micro-topography. In addition, in reality, drainage ditches are usually distributed on both sides of culverts to facilitate the timely drainage of water from these ditches. Future research could build upon the culvert prediction results and arrange drainage ditches sequentially according to this distribution pattern, further combining roadbed slope data to improve the drainage system.

[0149] In some embodiments of this specification, a method for selecting the location of drainage facilities for mountain railways based on a hydrological spatial location analysis model is provided. By preprocessing the collected data, mountain railway environmental data is obtained. Using the three-dimensional hydrological spatial location analysis model, spatial clustering dimensionality reduction analysis and surface runoff calculation are performed on the mountain railway environmental data to obtain the location results of drainage facilities for mountain railways. This improves the accuracy of the location selection of drainage facilities for mountain railways and its applicability under complex terrain conditions; reduces the probability of water accumulation in the railway subgrade; and significantly enhances the railway's resistance to water damage.

Claims

1. A method for selecting the location of drainage facilities for railways in mountainous areas based on a hydrological spatial location analysis model, characterized in that, include: S1: Preprocess the collected data to obtain mountain railway environmental data; S2: A three-dimensional hydrological spatial site selection analysis model is constructed by utilizing the topographic factor analysis layer, runoff calculation layer, dumping point screening layer, spatial cluster analysis layer, geometric identification and classification layer, and drainage facility recommendation layer. S3: Using the aforementioned three-dimensional hydrological spatial site selection analysis model, spatial clustering and dimensionality reduction analysis and surface runoff calculation are performed on the mountain railway environmental data to obtain the site selection results for mountain railway drainage facilities, thus completing the site selection for mountain railway drainage facilities; specifically including: S310: Using the terrain factor analysis layer, terrain features are extracted from the optimized digital elevation model data to obtain the basic terrain factor map; S320: Using the runoff calculation layer, based on the topographic basic factor map, joint classification data, soil hydrological condition information and regional rainfall spatial distribution map, slope correction is introduced for calculation to obtain the catchment unit, runoff data and initial dumping point information; The expression for the runoff data is: ； ； ； ； in, Represents runoff data. This represents the total rainfall. This represents the dynamic initial loss coefficient, i.e., the coefficient at location. The initial damage rate from rainfall at the location, Represents the x-coordinate of the grid cell. Represents the ordinate of the grid cell. Indicates potential water storage capacity. This indicates the revised general conditions. Indicates humid conditions. Indicates drought conditions. Represents the natural constant. Indicates local slope. Represents the normalized vegetation index. This indicates that the expression for the dynamic initial loss ratio is used to limit the range to ensure the rationality of its physical meaning and value range; S330: Using the flood point screening layer, flood safety buffer analysis and feature removal are performed on the initial flood point information and railway alignment vector data to obtain a set of threatening flood points; S340: Using a spatial clustering analysis layer, clustering and adaptive optimization are performed on the set of threatening spill points to obtain the spatial features of the clusters; S350: Using the geometric recognition classification layer, principal component analysis is performed on the spatial features of the clusters, and the cluster morphology classification results are obtained by setting the variance explanation rate threshold; S360: Using the drainage facility recommendation layer, based on the cluster morphology classification results, the runoff data, and the roadbed data, the standard parameters are back-calculated using the Manning equation to obtain the site selection results for drainage facilities on mountain railways, thus completing the site selection for drainage facilities on mountain railways; among them, the topographic factor analysis layer, runoff calculation layer, spillway screening layer, spatial cluster analysis layer, geometric identification classification layer, and drainage facility recommendation layer belong to the three-dimensional hydrological spatial site selection analysis model.

2. The method for site selection of drainage facilities for mountain railways based on a hydrological spatial site selection analysis model according to claim 1, characterized in that, S1 includes: The digital elevation model data, land use type data, soil hydrological condition information and meteorological and precipitation data are converted into the same coordinate system and spatial resolution. Boundary gaps and overlapping areas are repaired through topological consistency checks to obtain processed digital elevation model data, processed land use type data, processed soil hydrological condition information and processed meteorological and precipitation data. The local window minimum filtering algorithm is used to filter the areas shaded by forest buildings in the processed digital elevation model data, and the exposed slope areas are smoothed and optimized. The elevation difference threshold method is used to remove abnormal elevation disturbances, and the optimized digital elevation model data is obtained. Spatial distribution verification was performed on the processed land use type data, and joint classification was performed using soil texture information to obtain joint classification data. Based on the processed meteorological and rainfall data, an image is constructed using the inverse distance weighting method to obtain a regional rainfall spatial distribution map. Among them, the digital elevation model data, land use type data, soil hydrological condition information, and meteorological and rainfall data belong to the collected data, while the optimized digital elevation model data, the joint classification data, and the regional rainfall spatial distribution map belong to the mountain railway environment data.

3. The method for site selection of drainage facilities for mountain railways based on a hydrological spatial site selection analysis model according to claim 1, characterized in that, The expression for the actual buffer distance of the set of threatening dumping points is: ； in, Indicates the actual buffer distance. This indicates the threshold for safe distance for railway flood control. This represents the maximum slope within the study area. Indicates the local slope.

4. The method for site selection of drainage facilities for mountain railways based on a hydrological spatial site selection analysis model according to claim 1, characterized in that, S340 includes: Using a spatial clustering analysis layer, the distance from each point in the threatening dumping point set to its smallest neighbor is calculated. After being sorted in ascending order, a K-distance curve is formed. The abrupt gradient at the inflection point is selected as the neighborhood radius threshold for adaptive optimization. Using the adaptively optimized neighborhood radius threshold, the set of threatening dumping points is clustered to obtain the clustering results; The clustering results are then filtered a second time to remove invalid clusters located in tunnel and bridge structural regions, thus obtaining the spatial characteristics of the clusters.

5. The method for site selection of drainage facilities for mountain railways based on a hydrological spatial site selection analysis model according to claim 1, characterized in that, The S350 includes: Using a geometric recognition classification layer, principal component analysis is performed on the spatial features of the clusters to obtain the variance contribution rate of each principal component; Based on the variance explanation rate threshold, the variance contribution rate of the principal components is analyzed to obtain linear and nonlinear clusters; The least squares method is used to fit the points within the linear cluster to a straight line, extract the principal axis direction angle, and combine the railway alignment and drainage slope layout requirements to determine the layout location and direction of the linear drainage ditch. For nonlinear clusters, the lowest elevation point is selected as the recommended location for the facility by traversing the point set within the cluster, thus obtaining the preferred centralized drainage facility; wherein, the layout location and direction of the linear drainage ditch and the centralized drainage facility belong to the cluster morphology classification result.

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