A reservoir site selection method based on multi-source data, a storage medium and a program product

Abstract

The present application relates to the technical field of water conservancy information, and particularly relates to a reservoir site selection method based on multi-source data, a storage medium and a program product, the method comprising: obtaining digital elevation model data and constraint layer data of a research area; performing flow direction analysis and flow accumulation calculation; determining a minimum catchment area threshold and generating a candidate dam site point set; eliminating conflicting dam sites; calculating dam body parameters, reservoir capacity parameters with a correction term and submerged range sensitive element occupation; comprehensively scoring and sorting the candidate dam sites; the present application integrates the links of filling, improving multi-flow direction calculation, dynamic threshold candidate dam site identification, rigid constraint parallel filtering, reservoir capacity quantification with a correction term and entropy weight-TOPSIS coupling sorting in a unified process, so that the dam site census of the whole basin can be automatically and continuously operated.

Description

Technical Field

[0001] This invention relates to the field of information technology for water conservancy projects, specifically to a method for reservoir site selection based on multi-source data, a storage medium, and a program product. Background Technology

[0002] In small and medium-sized river basin planning, the selection of reservoir dam sites is usually completed manually by hydraulic engineers through comparison of paper or vector data such as topographic maps, remote sensing images, and land use maps. The data processing and map overlay for a single candidate dam site often takes several days. Comparing dozens of candidate points for a medium-sized river basin takes even longer, making it difficult to meet the needs of large-scale river basin planning to complete a comprehensive survey of dam sites for the entire basin within a short period.

[0003] The conclusions drawn from manual site selection rely heavily on the personal experience of engineers. Different engineers often arrive at significantly different recommendations for the same watershed, and there is a lack of reproducible quantitative evidence. For key indicators such as reservoir capacity, inundation area, and farmland occupation area, traditional methods often use approximate estimations based on cross-sectional or grid methods, resulting in significant discrepancies between the estimated results and the actual engineering quantities.

[0004] In recent years, GIS technology has been introduced, but it still has some shortcomings: First, the mainstream GIS workflow is usually based on interactive operation by engineers. The processing of rigid constraints such as ecological protection red lines and permanent basic farmland requires manual overlay of layers, which leads to conflicts in candidate schemes during subsequent review. Second, if accurate data is required, it is usually necessary to digitize it into vector files and then perform clipping and area statistics in the GIS platform, which is time-consuming. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a reservoir site selection method, storage medium, and program product based on multi-source data. Through algorithms, it automatically completes the entire process from topographic depression identification, reservoir capacity calculation, constraint filtering to dam body parameter (dam height, dam length, reservoir capacity curve) calculation, transforming traditional qualitative analysis relying on human experience into data-driven quantitative decision-making.

[0006] This invention is achieved through the following technical solution:

[0007] A reservoir site selection method based on multi-source data includes:

[0008] Acquire digital elevation model data and constraint layer data for the study area;

[0009] After filling depressions in the digital elevation model data, flow direction analysis and flow accumulation calculation are performed sequentially to obtain a flow accumulation grid.

[0010] The minimum catchment area threshold is determined based on the preset total runoff threshold, and a set of candidate dam sites is generated based on the minimum catchment area threshold and the flow accumulation grid.

[0011] For each of the candidate dam sites, the corresponding reservoir basin range is extracted;

[0012] Spatial overlay analysis is performed on the reservoir basin range and the constraint layer data to eliminate candidate dam site points that conflict with the constraint area defined by the constraint layer data, resulting in a filtered set of candidate dam site points.

[0013] For each candidate dam site after filtering, dam body parameters and reservoir capacity parameters are calculated, and the occupancy of sensitive elements within the inundation range is determined based on the spatial relationship between water level and topography.

[0014] Based on the dam body parameters, reservoir capacity parameters, and the occupancy status of the sensitive elements, the filtered candidate dam sites are comprehensively scored and ranked, and a recommended dam site scheme is output.

[0015] Optionally, the flow direction analysis employs a multi-flow direction algorithm, which calculates the flow distribution coefficient from the central grid to each downslope neighboring grid based on the slope between the central grid and the downslope neighboring grids, combined with a slope correction factor. The flow allocation coefficient The calculation formula is: ,in, For all downhill neighborhood grid sets, ; Center grid and the first The slope between neighboring grid cells of a downslope; Center grid and the first The slope between neighboring grid cells of a downslope; Assign an index to the flow direction; The slope correction factor is mentioned above.

[0016] Optionally, the initial planning threshold is dynamically adjusted based on the vegetation cover factor and runoff variation coefficient of the study area to determine the minimum catchment area threshold. The calculation formula is: ,in, The initial planning threshold; This is the vegetation correction factor; This is the runoff variability correction factor; The vegetation cover factor for the study area; denoted as the runoff variation coefficient for the study area.

[0017] Optionally, the calculation of the reservoir capacity parameters incorporates grid confidence weights, siltation correction terms, and elevation measurement error correction terms for correction.

[0018] The grid confidence weights are determined based on the accuracy of the digital elevation model data and the terrain complexity.

[0019] The sedimentation correction term is determined based on the multi-year average sediment transport modulus of the study area;

[0020] The elevation measurement error correction term is determined based on the measurement accuracy of the digital elevation model data in different elevation ranges.

[0021] Optionally, water level The corrected storage capacity below The calculation formula is: ,in, This is the lowest elevation of the reservoir bottom; Water level The corresponding submerged grid set; For the first The grid confidence weights for each grid cell; For the first The planar area of ​​each grid cell; Calculate the thickness for each layer; The reservoir capacity loss rate due to siltation; This is a correction item for elevation measurement errors.

[0022] Optionally, the filtered candidate dam sites are ranked and scored comprehensively, including:

[0023] An evaluation index system is constructed, which includes positive and negative indicators. The positive indicators include at least one of reservoir capacity satisfaction rate and reservoir capacity per unit dam height, and the negative indicators include at least one of ecological red line occupation area, basic farmland occupation area, relocated population, dam body engineering investment and inundation loss compensation cost.

[0024] After normalizing each evaluation index, the entropy weight-TOPSIS coupled scoring model is used to comprehensively score each candidate dam site.

[0025] Optionally, when the entropy-weighted TOPSIS coupled scoring model performs scoring, the initial weights are determined by the information entropy of each evaluation indicator, and an indicator conflict adjustment coefficient is introduced to correct the initial weights. Adjusted weights of each evaluation indicator The calculation formula is: ,in, This represents the total number of evaluation indicators in the aforementioned evaluation indicator system. ; For the first Information entropy of each evaluation indicator; This is the conflict adjustment coefficient for the aforementioned indicator; For the first The conflict coefficient between an evaluation indicator and other evaluation indicators;

[0026] No. The overall score of each candidate dam site The calculation formula is: ,in, For the first The Euclidean distance between the index normalized vectors of each candidate dam site and the positive ideal solution; For the first The Euclidean distance between the index normalized vectors of each candidate dam site and the negative ideal solution; For the first The candidate dam site was in the... Normalized values ​​for each evaluation indicator and The first The best and worst values ​​of each evaluation index among all candidate dam sites.

[0027] Optionally, before performing a comprehensive scoring and ranking on the filtered candidate dam sites, the filtered candidate dam sites are divided into small dam sites and medium-to-large dam sites according to the reservoir capacity parameters.

[0028] For the small dam site, if there is permanent basic farmland occupation or ecological red line occupation within the corresponding reservoir basin area, the small dam site will be judged as unqualified and eliminated.

[0029] For the medium and large dam sites, the comprehensive scoring and ranking will be used.

[0030] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the reservoir site selection method based on multi-source data as described above.

[0031] A computer program product includes a computer program / instructions that, when executed by a processor, implement the reservoir site selection method based on multi-source data as described above.

[0032] Compared with the prior art, the present invention has the following features and beneficial effects:

[0033] This invention integrates several steps, including filling depressions, improving multi-flow direction calculation, identifying candidate dam sites with dynamic thresholds, parallel filtering with rigid constraints, reservoir capacity quantification with correction terms, and entropy weight-TOPSIS coupled sorting, into a unified process. This transforms the execution chain of the whole basin dam site survey from manual, intermittent work to automatic, continuous algorithmic operation. Attached Figure Description

[0034] The accompanying drawings illustrate exemplary embodiments of the present invention and, together with the description thereof, serve to explain the principles of the invention. These drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, but do not constitute a limitation on the embodiments of the present invention.

[0035] Figure 1 This is a flowchart illustrating the reservoir site selection method based on multi-source data provided in an embodiment of the present invention.

[0036] Figure 2 This is a flowchart illustrating the comprehensive scoring and ranking steps provided in an embodiment of the present invention. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0038] It should also be noted that, for ease of description, only the parts relevant to the present invention are shown in the accompanying drawings.

[0039] Where there is no conflict, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0040] Example 1

[0041] This embodiment provides a reservoir site selection method based on multi-source data, such as... Figure 1 As shown, the overall execution chain consists of data acquisition, terrain preprocessing, candidate dam site identification, reservoir basin extraction, constraint filtering, parameter calculation, and comprehensive scoring and ranking, which are sequentially linked as follows:

[0042] Step 101 (Data Acquisition): Acquire digital elevation model (DEM) data and constraint layer data for the study area. DEM data can be obtained through aerial photogrammetry, airborne lidar measurement, spaceborne stereo mapping, and other methods. Commonly used raster resolutions include different accuracy levels such as 30 meters, 12.5 meters, and 5 meters.

[0043] Constraint layer data refers to spatial vector or raster layers that constitute legal or engineering prohibitions on the implementation of reservoir projects. Typical constraint layers include ecological protection red line layers, permanent basic farmland layers, geological disaster risk zone layers, nature reserve map layers, and cultural relic protection unit red line layers.

[0044] Step 102 (terrain preprocessing): After filling depressions in the digital elevation model data, flow direction analysis and flow accumulation calculation are performed sequentially to obtain the flow accumulation raster.

[0045] Depression filling refers to raising the elevation of pseudo-depression grids in digital elevation model data to the same height as the lowest outlet grid to ensure the continuity of the water flow path.

[0046] The flow accumulation grid refers to the grid data corresponding to the cumulative number of upstream catchment grids for each grid. The value of the flow accumulation grid directly reflects the catchment area of ​​that grid.

[0047] Step 103 (Candidate Dam Site Identification): Determine the minimum catchment area threshold based on the preset total runoff threshold, and generate a set of candidate dam site points based on the minimum catchment area threshold and the flow accumulation grid.

[0048] The total runoff threshold refers to the annual runoff threshold that matches the planned target reservoir capacity, and is determined by the planner based on downstream water demand or the project task book.

[0049] The minimum catchment area threshold is calculated based on the average runoff depth of the watershed over many years: the minimum catchment area required to generate that runoff volume can be obtained by dividing the total required runoff volume by the average runoff depth over many years.

[0050] Grid points in the flow accumulation grid that have a value greater than or equal to the number of grid points corresponding to the minimum catchment area threshold are identified as candidate dam sites that meet the runoff conditions. All grid points that meet the conditions constitute the set of candidate dam sites.

[0051] Step 104 (Reservoir Basin Extraction): For each candidate dam site, extract the corresponding reservoir basin range.

[0052] The reservoir basin range refers to the area of ​​the water body covered topographically when a dam is built at the candidate dam site and the water level rises to the elevation corresponding to the planned reservoir capacity.

[0053] The extraction method can employ either the watershed algorithm or the upstream tracing algorithm based on the flow direction grid. Starting from the candidate dam site, all grids flowing into that point are traced in reverse along the flow direction grid, and the resulting catchment area is used as the initial estimate of the reservoir basin's extent.

[0054] Step 105 (Constraint Filtering): Spatial overlay analysis is performed on the reservoir basin range and constraint layer data to eliminate candidate dam sites that conflict with the constraint area defined by the constraint layer data, thus obtaining a filtered set of candidate dam sites.

[0055] Spatial overlay analysis can be achieved based on raster algebra operations or vector intersection operations. Multiple constraint layers can participate in the operation in parallel, and a single operation can complete the filtering and judgment of all candidate dam sites.

[0056] Step 106 (parameter calculation): For each candidate dam site after filtering, calculate the dam body parameters and reservoir capacity parameters, and determine the occupancy of sensitive elements within the inundation range based on the spatial relationship between water level and topography.

[0057] Dam body parameters include parameters used to describe the geometric characteristics of the dam body, such as maximum dam height, dam crest elevation, and dam length.

[0058] The reservoir capacity parameters include the reservoir capacity values ​​at different water levels, and the reservoir capacity values ​​at different water levels constitute the water level-reservoir capacity curve.

[0059] The occupation of sensitive elements includes the area of ​​basic farmland within the inundation area, the area of ​​ecological red lines, the number of settlements, and the number of relocated people estimated from the number of settlements.

[0060] Maximum dam height dam crest elevation and Dam Chief The calculation formulas are as follows: ,in, To pass the total runoff threshold The water level corresponding to the maximum reservoir capacity can be obtained by referring to the water level-reservoir capacity curve, and the unit is meters. This represents the lowest elevation of the riverbed at the dam site, in meters (m). For safety, the superelevation is determined based on the dam type, and the safe superelevation range is 1.5 to 3.0 meters, for example, 1.5 meters, 2.2 meters, and 3.0 meters. The design wave height is determined based on the meteorological characteristics of the watershed, and the range of the design wave height is 0.5 to 2.0 m, for example, 0.5 m, 1.2 m, and 2.0 m; and These are the plane coordinates of the two intersection points obtained when the water surface line intersects the dam site section at an elevation equal to the dam crest elevation.

[0061] Step 107 (Comprehensive Scoring and Ranking): Based on the dam body parameters, reservoir capacity parameters, and the occupancy of sensitive elements, the filtered candidate dam sites are comprehensively scored and ranked, and a recommended dam site scheme is output.

[0062] The comprehensive scoring results are ranked from highest to lowest, and the candidate dam site with the highest score is selected as the recommended dam site for this planning task.

[0063] It should be noted that the order of the steps described above is for ease of writing and does not mean that the steps must be executed in strict order during algorithm implementation. For example, the constraint filtering step and the parameter calculation step can be executed in parallel for different candidate dam sites, and the reservoir basin extraction step can also be performed synchronously with the intermediate results of flow accumulation calculation.

[0064] Example 2

[0065] This embodiment further explains the implementation of flow direction analysis in step 102, based on embodiment one.

[0066] The flow direction analysis employs a multi-flow direction algorithm. This algorithm calculates the flow distribution coefficient from the central grid to each downslope neighboring grid based on the slope between the central grid and each downslope neighboring grid, combined with a slope correction factor. Flow distribution coefficient The calculation formula is: ,in, The set of all downslope neighborhood grid cells. ; Center grid and the first The slope between neighboring grid cells of a downslope; Center grid and the first The slope between neighboring grid cells of a downslope; Assign an index to the flow direction; For the corresponding number The slope correction factor for the downslope neighborhood grid is determined based on the relationship between the center grid and the first downslope grid. Slope between neighboring grid cells Sure.

[0067] Flow distribution index The value range is from 1.2 to 2.0, for example, it can be 1.2, 1.6, or 2.0. The closer the value is to 2.0, the more the flow tends to be concentrated and distributed to the neighborhood grid with the steepest slope; The closer the value is to 1.2, the more uniform the distribution of flow among the neighboring grids on each downhill slope.

[0068] Slope correction factor According to the following rules The value is determined: when When the slope is greater than 30°, the slope correction factor The value range is from 1.1 to 1.3, for example, it can be 1.1, 1.2, or 1.3; when When the slope is less than 5°, the slope correction factor The value range is from 0.7 to 0.9, for example, it can be 0.7, 0.8, or 0.9; when When the slope is between 5° and 30°, the slope correction factor Determined by linear interpolation, that is, between the values ​​at the two ends mentioned above, according to... The magnitude transitions linearly.

[0069] When a certain downhill direction is steep (i.e.) If the slope is relatively large, appropriately increase the flow allocation weight for that direction; when a certain downhill direction is relatively gentle (i.e., (If the flow is relatively small), appropriately reduce the flow allocation weight for that direction.

[0070] When there are multiple downhill neighborhoods in the central grid, the multi-flow algorithm will distribute the flow proportionally to each downhill neighborhood according to the above formula, making the flow propagation process on the terrain closer to the diffusion behavior of actual water flow.

[0071] Example 3

[0072] This embodiment further explains the method for determining the minimum catchment area threshold in step 103, based on embodiment one.

[0073] The minimum catchment area threshold is obtained by dynamically adjusting the initial planning threshold. This dynamic adjustment is based on the vegetation cover factor and runoff variability coefficient of the study area. The calculation formula is: ,in, The initial planning threshold is in km² and is directly estimated by the planner based on the planned target reservoir capacity divided by the multi-year average runoff depth. This is the vegetation correction factor; This is the runoff variability correction factor; The vegetation cover factor for the study area; denoted as the runoff variation coefficient for the study area.

[0074] Vegetation correction factor The value ranges from 0.1 to 0.3, for example, it can be 0.1, 0.2, or 0.3. Runoff variability correction coefficient. The value range is from 0.2 to 0.4, for example, it can be 0.2, 0.3, or 0.4. and The specific rules for determining the values ​​can be found in the relevant provisions of the "Specifications for Hydrological Calculation of Water Conservancy and Hydropower Projects" SL278-2002.

[0075] Vegetation Coverage Factor The value is obtained by performing spatial statistics on the land use map of the study area. It is equal to the ratio of the sum of the areas of vegetation types such as forest, grassland and cultivated land to the total area of ​​the study area.

[0076] Runoff variability coefficient It is equal to the ratio of the standard deviation of the multi-year runoff series in the study area to the multi-year average runoff, reflecting the degree of interannual fluctuation of watershed runoff.

[0077] Example 4

[0078] This embodiment further explains the calculation of the storage capacity parameter in step 106, based on embodiment one.

[0079] The calculation of reservoir capacity parameters incorporates raster confidence weights, sedimentation correction terms, and elevation measurement error correction terms for correction. The raster confidence weights are determined based on the accuracy of the digital elevation model data and the complexity of the terrain. The sedimentation correction terms are determined based on the multi-year average sediment transport modulus of the study area. The elevation measurement error correction terms are determined based on the measurement accuracy of the digital elevation model data in different altitude ranges.

[0080] water level The corrected storage capacity below The calculation formula is: ,in, This is the lowest elevation of the reservoir bottom, and ; Stratified water levels Depend on Start with The step size increases sequentially to ; Calculate the thickness for each layer; For stratified water levels The corresponding submerged raster set, i.e., the digital elevation model data with elevations less than or equal to... A grid set; For the first The planar area of ​​each grid cell; For the first The grid confidence weights of each grid cell; This represents the reservoir capacity loss rate due to siltation, and the corresponding siltation correction term. For corresponding stratified water levels Elevation measurement error correction item.

[0081] Grid confidence weights The value range is from 0.7 to 1.0, for example, it can be 0.7, 0.85, or 1.0.

[0082] The specific value selection rule is: dynamically select a value of 0.7~1.0 based on the accuracy of the digital elevation model data source and the terrain complexity.

[0083] Siltation and reservoir capacity loss rate The value range is from 0.05 to 0.2, for example, it can be 0.05, 0.12, or 0.2. The specific value is determined based on the multi-year average sediment transport modulus of the watershed where the study area is located. For watersheds with a larger sediment transport modulus, Values ​​close to the upper limit; for watersheds with smaller sediment transport moduli, The value is close to the lower limit.

[0084] Elevation measurement error correction item The value range is from 0.98 to 1.02, for example, it can be 0.98, 1.00, or 1.02.

[0085] The specific value selection rule is as follows: for high-altitude areas where the measurement accuracy of digital elevation model data is relatively low. The value should be appropriately deviated from 1.00; for medium and low altitude ranges with high measurement accuracy, The value is close to 1.00.

[0086] Layered thickness calculation The value is usually taken from 0.1m to 1.0m, for example, 0.1m, 0.5m, 1.0m; The smaller the value, the smoother the calculated water level-reservoir capacity curve, but the greater the computational workload.

[0087] Calculate the water level using the formula described above. This allows us to obtain the water level-reservoir capacity curve for the candidate dam site.

[0088] Compared to the crude logic of traditional static volume calculation which is based solely on the simple summation of elevations from a fixed digital elevation model, this model introduces a multi-correction mechanism that incorporates the dynamic influence of sediment deposition and the probability correction of elevation errors, effectively reducing reservoir capacity calculation errors.

[0089] Example 5

[0090] This embodiment is based on Embodiment 1, such as... Figure 2 The implementation of step 107 will be further explained as shown below.

[0091] The filtered candidate dam sites are comprehensively scored and ranked, specifically including: constructing an evaluation index system, which includes positive and negative indicators. Positive indicators include at least one of reservoir capacity satisfaction rate and reservoir capacity per unit dam height, while negative indicators include at least one of ecological red line occupation area, basic farmland occupation area, relocated population, dam engineering investment, and inundation loss compensation cost; after normalizing each evaluation indicator, the entropy weight-TOPSIS coupled scoring model is used to comprehensively score each candidate dam site.

[0092] The reservoir capacity satisfaction rate is defined as the ratio of the design reservoir capacity to the planned target reservoir capacity at a candidate dam site.

[0093] The unit dam height reservoir capacity is defined as the ratio of the reservoir capacity at a candidate dam site to the maximum dam height.

[0094] The ecological red line area and the basic farmland area are the areas of the intersection obtained after the reservoir basin range of the candidate dam site intersects with the ecological red line layer and the permanent basic farmland layer.

[0095] The number of migrants relocated was estimated by combining spatial distribution data of settlements within the reservoir basin with population density.

[0096] The investment in the dam body project is estimated based on the maximum dam height, dam length, and unit cost corresponding to the dam type at the candidate dam site.

[0097] The compensation for flooding losses is calculated by weighted summation of the corresponding areas of various types of land within the flooded area according to the compensation standards.

[0098] When normalizing the evaluation indicators, for positive indicators, the maximum value is used for normalization so that the larger the indicator value, the closer the normalization result is to 1; for negative indicators, the minimum value is used for normalization so that the smaller the indicator value, the closer the normalization result is to 1. All indicator values ​​are uniformly mapped to the 0~1 range to eliminate the influence of dimensional differences on the evaluation results.

[0099] The entropy-weighted TOPSIS coupled scoring model determines the initial weights based on the information entropy of each evaluation indicator, and introduces an indicator conflict adjustment coefficient to correct the initial weights. Adjusted weights of each evaluation indicator The calculation formula is: ,in, This represents the total number of evaluation indicators in the evaluation indicator system. ; For the first The information entropy of the evaluation index, the th The information entropy of each evaluation index is calculated according to the definition of information entropy based on the distribution of the normalized values ​​of each candidate dam site on that index. This is the adjustment coefficient for the degree of conflict of the indicators. The value range is from 0.1 to 0.5, for example, it can be 0.1, 0.3, or 0.5; For the first The conflict coefficient between an evaluation indicator and other evaluation indicators The value range is from 0 to 0.3, for example, it can be 0, 0.15, or 0.3. The specific value depends on the first The correlation coefficient between the normalized values ​​of each evaluation indicator and other evaluation indicators is determined—the closer the correlation coefficient is to 0, the higher the degree of conflict. The smaller the value, the closer the correlation coefficient is to 1 or -1, and the higher the conflict coefficient. The larger.

[0100] No. The overall score of each candidate dam site The calculation formula is: ,in, These are the numbers of the filtered candidate dam sites. , This represents the total number of candidate dam sites after filtering. and The first The Euclidean distance between the index normalized vector of each candidate dam site and the positive and negative ideal solutions.

[0101] The positive and negative ideal solutions are respectively composed of the optimal and worst values ​​of each evaluation index among all the filtered candidate dam sites. and The specific calculation formula is as follows: , in, For the first The candidate dam site was in the... Normalized values ​​for each evaluation indicator and The first The best and worst values ​​of each evaluation index among all filtered candidate dam sites.

[0102] The overall score obtained using the above formula The value range is from 0 to 1. The closer to 1, the more likely it is to be the first The better the candidate dam site is under the evaluation index system, the better. By sorting the candidate dam sites from high to low, we can obtain the recommended dam site scheme and the relative advantages and disadvantages of the candidate dam sites.

[0103] In addition, the implementation of differentiated processing based on warehouse capacity in step 107 of the comprehensive scoring and ranking process will be further explained.

[0104] Before comprehensively scoring and ranking the filtered candidate dam sites, they are divided into small and medium-to-large dam sites based on reservoir capacity parameters. The specific classification is based on the design reservoir capacity value corresponding to each candidate dam site.

[0105] When the designed reservoir capacity is smaller than the preset scale boundary reservoir capacity, the candidate dam site is classified as a small dam site.

[0106] When the designed reservoir capacity is greater than or equal to the preset scale boundary reservoir capacity, the candidate dam site is classified as a medium-to-large dam site.

[0107] The preset size boundary capacity ranges from 5 million m³ to 20 million m³, for example, 5 million m³, 10 million m³, and 20 million m³.

[0108] For small dam sites, if there is permanent basic farmland or ecological red line occupation within the corresponding reservoir basin area, the small dam site will be judged as unqualified and eliminated; for medium and large dam sites, they will be included in the comprehensive scoring and ranking.

[0109] For small reservoir projects, their storage capacity benefits are relatively limited, and their marginal benefits in terms of regulation capacity and downstream water supply security are low. The method in this embodiment ensures that the final recommended small reservoir scheme strictly meets the "zero occupancy" condition.

[0110] For medium and large reservoir projects, the marginal benefits of reservoir capacity and water supply security are relatively high. Under the premise that the ecological red line and permanent basic farmland have been eliminated through step 105, the schemes can still be compared and selected by comprehensive scoring and ranking under the remaining evaluation indicators.

[0111] Example 6

[0112] This embodiment uses a specific watershed planning project as an example to further illustrate the execution process of the method of the present invention.

[0113] The study area has a drainage basin of approximately 98 km², with mountainous terrain upstream and rural towns downstream. The planning objective is to locate a reservoir within the basin with a capacity between 8 million and 11 million m³ (preferred capacity approximately 9.5 million m³) to meet the water supply needs of the downstream towns. The core constraint is to completely avoid ecological protection red lines and minimize the occupation of permanent basic farmland.

[0114] Step 101: Obtain digital elevation model data and constraint layer data for the study area. The raster resolution of the digital elevation model data is 29.41m; the constraint layer data includes vector data of ecological protection red lines and vector data of permanent basic farmland distribution.

[0115] Step 102: After filling depressions in the digital elevation model data, flow direction analysis and flow accumulation calculation are performed sequentially to obtain the flow accumulation raster for the entire watershed.

[0116] Step 103: Based on the planned total runoff (8 million m³ to 11 million m³) and the average runoff depth of the watershed over many years (932.66 mm), the initial planning threshold is calculated to be approximately 8.58 km². According to the dynamic correction mechanism of Example 3, with a vegetation correction coefficient of 0.15, a runoff variation correction coefficient of 0.25, a vegetation coverage factor of 0.94, and a runoff variation coefficient of 0.28, the minimum catchment area threshold is approximately 10.39 km².

[0117] When the raster resolution of the digital elevation model data is 29.41m, the cumulative flow raster threshold corresponding to the minimum catchment area threshold is approximately 12009 rasters.

[0118] Similarly, based on the upper limit of the planned target reservoir capacity, the corresponding cumulative flow grid threshold is approximately 16,513 grids. The algorithm scans the entire river network of the basin and extracts all grid points with cumulative flow values ​​between 12,009 and 16,513 as initial candidate points, resulting in a total of 111 potential dam sites.

[0119] Step 104: Extract the corresponding reservoir basin range for each of the 111 candidate dam sites.

[0120] Step 105: Spatial overlay analysis is performed on the reservoir basin range corresponding to the 111 candidate dam sites and the ecological protection red line layer. No candidate dam sites that conflict with the ecological protection red line were identified in this watershed. All 111 candidate dam sites proceed to the next step.

[0121] Step 106: Calculate the dam body parameters (maximum dam height, dam length), reservoir capacity parameters (water level-reservoir capacity curve), inundation range, and basic farmland occupation area in parallel for 111 candidate dam sites.

[0122] Step 107: Based on the dam body parameters, reservoir capacity parameters, and the occupancy status of sensitive elements, the filtered candidate dam sites are comprehensively scored and ranked, and a recommended dam site scheme is output. The key indicators of this recommended scheme are shown in Table 1.

[0123] Table 1 Key Indicators of Recommended Scheme for a Certain Watershed

[0124]

[0125] Example 7

[0126] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described reservoir site selection method based on multi-source data.

[0127] Without loss of generality, computer-readable media can include computer storage media and communication media. Computer storage media includes volatile and non-volatile, removable and non-removable media implemented using any method or technology for storing information such as computer-readable instruction data structures, program modules, or other data. Computer storage media includes RAM, ROM, EPROM, EEPROM, flash memory or other solid-state storage technologies, CD-ROM, DVD or other optical storage, magnetic tape cassettes, magnetic tape, disk storage, or other magnetic storage devices. Of course, those skilled in the art will recognize that computer storage media are not limited to the above-mentioned types. The aforementioned system memories and mass storage devices can be collectively referred to as memory.

[0128] A computer program product includes a computer program / instructions that, when executed by a processor, implement the above-described reservoir site selection method based on multi-source data.

[0129] Computer program products include computer programs or instruction sets used to perform specific tasks or achieve specific functions. These programs or instructions are designed to be executed by a processor to implement a series of predefined steps or operations. The program product may be stored in various forms of computer storage media, such as memory, hard disks, solid-state drives, optical discs, or other forms of digital storage devices. It may exist in the form of compiled binary code or in the form of scripts or bytecode that can be executed by an interpreter. Through carefully designed algorithms and logical instructions, the program product enables the processor to process data in a specific order and manner, performing various functions such as data analysis, user interaction, and device control.

[0130] In the description of this specification, the references to terms such as "one embodiment / mode," "some embodiments / modes," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment / mode or example is included in at least one embodiment / mode or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment / mode or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments / modes or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments / modes or examples described in this specification, as well as the features of different embodiments / modes or examples.

[0131] Those skilled in the art should understand that the above embodiments are merely for illustrating the present invention and are not intended to limit the scope of the invention. Those skilled in the art can make other changes or modifications based on the above invention, and these changes or modifications still fall within the scope of the present invention.

Claims

1. A reservoir site selection method based on multi-source data, characterized in that, include: Acquire digital elevation model data and constraint layer data for the study area. The constraint layer data is a spatial vector or raster layer that constitutes legal or engineering prohibitive constraints on the implementation of the reservoir project, including at least one of the following: ecological protection red line layer, permanent basic farmland layer, geological disaster risk zone layer, nature reserve map layer, and cultural relic protection unit red line layer. After filling depressions in the digital elevation model data, flow direction analysis and flow accumulation calculation are performed sequentially to obtain a flow accumulation grid. The minimum catchment area threshold is determined based on the preset total runoff threshold, and a set of candidate dam sites is generated based on the minimum catchment area threshold and the flow accumulation grid. For each of the candidate dam sites, the corresponding reservoir basin range is extracted; Spatial overlay analysis is performed on the reservoir basin range and the constraint layer data to eliminate candidate dam site points that conflict with the constraint area defined by the constraint layer data, resulting in a filtered set of candidate dam site points. For each candidate dam site after filtering, dam body parameters and reservoir capacity parameters are calculated, and the occupancy of sensitive elements within the inundation area is determined based on the spatial relationship between water level and topography. The dam body parameters include the maximum dam height, dam crest elevation, and dam length. The reservoir capacity parameters include reservoir capacity values ​​at different water levels, which form a water level-reservoir capacity curve. The occupancy of sensitive elements includes the area of ​​basic farmland, the area of ​​ecological red line, the number of settlements, and the relocated population estimated from the number of settlements within the inundation area. Based on the dam body parameters, reservoir capacity parameters, and the occupancy status of the sensitive elements, the filtered candidate dam sites are comprehensively scored and ranked, and a recommended dam site scheme is output. Specifically, the initial planning threshold is dynamically adjusted based on the vegetation cover factor and runoff variation coefficient of the study area to determine the minimum catchment area threshold. The calculation formula is: ,in, The initial planning threshold; This is the vegetation correction factor; This is the runoff variability correction factor; The vegetation cover factor for the study area; denoted as the runoff variation coefficient for the study area.

2. The reservoir site selection method based on multi-source data according to claim 1, characterized in that, The flow direction analysis employs a multi-flow direction algorithm, which calculates the flow distribution coefficient from the central grid to each downslope neighboring grid based on the slope between the central grid and the downslope neighboring grids, combined with a slope correction factor. The flow allocation coefficient The calculation formula is: ,in, For all downhill neighborhood grid sets, ; Center grid and the first The slope between neighboring grid cells of a downslope; Center grid and the first The slope between neighboring grid cells of a downslope; Assign an index to the flow direction; The slope correction factor is mentioned above.

3. The reservoir site selection method based on multi-source data according to claim 1, characterized in that, The calculation of the reservoir capacity parameters incorporates grid confidence weights, siltation correction terms, and elevation measurement error correction terms for correction. The grid confidence weights are determined based on the accuracy of the digital elevation model data and the terrain complexity. The sedimentation correction term is determined based on the multi-year average sediment transport modulus of the study area; The elevation measurement error correction term is determined based on the measurement accuracy of the digital elevation model data in different elevation ranges.

4. The reservoir site selection method based on multi-source data according to claim 3, characterized in that, water level The corrected storage capacity below The calculation formula is: ,in, This is the lowest elevation of the reservoir bottom; Water level The corresponding submerged grid set; For the first The grid confidence weights for each grid cell; For the first The planar area of ​​each grid cell; Calculate the thickness for each layer; The reservoir capacity loss rate due to siltation; This is a correction item for elevation measurement errors.

5. The reservoir site selection method based on multi-source data according to claim 1, characterized in that, The filtered candidate dam sites are then ranked and scored comprehensively, including: An evaluation index system is constructed, which includes positive and negative indicators. The positive indicators include at least one of reservoir capacity satisfaction rate and reservoir capacity per unit dam height, and the negative indicators include at least one of ecological red line occupation area, basic farmland occupation area, relocated population, dam body engineering investment and inundation loss compensation cost. After normalizing each evaluation index, the entropy weight-TOPSIS coupled scoring model is used to comprehensively score each candidate dam site.

6. The reservoir site selection method based on multi-source data according to claim 5, characterized in that, When the entropy-weighted TOPSIS coupled scoring model performs scoring, the initial weights are determined by the information entropy of each evaluation indicator, and the initial weights are corrected by introducing an indicator conflict adjustment coefficient. Adjusted weights of each evaluation indicator The calculation formula is: ,in, This represents the total number of evaluation indicators in the aforementioned evaluation indicator system. ; For the first Information entropy of each evaluation indicator; This is the conflict adjustment coefficient for the aforementioned indicator; For the first The conflict coefficient between an evaluation indicator and other evaluation indicators; No. The overall score of each candidate dam site The calculation formula is: ,in, For the first The Euclidean distance between the index normalized vectors of each candidate dam site and the positive ideal solution; For the first The Euclidean distance between the index normalized vectors of each candidate dam site and the negative ideal solution; For the first The candidate dam site was in the... Normalized values ​​for each evaluation indicator and The first The best and worst values ​​of each evaluation index among all candidate dam sites.

7. The reservoir site selection method based on multi-source data according to claim 1, characterized in that, Before performing a comprehensive scoring and ranking of the filtered candidate dam sites, the filtered candidate dam sites are divided into small dam sites and medium-to-large dam sites according to the reservoir capacity parameters. For the small dam site, if there is permanent basic farmland occupation or ecological red line occupation within the corresponding reservoir basin area, the small dam site will be judged as unqualified and eliminated. For the medium and large dam sites, the comprehensive scoring and ranking will be used.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the reservoir site selection method based on multi-source data as described in any one of claims 1-7.

9. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the reservoir site selection method based on multi-source data as described in any one of claims 1-7.

Images