CN117558105A - High-density urban yellow mud water phenomenon early warning method and system - Google Patents
High-density urban yellow mud water phenomenon early warning method and system Download PDFInfo
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Abstract
The invention discloses a high-density urban yellow mud water phenomenon early warning method and system, and relates to the field of disaster early warning. The method comprises the following steps: obtaining a DEM image, a remote sensing image and a road map in the area range, and interpreting to obtain a water collecting area grading data set, a vegetation coverage index data set, a land utilization classification data set, a current construction site block map data set and a road grid data set; carrying out qualitative judgment and quantitative scoring on the data in each data set; carrying out weight comparison and calculation on the influence factors of each data set; the layers where different data sets are located are weighted and overlapped to obtain a yellow water easy-occurrence area grade diagram; and acquiring ground object identification point position data in the area range, extracting landmark points in the dangerous grade division range and the road range, and issuing early warning. The invention carries out risk assessment and early warning on urban yellow mud water phenomenon, provides scientific risk avoidance suggestions for resident travel arrangement and route selection, and provides important technical support for emergency plan response.
Description
Technical Field
The invention belongs to the technical field of disaster early warning, relates to the technology of remote sensing and geographic information systems, and particularly relates to a high-density urban yellow mud water phenomenon early warning method and system.
Background
In the rapid urbanization process, a large amount of land is covered by a hard ground, most of rainwater cannot directly enter the soil, so that precipitation infiltration is reduced, and surface runoff is increased. When the rainfall exceeds the drainage capacity of the urban drainage system, surface water accumulation and urban waterlogging are very easy to occur. Contaminants such as soil, stones, cement and the like generated in the urban development and construction process enter urban surface and water body through rain wash and surface runoff, and the phenomenon of yellow mud water is extremely easy to cause.
The yellow mud water contains a large amount of sediment, pollutants and suspended matters, which can lead to road water accumulation, water drainage pipe network congestion and traffic jam, and even damage to infrastructure and vehicles. Huang Nishui flowing into water can result in poor water quality, affecting aquatic life and ecosystems. In addition, huang Nishui also has a negative impact on the living environment and health of the resident.
At present, there is almost no research on the phenomenon of high-density urban yellow mud water, and most of the previous related researches are aimed at urban waterlogging, neglecting the characteristics of high-density cities in the rapid urban process, and cannot be well applied to evaluation or early warning of the phenomenon of high-density urban yellow mud water.
Disclosure of Invention
In view of the problems existing in the prior art, the invention provides a high-density urban yellow mud water phenomenon early warning method and system, which can effectively perform risk assessment and early warning on the yellow mud water phenomenon of a high-density city under the condition of heavy rain frequency, provide scientific risk avoidance suggestions for resident travel arrangement and route selection, and provide important technical support for emergency plan response.
The invention provides a high-density urban yellow mud water phenomenon early warning method, which comprises the following steps:
s1, obtaining a DEM image in a region range, and interpreting and obtaining a terrain gradient data set and a water collecting region grading data set under the rainfall condition;
s2, acquiring a high-resolution multispectral remote sensing image in a regional range, and interpreting and obtaining a vegetation coverage index data set, a land utilization classification data set and a current construction site block pattern data set;
s3, acquiring a road map in the area range, and converting the road map into a road grid data set;
s4, carrying out qualitative judgment and quantitative scoring on the data in each data set by adopting a reclassification method, and giving different scores to the numerical values in different intervals;
s5, carrying out weight comparison and calculation on the influence factors of each data set to obtain weights of different influence factors of each data set;
s6, obtaining a yellow mud water easy-occurrence area grade diagram in the area range by carrying out weighted superposition on the layers where different data sets are located;
and S7, acquiring ground object identification point position data in the area range, extracting the landmark points in the dangerous grade division range and the road range, obtaining the position of the yellow mud water phenomenon easily-generated area, and issuing an early warning.
The invention provides a high-density urban yellow mud water phenomenon early warning system, which comprises the following modules:
the data set acquisition module is used for acquiring the DEM image in the area range, interpreting and obtaining a terrain gradient data set and a water collecting area grading data set under the rainfall condition; the method comprises the steps of acquiring a high-resolution multispectral remote sensing image in an area range, and interpreting and obtaining a vegetation coverage index data set, a land utilization classification data set and a current construction site block pattern data set; the method is also used for acquiring a road map in the area range and converting the road map into a road grid data set;
the data judging and scoring module is used for qualitatively judging and quantitatively scoring the data in each data set by adopting a reclassification method, and giving values in different intervals to different scores;
the weight comparison and calculation module is used for comparing and calculating the weights of the influence factors of each data set to obtain the weights of different influence factors of each data set;
the layer weighted superposition module is used for carrying out weighted superposition on layers where different data sets are located to obtain a yellow mud water easy occurrence area grade diagram in the area range;
the position extraction module is used for acquiring ground object identification point position data in the area range, extracting a landmark point in the dangerous grade division range and a road range, obtaining the position of a yellow mud water phenomenon easily-generated area, and issuing an early warning.
Compared with the prior art, the invention has the beneficial effects that:
(1) On the basis of urban waterlogging research, the invention combines the characteristics of high-density cities of rapid urban process, considers the land disturbance of construction sites and the composition of impermeable areas and complex blocks of the high-density cities, and realizes real-time monitoring, risk assessment and predictive early warning of yellow mud water phenomena on a remote sensing and geographic information system platform by integrating multisource data, models and algorithms from the angles of weather, hydrology, topography, land utilization, vegetation, urban blocks and the like.
(2) The method is suitable for risk assessment and early warning of urban yellow mud water phenomenon under the condition of rapid urban mass frequency in the current stage, can help urban managers and residents to take measures in time to reduce the influence of yellow mud water flood disasters, provides scientific risk avoidance suggestions for resident travel arrangement and route selection, provides important technical support for emergency plan response, protects urban environment and life and property safety of people, and has very wide application prospect.
Drawings
In order to more clearly illustrate the technical solution of the embodiments of the present invention, the drawings that are required to be used in the embodiments will be briefly described. It is appreciated that the following drawings depict only certain embodiments of the invention and are therefore not to be considered limiting of its scope. Other relevant drawings may be made by those of ordinary skill in the art without undue burden from these drawings.
FIG. 1 is a schematic flow chart of a method for early warning of high density urban yellow mud water phenomenon according to an embodiment of the invention;
fig. 2 is a schematic flow chart of DEM image processing in the high-density urban yellow mud water phenomenon early warning method according to the embodiment of the invention;
FIG. 3 is a schematic diagram of a calculated water collection area classification in a high density urban yellow mud water phenomenon early warning method according to an embodiment of the invention;
fig. 4 is a schematic flow chart of multispectral remote sensing image processing in the high-density urban yellow mud water phenomenon early warning method according to the embodiment of the invention;
FIG. 5 is a diagram illustrating data reclassification in a high density urban yellow mud water phenomenon early warning method according to an embodiment of the present invention;
fig. 6 is a schematic flow chart of calculating the weight of the influencing factor in the high-density urban yellow mud water phenomenon early warning method according to the embodiment of the invention;
FIG. 7 is a schematic diagram of a vegetation coverage index grid dataset interpreted on a remote sensing image processing platform in accordance with an embodiment of the present invention;
FIG. 8 is a schematic diagram of land utilization classification grid data set interpreted on a remote sensing image processing platform according to an embodiment of the present invention;
FIG. 9 is a schematic diagram of a current job site land parcel grid dataset interpreted on a geographic information system platform in accordance with an embodiment of the present invention;
FIG. 10 is a schematic diagram of converting a study area road map into a road raster data set on a geographic information system platform in accordance with an embodiment of the present invention;
FIG. 11 is a schematic flow chart of qualitative judgment and quantitative scoring of data in each data set by adopting a reclassification method in the embodiment of the invention;
FIG. 12 is a flowchart of calculating the influence factor weight of each data set by using the AHP method according to the embodiment of the present invention;
FIG. 13 is a schematic diagram of the matrix calculation to obtain the influence factor weight in the embodiment of the present invention;
FIG. 14 is a graph of ranking Huang Nishui vulnerable areas in a region obtained by weighting, stacking and analyzing layers in an embodiment of the present invention;
FIG. 15 is a schematic view showing the location of the yellow mud water phenomenon susceptibility areas analyzed in the embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but embodiments of the present invention are not limited thereto.
Example 1
The invention provides a high-density urban yellow mud water phenomenon early warning method, which is shown in fig. 1, and specifically comprises the following steps:
step S1: obtaining a DEM image in the area range, and interpreting and obtaining a terrain gradient data set and a water collecting area grading data set under the rainfall condition;
step S2: acquiring a high-resolution multispectral remote sensing image in a regional range, and interpreting and obtaining a vegetation coverage index data set, a land utilization classification data set and a current construction site block pattern data set;
step S3: acquiring a road map in the area range, and converting the road map into a road grid data set;
step S4: qualitative judgment and quantitative scoring are carried out on the data in each data set by adopting a reclassification method, and values in different intervals are given to different scores;
step S5: performing weight comparison and calculation on the influence factors of each data set by adopting an AHP method to obtain weights of different influence factors of each data set;
step S6: the yellow mud water easy-occurrence area level diagram in the area range is obtained by carrying out weighted superposition on the layers where different data sets are located;
step S7: and acquiring ground object identification point position data in the area range, extracting landmark points in the dangerous grade division range and the road range, obtaining the position of a yellow mud water phenomenon easily-generated area, and issuing early warning.
In this embodiment, referring to fig. 2, step S1 specifically includes the following steps:
step S11: obtaining a DEM image in the area range, and interpreting the DEM image on a geographic information system platform to obtain a terrain gradient data set in the research area range;
the interpretation process of step S11 includes:
and obtaining DEM images in the area range, namely grid images with elevation values, and calculating the gradient of the research area on a geographic information system platform by using a gradient tool. The elevation value of each pixel on the surface of the grid image is obtained, the elevation difference value of each pixel and surrounding pixels thereof and the distance between each pixel and surrounding pixels thereof in the horizontal direction and the vertical direction are calculated, and the gradient of each pixel is calculated by using the following gradient calculation formula. The calculation formula of the gradient alpha is as follows:
where dz is the elevation change, dx is the distance in the horizontal direction, and dy is the distance in the vertical direction. The terrain grade grid data set for the area of interest is obtained by interpolation, see fig. 3.
Step S12: and calculating a water collecting area in the research area range on the geographic information system platform by using the DEM image in the area range, and dividing the water collecting area in different grades to obtain a water collecting area grading data set in the research area range.
Referring to fig. 4, step S12 specifically includes:
step S121: performing hydrologic analysis on the DEM image in the area range on a geographic information system platform, and removing possible too small runoff collection points by filling water in a surface grid by using a filling tool so as to ensure the continuity of a water system network and obtain a filled grid data set;
step S122: determining the water flow direction of each pixel point in each pixel by using a flow direction tool, selecting a D8 flow direction distribution method (a method for distributing the water flow direction to the steepest downhill neighborhood), calculating the elevation difference values of 8 adjacent pixel points around each pixel point, finding the direction with the largest elevation change, and creating a flow direction grid data set from each pixel to the downhill adjacent point for the filled grid data set;
step S123: calculating an area accumulation value from each pel to a drainage basin outlet for each pel flowing to the raster data set by using a flow tool, and creating an accumulated flow raster data set of the research area;
step S124: using a river network grading tool, combining the accumulated flow grid data set and the flow direction grid data set, making a numerical sequence for grid line segments representing linear grid branches in the accumulated flow grid data set, and calculating to obtain a river network grading grid data set;
step S125: converting the grid data set representing the linear grid into a vector element data set representing the linear grid by using a grid river grid vectorization tool and combining the river grid grading grid data set and the flow direction grid data set;
step S126: using an element inflection point turning tool to find the end points of each runoff in the vector element data set representing the linear grid, and generating a pouring point (namely the point position of the most upstream of each tributary) vector data set;
step S127: the collector region tool is used to combine the flow direction raster data set and the pour point vector data set to obtain a collector region graded raster data set for the convergence region in the investigation region under rainfall conditions, see fig. 5.
Referring to fig. 6, step S2 specifically includes the following steps:
step S21: acquiring a high-resolution multispectral remote sensing image in the current stage area range, interpreting the multispectral remote sensing image on a remote sensing image processing platform, and acquiring a vegetation coverage index data set;
step S22: interpreting on a remote sensing image processing platform by utilizing the high-resolution multispectral remote sensing image in the current region range, and obtaining a land utilization classification data set;
step S23: and (3) utilizing the land utilization classification data set to interpret on a geographic information system platform and obtain the current construction site land block data set of the construction road and bridge.
The step S21 specifically includes the following steps:
obtaining a high-resolution multispectral remote sensing image in the current stage area range, carrying out pretreatment such as radiation calibration and atmospheric correction on the image on a remote sensing image processing platform, and then carrying out calculation on a normalized vegetation index (NDVI) of a research area, wherein the expression of the index NDVI is as follows:
wherein, NIR is the reflection value of near infrared band, R is the reflection value of red light band. Interpreting the index NDVI, a vegetation coverage index grid dataset for the area under study is obtained, see fig. 7.
The step S22 is specifically as follows:
and performing supervision classification on land utilization types on a remote sensing image processing platform by utilizing the preprocessed high-resolution multispectral remote sensing image in the current region range. The land utilization types are divided into 3 types, namely: water, water permeable floors and water impermeable floors. Wherein the body of water comprises: reservoirs, rivers, lakes and river surges at all levels; the permeable ground comprises: farmland, herb cover, shrub cover, broadleaf woodland, conifer woodland and wetland; the rest is classified as a watertight ground. The land use classification grid dataset of the area of investigation is interpreted and derived, see fig. 8.
The step S23 is specifically as follows:
the impervious ground in the land utilization classification grid data set of the research area is classified again, the exposed ground surface damaged by disturbance, such as the current construction site of a road bridge, is found out by adopting a visual interpretation method on a geographic information system platform, and the current construction site land block grid data set of the research area is interpreted and obtained, as shown in fig. 9.
The step S3 is specifically as follows:
acquiring a road map within an area, comprising: expressways, primary highways, secondary highways, tertiary highways, quaternary highways, bike ways, sidewalks, living block roads, etc. On the geographic information system platform, the study area road map is converted into a road raster data set, see fig. 10.
Referring to fig. 11, step S4 specifically includes the following steps:
step S41: on a geographic information system platform, qualitative judgment and quantitative scoring are carried out on data in the terrain gradient grid data set of the research area by adopting a reclassification method. The smaller the gradient, the easier it is to collect and to confluence the yellow mud water, the greater the probability of occurrence of yellow mud water phenomena, and the higher the yellow mud water susceptibility score should be. The grids with the gradient alpha of more than 45 degrees are assigned 1 part, 2 parts when the alpha is more than 40 degrees and less than or equal to 45 degrees, 3 parts when the alpha is more than 35 degrees and less than or equal to 40 degrees, 4 parts when the alpha is more than 30 degrees and less than or equal to 35 degrees, 5 parts when the alpha is more than 25 degrees and less than or equal to 30 degrees, 6 parts when the alpha is more than 20 degrees and less than or equal to 25 degrees, 7 parts when the alpha is more than 15 degrees and less than or equal to 20 degrees, 8 parts when the alpha is more than 10 degrees and less than or equal to 15 degrees, 9 parts when the alpha is more than 5 degrees and less than or equal to 10 degrees, and 10 parts when the alpha is more than 0 degrees and less than or equal to 5 degrees.
Step S42: and qualitatively judging and quantitatively scoring the data in the grading grid data set of the water collecting area of the research area by adopting a reclassification method. The higher the water collection area grade is, the more easily urban waterlogging occurs, the higher the probability of occurrence of yellow mud water phenomenon is, and the higher the yellow mud water susceptibility score is. Taking the water collection area as an example, the water collection area is classified into 0-6 stages. Wherein, the level 0 is a non-water collecting area, the level 0 is assigned 1 point, the level 1 is assigned 2 points, the level 2 is assigned 3 points, the level 3 is assigned 4 points, the level 4 is assigned 6 points, the level 5 is assigned 8 points, and the level 6 is assigned 10 points.
Step S43: and qualitatively judging and quantitatively scoring the data in the vegetation coverage index grid data set of the research area by adopting a reclassification method. Urban green land can effectively store rainwater, and healthier or denser vegetation canopies and rhizomes can better intercept surface runoff. The normalized vegetation index NDVI has a value between-1 and the vegetation cover surface NDVI is greater than 0. The larger the normalized vegetation index NDVI value, the smaller the probability of occurrence of the yellow mud water phenomenon, which represents the better vegetation growth condition, and the lower the yellow mud water susceptibility score should be. 1 score when NDVI is more than 0.9 and less than or equal to 1, 2 score when NDVI is more than 0.8 and less than or equal to 0.9, 3 score when NDVI is more than 0.7 and less than or equal to 0.8, 4 score when NDVI is more than 0.6 and less than or equal to 4 score when NDVI is more than 0.5 and less than or equal to 0.6, 6 score when NDVI is more than 0.4 and less than or equal to 0.5, 7 score when NDVI is more than 0.3 and less than or equal to 0.4, 8 score when NDVI is more than 0.2 and less than or equal to 8 score when NDVI is more than 0.1 and less than or equal to 0.2, and 10 score when NDVI is less than or equal to 0.1.
Step S44: and (3) qualitatively judging and quantitatively scoring the data in the land utilization classification grid data set of the research area by adopting a reclassification method. The greater the ground permeability, the less likely that the yellow mud water phenomenon will occur, and the lower the yellow mud water susceptibility score should be. The water body is 1 part, the water permeable ground is 6 parts, and the water impermeable ground is 10 parts.
Step S45: and qualitatively judging and quantitatively scoring the data in the grid data set of the current construction site land parcel of the research area by adopting a reclassification method. Along with urban promotion, soil, stones, cement and the like generated in the construction process of facilities of houses and roads enter urban surface and water bodies through rain wash, surface runoff and other ways, and yellow mud water phenomenon is easy to cause. The land mass in the construction site is scored high, 10 points are given, other areas are scored low, and 1 point is given.
Step S46: and (5) qualitatively judging and quantitatively scoring the data in the road grid data set of the research area by adopting a reclassification method. When heavy rainfall occurs, rainwater is usually led to roadside drainage ditches and drainage pipe networks, and if a drainage system is insufficient and a road surface is uneven, accumulated water in the road surface or a culvert under a bridge can be caused, so that the road is a yellow mud water phenomenon high-occurrence area. The road occupation score is high, 10 points are given, the score in other areas is low, and 1 point is given.
Referring to fig. 12, step S5 is specifically as follows:
step S51: on an AHP realization platform, the structural relation between the yellow mud water phenomenon susceptibility area and the influence factors of each data set is constructed. The built hierarchy includes three layers, the first layer being the decision target, namely: yellow mud water phenomenon easy-occurrence area; the second layer is an intermediate layer element, namely: the system comprises a terrain gradient data set, a water collecting area grading data set, a vegetation coverage index data set, a land utilization classification data set, a current construction site disturbance land block data set and a road grid data set, wherein middle layer elements are connected with a decision target; the third layer is a decision scheme, namely: the total node connecting the intermediate layer elements.
Step S52: and carrying out pairwise qualitative comparison on the weights of the influence factors to obtain a weight comparison result. The plurality of influencing factors are respectively: these 6 impact factors are not equally important, as are grade, catchment classification, vegetation cover index, land use classification, construction site disturbance plots and roads. Qualitative comparisons can be made of the importance of the influencing factors by soliciting expert opinion.
Step S53: and obtaining the weight of the influence factor through matrix calculation. Taking the following weight comparison results as examples: land utilization classification = construction site disturbance block = road > water collection classification > grade > vegetation cover index. Matrix calculation is carried out to obtain: the weight of land utilization classification, construction site disturbance land block and road is 0.2429, the weight of water collection area classification is 0.1266, the weight of gradient is 0.0861, and the weight of vegetation coverage index is 0.0586, see fig. 13.
The step S6 is specifically as follows:
and on a geographic information system platform, carrying out weighted superposition analysis on the map layers of the terrain gradient data set, the water collecting area grading data set, the vegetation coverage index data set, the land utilization classification data set, the current construction site disturbance land block data set and the road grid data set to obtain a yellow mud water easy-occurrence area grade map in the area range. The ranks are classified into 4 ranks, namely, a safe region, a low-incidence region, a medium-incidence region, and a high-incidence region, respectively, as shown in fig. 14.
The step S7 is specifically as follows:
the method comprises the steps of acquiring ground object identification point position data in a research area, extracting landmark points and road ranges in a high-susceptibility area of yellow mud water phenomenon, and issuing early warning for activities and strokes needing to be reduced or delayed to go in the range, wherein the map is shown in fig. 15.
Example 2
Based on the same inventive concept as embodiment 1, this embodiment provides a high-density urban yellow mud water phenomenon early warning system, which includes the following modules:
the data set acquisition module is used for acquiring the DEM image in the area range, interpreting and obtaining a terrain gradient data set and a water collecting area grading data set under the rainfall condition; the method comprises the steps of acquiring a high-resolution multispectral remote sensing image in an area range, and interpreting and obtaining a vegetation coverage index data set, a land utilization classification data set and a current construction site block pattern data set; the method is also used for acquiring a road map in the area range and converting the road map into a road grid data set;
the data judging and scoring module is used for qualitatively judging and quantitatively scoring the data in each data set by adopting a reclassification method, and giving values in different intervals to different scores;
the weight comparison and calculation module is used for comparing and calculating the weights of the influence factors of each data set to obtain the weights of different influence factors of each data set;
the layer weighted superposition module is used for carrying out weighted superposition on layers where different data sets are located to obtain a yellow mud water easy occurrence area grade diagram in the area range;
the position extraction module is used for acquiring ground object identification point position data in the area range, extracting a landmark point in the dangerous grade division range and a road range, obtaining the position of a yellow mud water phenomenon easily-generated area, and issuing an early warning.
The implementation process of the weight comparison and calculation module comprises the following steps:
on an AHP realization platform, constructing the structural relationship between the yellow mud water phenomenon susceptibility area and the influence factors of each data set; the constructed hierarchical structure comprises three layers, wherein the first layer is a decision target, namely a yellow mud water phenomenon easy occurrence area; the second layer is an intermediate layer element, namely a terrain gradient data set, a water collecting area grading data set, a vegetation coverage index data set, a land utilization classification data set, a current construction site disturbance land block data set and a road grid data set, wherein the intermediate layer element is connected with a decision target; the third layer is a decision scheme, namely a total node connected with each middle layer element;
carrying out pairwise qualitative comparison on the weights of the plurality of influence factors to obtain a weight comparison result; the plurality of influencing factors comprise gradient, water collecting area grading, vegetation coverage index, land utilization classification, construction site disturbance land parcels and roads;
and obtaining the weight of the influence factor through matrix calculation.
The modules of this embodiment are used to implement the corresponding one or more steps of embodiment 1, respectively, for detailed implementation see embodiment 1.
It is apparent that the above-described embodiments are only some embodiments of the present invention, but not all embodiments, and the present invention is not limited to the details of the above-described embodiments, and any appropriate changes or modifications made by those skilled in the art will be deemed to be within the scope of the present invention.
Claims (10)
1. The high-density urban yellow mud water phenomenon early warning method is characterized by comprising the following steps of:
s1, obtaining a DEM image in a region range, and interpreting and obtaining a terrain gradient data set and a water collecting region grading data set under the rainfall condition;
s2, acquiring a high-resolution multispectral remote sensing image in a regional range, and interpreting and obtaining a vegetation coverage index data set, a land utilization classification data set and a current construction site block pattern data set;
s3, acquiring a road map in the area range, and converting the road map into a road grid data set;
s4, carrying out qualitative judgment and quantitative scoring on the data in each data set by adopting a reclassification method, and giving different scores to the numerical values in different intervals;
s5, carrying out weight comparison and calculation on the influence factors of each data set to obtain weights of different influence factors of each data set;
s6, obtaining a yellow mud water easy-occurrence area grade diagram in the area range by carrying out weighted superposition on the layers where different data sets are located;
and S7, acquiring ground object identification point position data in the area range, extracting the landmark points in the dangerous grade division range and the road range, obtaining the position of the yellow mud water phenomenon easily-generated area, and issuing an early warning.
2. The method according to claim 1, wherein step S1 includes:
s11, obtaining a DEM image in an area range, and interpreting the DEM image on a geographic information system platform to obtain a terrain gradient data set in a research area range;
and S12, calculating a water collecting area in the research area range on the geographic information system platform by using the DEM image in the area range, and dividing the water collecting area in different grades to obtain a water collecting area grading data set in the research area range.
3. The method according to claim 2, wherein the DEM image is a raster image with an elevation value, and the interpretation in step S11 includes:
calculating the gradient of the research area on a geographic information system platform by using a gradient tool; the method comprises the steps of obtaining elevation values of pixels on the surface of a grid image, calculating elevation difference values of the pixels and surrounding pixels, calculating distances between the pixels and the surrounding pixels in the horizontal direction and the vertical direction, and calculating gradients of the pixels; the calculation formula of the gradient alpha is as follows:
wherein dz is the elevation change, dx is the distance in the horizontal direction, and dy is the distance in the vertical direction; the terrain grade grid data set of the investigation region is obtained by interpolation.
4. The method according to claim 2, wherein the DEM image is a raster image with an elevation value, and step S12 specifically includes:
step S121, performing hydrologic analysis on the DEM image in the area range on a geographic information system platform, and removing the too small runoff collection points by filling water in the surface grids by using a filling tool to enable the water system network to be continuous, so as to obtain a filled grid data set;
step S122, determining the water flow direction of each pixel point in each pixel by using a flow direction tool, selecting a method for distributing the water flow direction to the steepest downhill neighborhood, calculating the elevation difference values of a plurality of adjacent pixel points around each pixel point, finding the direction with the largest elevation change, and creating a flow direction grid data set from each pixel point to the downhill adjacent point for the filled grid data set;
step S123, calculating an area accumulation value from each pixel to a drainage basin outlet for each pixel flowing to the grid data set by using a flow tool, and creating an accumulated flow grid data set of the research area;
step S124, using a river network grading tool, combining the accumulated flow grid data set and the flow direction grid data set, making a numerical sequence for grid line segments representing linear grid branches in the accumulated flow grid data set, and calculating to obtain a river network grading grid data set;
step S125, using a grid river network vectorization tool, combining the river network grading grid data set and the flow direction grid data set, and converting the grid data set representing the linear grid into a vector element data set representing the linear grid;
step S126, using an element inflection point turning tool, finding out the end points of each runoff in the vector element data set representing the linear grid, and generating a pouring point vector data set;
step S127, using a collecting area tool, combining the flow direction raster data set and the pour point vector data set to obtain a collecting area grading raster data set of the converging area in the research area under rainfall condition.
5. The method according to claim 1, wherein step S2 includes:
s21, acquiring a high-resolution multispectral remote sensing image in the current stage area range, interpreting the multispectral remote sensing image on a remote sensing image processing platform, and obtaining a vegetation coverage index data set;
s22, interpreting the multispectral remote sensing image with high resolution in the current stage area range on a remote sensing image processing platform, and obtaining a land utilization classification data set;
and S23, interpreting the land utilization classification data set on a geographic information system platform, and obtaining the current construction site land block data set of the construction road and bridge.
6. The method of claim 5, wherein step S21 specifically comprises:
obtaining a high-resolution multispectral remote sensing image in the region of the current stage, carrying out radiometric calibration and atmospheric correction pretreatment on the image on a remote sensing image processing platform, and then carrying out normalized vegetation index NDVI calculation on the research region, wherein the index NDVI has the expression:
wherein, NIR is the reflection value of near infrared band, R is the reflection value of red band;
interpreting the index NDVI and obtaining a vegetation coverage index grid dataset for the area of investigation;
the step S22 specifically includes:
utilizing the pretreated high-resolution multispectral remote sensing image in the current stage area range to supervise and classify land utilization types on a remote sensing image processing platform, and dividing the land utilization types into water bodies, permeable floors and impermeable floors; interpreting and obtaining land use classification grid data sets of the research area;
the step S23 specifically includes:
classifying the watertight ground in the land utilization classified grid data set of the research area again, finding out the exposed ground surface of the current construction site of the road bridge, which is disturbed and destroyed, by adopting a visual interpretation method on a geographic information system platform, and interpreting and obtaining the current construction site land block grid data set of the research area.
7. The method according to claim 1, wherein the step S4 specifically includes the steps of:
s41, carrying out qualitative judgment and quantitative scoring on data in the terrain gradient grid data set of the research area by adopting a reclassification method on a geographic information system platform; the smaller the gradient is, the easier the yellow mud water is gathered and converged, and the higher the probability of occurrence of the yellow mud water phenomenon is, the higher the yellow mud water liability score is;
step S42, carrying out qualitative judgment and quantitative scoring on data in the grading grid data set of the water collecting area in the research area by adopting a reclassification method; the higher the grade of the water collecting area is, the more easily urban waterlogging occurs, the higher the probability of occurrence of yellow mud water phenomenon is, and the higher the probability of occurrence of yellow mud water is;
s43, qualitatively judging and quantitatively scoring the data in the vegetation coverage index grid data set of the research area by adopting a reclassification method; the larger the value of the normalized vegetation index NDVI is, the better the vegetation growth condition is, the smaller the probability of occurrence of yellow mud water phenomenon is, and the lower the yellow mud water susceptibility score is;
step S44, carrying out qualitative judgment and quantitative scoring on the data in the land utilization classification grid data set of the research area by adopting a reclassification method; the larger the ground water permeability is, the smaller the probability of occurrence of yellow mud water phenomenon is, and the lower the yellow mud water susceptibility scoring is;
step S45, carrying out qualitative judgment and quantitative scoring on data in the grid data set of the current construction site land block in the research area by adopting a reclassification method; the land mass scoring on the construction site is high, and the scoring on other areas is low;
step S46, qualitatively judging and quantitatively scoring the data in the road grid data set of the research area by adopting a reclassification method; road occupation scoring is high and other areas scoring is low.
8. The method according to claim 1, wherein step S5 includes:
s51, constructing structural relations between the yellow mud water phenomenon susceptibility areas and the influence factors of all data sets on an AHP realization platform; the constructed hierarchical structure comprises three layers, wherein the first layer is a decision target, namely a yellow mud water phenomenon easy occurrence area; the second layer is an intermediate layer element, namely a terrain gradient data set, a water collecting area grading data set, a vegetation coverage index data set, a land utilization classification data set, a current construction site disturbance land block data set and a road grid data set, wherein the intermediate layer element is connected with a decision target; the third layer is a decision scheme, namely a total node connected with each middle layer element;
step S52, carrying out pairwise qualitative comparison on the weights of the influence factors to obtain a weight comparison result; the plurality of influencing factors comprise gradient, water collecting area grading, vegetation coverage index, land utilization classification, construction site disturbance land parcels and roads;
and step S53, obtaining the weight of the influence factor through matrix calculation.
9. The utility model provides a high density city yellow mud water phenomenon early warning system which characterized in that includes following module:
the data set acquisition module is used for acquiring the DEM image in the area range, interpreting and obtaining a terrain gradient data set and a water collecting area grading data set under the rainfall condition; the method comprises the steps of acquiring a high-resolution multispectral remote sensing image in an area range, and interpreting and obtaining a vegetation coverage index data set, a land utilization classification data set and a current construction site block pattern data set; the method is also used for acquiring a road map in the area range and converting the road map into a road grid data set;
the data judging and scoring module is used for qualitatively judging and quantitatively scoring the data in each data set by adopting a reclassification method, and giving values in different intervals to different scores;
the weight comparison and calculation module is used for comparing and calculating the weights of the influence factors of each data set to obtain the weights of different influence factors of each data set;
the layer weighted superposition module is used for carrying out weighted superposition on layers where different data sets are located to obtain a yellow mud water easy occurrence area grade diagram in the area range;
the position extraction module is used for acquiring ground object identification point position data in the area range, extracting a landmark point in the dangerous grade division range and a road range, obtaining the position of a yellow mud water phenomenon easily-generated area, and issuing an early warning.
10. The early warning system of claim 9, wherein the weight comparison calculation module is implemented by:
on an AHP realization platform, constructing the structural relationship between the yellow mud water phenomenon susceptibility area and the influence factors of each data set; the constructed hierarchical structure comprises three layers, wherein the first layer is a decision target, namely a yellow mud water phenomenon easy occurrence area; the second layer is an intermediate layer element, namely a terrain gradient data set, a water collecting area grading data set, a vegetation coverage index data set, a land utilization classification data set, a current construction site disturbance land block data set and a road grid data set, wherein the intermediate layer element is connected with a decision target; the third layer is a decision scheme, namely a total node connected with each middle layer element;
carrying out pairwise qualitative comparison on the weights of the plurality of influence factors to obtain a weight comparison result; the plurality of influencing factors comprise gradient, water collecting area grading, vegetation coverage index, land utilization classification, construction site disturbance land parcels and roads;
and obtaining the weight of the influence factor through matrix calculation.
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