Landslide susceptible area grading evaluation method based on extreme rainfall Thiessen polygon

Abstract

The invention discloses a landslide susceptible area grading evaluation method based on an extreme rainfall Thiessen polygon. Extreme rainfall is the most direct factor for inducing landslides, and the possibility of landslides in corresponding areas can be increased by increasing the extreme rainfall frequency and the extreme rainfall intensity. According to the method, a research area is divided into a plurality of Thiessen polygon areas, and only one rainfall station or weather station exists in each Thiessen polygon area; obtaining long-sequence day-by-day rainfall data, respectively calculating an extreme rainfall frequency index and an extreme rainfall intensity index of each station, and obtaining an extreme rainfall evaluation result of each Thiessen polygon region; performing visual interpretation or artificial intelligence interpretation on the remote sensing satellite image to obtain a large-range landslide layer; and superposing the extreme rainfall evaluation result of each Thiessen polygon region and the large-range remote sensing landslide interpretation result, and carrying out grading evaluation on the landslide-prone region. According to the invention, grading evaluation is carried out on the landslide-prone areas, and the prevention and control efficiency of landslide disasters is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of geological disaster prevention and control, and in particular relates to a landslide prone area classification evaluation method based on extreme precipitation Thiessen polygons. Background Art

[0002] Landslide refers to the natural phenomenon of the overall downward sliding of rock and soil on a slope. The main influencing factors are precipitation, river scouring, earthquakes, etc. Among them, extreme precipitation is the most direct factor inducing landslides. The increase in extreme precipitation frequency and intensity will increase the possibility of landslides in the corresponding area.

[0003] The main types of landslides are early landslides that have not yet occurred, newly occurred landslides, and old landslides. At present, these landslides are interpreted based on satellite remote sensing images using visual interpretation or artificial intelligence methods, which has achieved landslide identification in a large area and provided a reference for landslide disaster prevention and control.

[0004] However, for linear engineering surveys such as railways and highways, the landslide range obtained by remote sensing image interpretation is too large, and it is difficult to determine which areas are prone to landslides and which areas are stable, making it difficult to carry out targeted disaster prevention and disaster relief work. Therefore, it is necessary to classify the susceptibility of large-scale landslide areas obtained by remote sensing interpretation, as different levels of landslides have different probabilities of occurring in the future. Summary of the invention

[0005] In order to make up for the shortcomings of the prior art, the present invention provides a landslide-prone area classification evaluation method based on extreme precipitation Thiessen polygons, which can perform graded evaluation on landslide areas identified by remote sensing, which is beneficial to improving the work efficiency of engineering disaster prevention and avoidance.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

[0007] A landslide prone area classification evaluation method based on extreme precipitation Thiessen polygons, characterized by comprising the following steps:

[0008] S1: Based on the latitude and longitude coordinates of the study area boundary and the internal rainfall station or meteorological station, the study area is divided into multiple Thiessen polygon areas, and each Thiessen polygon area has only one rainfall station or meteorological station;

[0009] S2: Obtain the long-sequence daily precipitation data recorded by the rain gauge or meteorological station, calculate the extreme precipitation frequency index and extreme precipitation intensity index of each station respectively, and then divide the precipitation change within the Thiessen polygon into four categories: only the precipitation frequency increases significantly, only the precipitation intensity increases significantly, both the precipitation frequency and intensity increase significantly, and no significant increase in precipitation, as the extreme precipitation evaluation results of each Thiessen polygon area;

[0010] S3: Obtain a large-scale landslide layer obtained by visual interpretation or artificial intelligence interpretation from remote sensing satellite images;

[0011] S4: Superimpose the extreme precipitation evaluation results of each Thiessen polygon area with the large-scale remote sensing landslide interpretation results to conduct a graded evaluation of landslide-prone areas.

[0012] Further, the specific method of step S1 is:

[0013] S1-1: Collect the vector boundary layer of the study area, as well as the longitude and latitude of the rain gauges and meteorological stations in the study area and long-sequence daily precipitation data. The sequence length must be greater than 30 years.

[0014] S1-2: Divide the Thiessen polygons, each of which has only one rainfall station or meteorological station.

[0015] Further, the specific method of step S2 is:

[0016] S2-1: Select the number of heavy rain days R25: the number of days with annual daily precipitation ≥ 25 mm, as the evaluation index of extreme precipitation frequency index; select the heavy precipitation R95p: the sum of precipitation with annual daily precipitation > 95th percentile value, as the evaluation index of extreme precipitation intensity index; the sequence length of extreme precipitation frequency index and extreme precipitation intensity index is the number of years of daily precipitation sequence of corresponding meteorological station or rainfall station, which is greater than 30 years;

[0017] S2-2: Calculate the trend change of the long-sequence extreme precipitation frequency index and extreme precipitation intensity index, taking the significance level as 0.05. The trend analysis results can be divided into: significant decrease, significant increase, and other situations;

[0018] S2-3: Divide the Thiessen polygons into four categories: "only the precipitation frequency increases significantly, only the precipitation intensity increases significantly, both the precipitation frequency and intensity increase significantly, and no significant increase in precipitation" as the extreme precipitation evaluation results for each Thiessen polygon area.

[0019] Further, the specific method of step S3 is:

[0020] S3-1: Obtain landslide interpretation results based on satellite remote sensing images using visual interpretation or artificial intelligence methods to obtain landslide identification areas within a large area;

[0021] S3-2: Export the landslide identification area within the large area of ​​S3-1 as a vector layer in ArcGIS.

[0022] Further, the specific method of step S4 is:

[0023] S4-1: Superimpose the extreme precipitation evaluation results of each Thiessen polygon area with the large-scale remote sensing landslide interpretation results to conduct a graded evaluation of landslide-prone areas;

[0024] S4-2: Export the landslide-prone area classification evaluation results in step S4-1 as a vector layer in ArcGIS to obtain the landslide-prone area classification evaluation results based on extreme precipitation Thiessen polygons.

[0025] Furthermore, in step S1-2, all adjacent rain gauges or meteorological stations in the study area are connected into triangles, and perpendicular bisectors of the sides of these triangles are drawn. The intersection points of the perpendicular bisectors of the three sides of each triangle are connected to obtain a polygon, namely, a Thiessen polygon, and there is only one rain gauge or meteorological station in each Thiessen polygon.

[0026] Furthermore, in step S2, the precipitation changes within the Thiessen polygon are classified by a trend test method.

[0027] Furthermore, “precipitation frequency and intensity have increased significantly” corresponds to Class A landslide-prone areas.

[0028] Furthermore, “only precipitation frequency increased significantly, only precipitation intensity increased significantly” corresponds to Class B landslide-prone areas.

[0029] Furthermore, “no significant increase in precipitation” corresponds to Class C landslide-prone areas.

[0030] Beneficial effects of the present invention:

[0031] The method of the present invention starts from the evolution of long-sequence extreme precipitation at rain gauges or meteorological stations, calculates the Thiessen polygon area where the risk of future precipitation disasters will significantly increase, and by superimposing it with the results of landslide interpretation from remote sensing images, conducts a graded evaluation of landslide-prone areas, thereby improving the efficiency of landslide disaster prevention and control. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 is a flow chart of the evaluation method of the present invention;

[0033] Figure 2 The present invention is based on the Thiessen polygon partitioning of the weather station or the rainfall station;

[0034] Figure 3 An extreme precipitation evaluation map for each Thiessen polygon area of ​​the present invention;

[0035] Figure 4 A large-scale landslide layer obtained by interpreting remote sensing satellite images of the present invention;

[0036] Figure 5 This is the landslide prone area classification evaluation result of the present invention. DETAILED DESCRIPTION

[0037] The present invention is described in detail below in conjunction with specific implementation modes.

[0038] The present invention considers the long-sequence changes of extreme precipitation frequency and extreme precipitation intensity, and proposes a landslide prone area classification evaluation method based on extreme precipitation Thiessen polygons;

[0039] like Figure 1 As shown, the present invention comprises the following steps:

[0040] S1: Figure 2 As shown in the figure, based on the latitude and longitude coordinates of the study area boundary and the internal rainfall station or meteorological station, the study area is divided into multiple Thiessen polygon areas. Each Thiessen polygon area has only one rainfall station or meteorological station. The specific method is as follows:

[0041] S1-1: Collect the vector boundary layer of the study area, as well as the longitude and latitude of the rain gauges and meteorological stations in the study area and long-sequence daily precipitation data. The sequence length must be greater than 30 years.

[0042] S1-2: Connect all adjacent rain gauges or meteorological stations in the study area into triangles, draw perpendicular bisectors of the sides of these triangles, and connect the intersection points of the perpendicular bisectors of the three sides of each triangle to obtain a polygon, namely the Thiessen polygon. There is only one rain gauge or meteorological station in each Thiessen polygon.

[0043] The distance from any point in the Thiessen polygon obtained by step S1-2 to the corresponding rain gauge or meteorological station is less than the distance to other polygonal stations. The precipitation at each rain gauge or meteorological station is used to represent the precipitation in the corresponding Thiessen polygon area, thereby realizing a reasonable conversion of precipitation from point to surface.

[0044] S2: Obtain the long-sequence daily precipitation data recorded by the rain gauge or weather station, calculate the extreme precipitation frequency index and extreme precipitation intensity index of each station respectively, and then divide the precipitation change within the Thiessen polygon into four categories through the Mann-Kendall trend test method: only the precipitation frequency increases significantly, only the precipitation intensity increases significantly, both the precipitation frequency and intensity increase significantly, and no significant increase in precipitation. These four categories are used as the extreme precipitation evaluation results for each Thiessen polygon area, such as Figure 3 As shown;

[0045] The specific method is:

[0046] S2-1: Select the number of heavy rain days R25: the number of days with annual daily precipitation ≥ 25 mm, as the evaluation index of extreme precipitation frequency index; select the heavy precipitation R95p: the sum of precipitation with annual daily precipitation > 95th percentile value, as the evaluation index of extreme precipitation intensity index; the sequence length of extreme precipitation frequency index and extreme precipitation intensity index is the number of years of daily precipitation sequence of corresponding meteorological station or rainfall station, which is greater than 30 years;

[0047] S2-2: Calculate the trend change of the long-sequence extreme precipitation frequency index and extreme precipitation intensity index, taking the significance level as 0.05. The trend analysis results can be divided into: significant decrease, significant increase, and other situations;

[0048] S2-3: A significant increase in precipitation frequency or precipitation intensity will increase the possibility of landslides. The Thiessen polygons are divided into four categories: "only precipitation frequency increases significantly, only precipitation intensity increases significantly, both precipitation frequency and intensity increase significantly, and precipitation does not increase significantly" as the extreme precipitation evaluation results for each Thiessen polygon area.

[0049] S3: Obtain large-scale landslide layers obtained from remote sensing satellite images by visual interpretation or artificial intelligence interpretation, such as Figure 4 As shown;

[0050] The specific method is:

[0051] S3-1: Obtain landslide interpretation results based on satellite remote sensing images using visual interpretation or artificial intelligence methods to obtain landslide identification areas within a large area;

[0052] S3-2: Export the landslide identification area within the large area of ​​S3-1 as a vector layer in ArcGIS.

[0053] S4: Superimpose the extreme precipitation evaluation results of each Thiessen polygon area with the large-scale remote sensing landslide interpretation results to conduct a graded evaluation of landslide-prone areas.

[0054] The specific method is:

[0055] S4-1: Superimpose the extreme precipitation evaluation results of each Thiessen polygon area with the large-scale remote sensing landslide interpretation results to conduct a graded evaluation of landslide-prone areas;

[0056] S4-2: Export the graded evaluation results of landslide-prone areas in step S4-1 as vector layers in ArcGIS, and obtain the graded evaluation results of landslide-prone areas based on extreme precipitation Thiessen polygons, including Class A landslide-prone areas, Class B landslide-prone areas, and Class C landslide-prone areas. "Precipitation frequency and intensity both increase significantly" corresponds to Class A landslide-prone areas, "only precipitation frequency increases significantly, only precipitation intensity increases significantly" corresponds to Class B landslide-prone areas, and "no significant increase in precipitation" corresponds to Class C landslide-prone areas. The landslide susceptibility levels are from high to low: Class A area, Class B area, and Class C area.

[0057] The content of the present invention is not limited to the embodiments listed, and any equivalent changes made to the technical solution of the present invention by ordinary technicians in this field after reading the description of the present invention are covered by the claims of the present invention.

Claims

1. A landslide prone area classification evaluation method based on extreme precipitation Thiessen polygons, characterized by: The steps include: S1: Based on the latitude and longitude coordinates of the study area boundary and the internal rainfall station or meteorological station, the study area is divided into multiple Thiessen polygon areas, and each Thiessen polygon area has only one rainfall station or meteorological station; S2: Obtain the long-sequence daily precipitation data recorded by the rain gauge or meteorological station, calculate the extreme precipitation frequency index and extreme precipitation intensity index of each station respectively, and then divide the precipitation change within the Thiessen polygon into four categories: only the precipitation frequency increases significantly, only the precipitation intensity increases significantly, both the precipitation frequency and intensity increase significantly, and no significant increase in precipitation, as the extreme precipitation evaluation results of each Thiessen polygon area; S3: Obtain a large-scale landslide layer obtained by visual interpretation or artificial intelligence interpretation from remote sensing satellite images; S4: Superimpose the extreme precipitation evaluation results of each Thiessen polygon area with the large-scale remote sensing landslide interpretation results to conduct a graded evaluation of landslide-prone areas.

2. The landslide prone area classification evaluation method based on extreme precipitation Thiessen polygons according to claim 1 is characterized by: The specific method of step S1 is: S1-1: Collect the vector boundary layer of the study area, as well as the longitude and latitude of the rain gauges and meteorological stations in the study area and long-sequence daily precipitation data. The sequence length must be greater than 30 years. S1-2: Divide the Thiessen polygons, each of which has only one rainfall station or meteorological station.

3. The landslide prone area classification evaluation method based on extreme precipitation Thiessen polygons according to claim 2 is characterized by: The specific method of step S2 is: S2-1: Select the number of heavy rain days R25: the number of days with annual daily precipitation ≥ 25 mm, as the evaluation index of extreme precipitation frequency index; select the heavy precipitation R95p: the sum of precipitation with annual daily precipitation > 95th percentile value, as the evaluation index of extreme precipitation intensity index; the sequence length of extreme precipitation frequency index and extreme precipitation intensity index is the number of years of daily precipitation sequence of corresponding meteorological station or rainfall station, which is greater than 30 years; S2-2: Calculate the trend change of the long-sequence extreme precipitation frequency index and extreme precipitation intensity index, taking the significance level as 0.

05. The trend analysis results can be divided into: significant decrease, significant increase, and other situations; S2-3: Divide the Thiessen polygons into four categories: "only the precipitation frequency increases significantly, only the precipitation intensity increases significantly, both the precipitation frequency and intensity increase significantly, and no significant increase in precipitation" as the extreme precipitation evaluation results for each Thiessen polygon area.

4. The landslide prone area classification evaluation method based on extreme precipitation Thiessen polygons according to claim 3 is characterized by: The specific method of step S3 is: S3-1: Obtain landslide interpretation results based on satellite remote sensing images using visual interpretation or artificial intelligence methods to obtain landslide identification areas within a large area; S3-2: Export the landslide identification area within the large area of ​​S3-1 as a vector layer in ArcGIS.

5. The landslide prone area classification evaluation method based on extreme precipitation Thiessen polygons according to claim 4 is characterized by: The specific method of step S4 is: S4-1: Superimpose the extreme precipitation evaluation results of each Thiessen polygon area with the large-scale remote sensing landslide interpretation results to conduct a graded evaluation of landslide-prone areas; S4-2: Export the landslide-prone area classification evaluation results in step S4-1 as a vector layer in ArcGIS to obtain the landslide-prone area classification evaluation results based on extreme precipitation Thiessen polygons.

6. The landslide prone area classification evaluation method based on extreme precipitation Thiessen polygons according to claim 5 is characterized by: In step S1-2, all adjacent rain gauges or meteorological stations in the study area are connected into triangles, and perpendicular bisectors of the sides of these triangles are drawn. The intersection points of the perpendicular bisectors of the three sides of each triangle are connected to obtain a polygon, namely, a Thiessen polygon. There is only one rain gauge or meteorological station in each Thiessen polygon.

7. The landslide prone area classification evaluation method based on extreme precipitation Thiessen polygons according to claim 6 is characterized by: In step S2, the precipitation changes within the Thiessen polygon are classified by a trend test method.

8. The landslide prone area classification evaluation method based on extreme precipitation Thiessen polygons according to claim 7 is characterized by: "Both the frequency and intensity of precipitation have increased significantly" corresponds to Class A landslide-prone areas.

9. The landslide prone area classification evaluation method based on extreme precipitation Thiessen polygons according to claim 8 is characterized by: "Only the precipitation frequency increased significantly, only the precipitation intensity increased significantly" corresponds to Class B landslide-prone areas.

10. The landslide prone area classification evaluation method based on extreme precipitation Thiessen polygons according to claim 9 is characterized by: "No significant increase in precipitation" corresponds to Class C landslide-prone areas.

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