Geological disaster risk assessment model fusing soil humidity, deformation and rainfall information

Abstract

The invention discloses a geological disaster risk assessment model fusing soil humidity, deformation and rainfall information, and relates to the technical field of geological disaster risk assessment. Comprising the steps of 1, establishing a rainfall information-based Logistic geological disaster risk assessment model, 2, introducing soil humidity parameters into the Logistic geological disaster risk assessment model, 3, introducing earth surface deformation parameters into the Logistic geological disaster risk assessment model, and 4, constructing an earth surface deformation, soil humidity and rainfall information-fused geological disaster risk assessment model. The invention aims to establish a soil humidity-deformation-rainfall fusion model by introducing soil humidity and deformation information, thereby effectively improving the accuracy of rainfall-induced geological disaster monitoring and early warning, optimizing the existing geological disaster risk assessment model, and providing technical support for disaster prevention and reduction work.

Description

Technical Field

[0001] The present invention relates to the technical field of geological disaster risk assessment, and in particular to a geological disaster risk assessment model integrating soil moisture, deformation and rainfall information. Background Art

[0002] Rainfall-induced landslides caused by climate change and extreme rainfall events are a high-incidence geological disaster. At present, they are mainly warned by rainfall thresholds. However, due to the complexity of landslide disasters and the differences in geological background conditions, the relationship between geological disasters and rainfall is very complicated, and there is currently no good general method to predict and analyze them. Since the occurrence of landslides is related to the intensity, duration and rainfall of rainfall, it is difficult to define them with a certain rainfall value. The existing thresholds are not accurate enough and have significant regional differences, resulting in a large number of false alarms and missed alarms, which greatly reduces the practicality of meteorological elements in landslide early warning systems. Therefore, a geological disaster risk assessment model that integrates soil moisture, deformation and rainfall information is very necessary.

[0003] The existing geological disaster risk assessment model still has the following defects, which are specifically reflected in:

[0004] 1. Based on the statistics of the relationship between disasters and rainfall in a certain area, the rainfall value within 1 hour, 1 day or 1 continuous rainfall process is artificially defined and used as the rainfall threshold for landslide. This method has no unified standard and is highly subjective and arbitrary, and cannot be used for objective quantitative analysis and prediction.

[0005] 2. The existing geological disaster risk assessment model only considers rainfall information, which may have large errors in predicting rainfall and cannot adapt to the climate and terrain conditions in different regions.

[0006] 3. The construction and prediction process of the existing geological hazard risk assessment model mainly relies on a single meteorological observation data source, which cannot fully reflect the authenticity and variability of meteorological conditions, affecting the accuracy and stability of the model in predicting weather changes, thereby reducing the overall reliability and practicality of the model. Summary of the invention

[0007] The purpose of the present invention is to provide a geological disaster risk assessment model that integrates soil moisture, deformation and rainfall information, thereby solving the problems existing in the background technology.

[0008] To solve the above technical problems, the present invention adopts the following technical solution: The geological disaster risk assessment model of the present invention integrates soil moisture, deformation and rainfall information, including: Step 1: Establishing a Logistic geological disaster risk assessment model based on precipitation information.

[0009] Step 2: Introduce soil moisture parameters into the Logistic geological hazard risk assessment model.

[0010] Step 3: Introduce surface deformation parameters into the Logistic geological hazard risk assessment model.

[0011] Step 4: Construct a geological disaster risk assessment model that integrates surface deformation, soil moisture and rainfall information.

[0012] As a preferred solution, the step 1: establish a Logistic geological disaster risk assessment model based on precipitation information, and the specific analysis method is: assuming that under the influence of the rainfall independent variable, the probability of a disaster occurring is 1, and the probability of no disaster occurring is 0, p is the predicted probability of a disaster occurring, and the value range is [0,1], then (1-p) is the predicted probability of no disaster occurring, p / (1-p) is the disaster logistic regression occurrence ratio, and its natural logarithm ln[p / (1-p)] is taken, and the independent variable is x 1 ,x 2 ,...,xk, the dependent variable is p, then the Logistic linear regression function can be expressed as: where β i is the i-th model coefficient, i=0,1,2,…,k, and the probability of geological disaster risk based on precipitation information is analyzed.

[0013] As a preferred solution, the specific analysis method of the geological disaster risk probability based on precipitation information is as follows: According to formula (1), we can get Formula (2) can be used to predict the probability of disaster occurrence under rainfall conditions, obtain rainfall information in historical rainfall-type landslide events in the study area, and obtain rainfall factors, cumulative rainfall factors, and previous rainfall attenuation coefficients. By performing systematic Logistic regression analysis, correlation analysis, and partial correlation analysis on the relationship between rainfall factors, cumulative rainfall factors, and previous rainfall attenuation coefficients and the occurrence and number of disasters, appropriate rainfall factors and previous rainfall attenuation coefficients for the study area are screened out as independent variable factors of the model. where p 1 is the geological disaster risk probability based on precipitation information, R 0 is the precipitation on the day when the geological disaster occurs, R 1 is the precipitation one day before the geological disaster occurs, R 2 is the precipitation two days before the geological disaster occurs, R 3 The maximum hourly precipitation on the day when the geological disaster occurs is fitted with a large number of rainfall-type landslide events, and the model coefficient β is finally determined by using the least squares method. i , the Logistic geological hazard risk assessment model based on precipitation information in the study area can be obtained.

[0014] As a preferred solution, the step 2: introducing soil moisture parameters into the Logistic geological disaster risk assessment model, the specific analysis method is: according to the change characteristics of soil moisture before and after the landslide disaster, the geological disasters are divided into five categories: A. soil moisture continues to be higher than a certain threshold M before and after the landslide occurs, B. frequency change, soil moisture fluctuates significantly before the disaster, C. overall change, soil moisture first increases and then continues to decrease, D. overall change, soil moisture first decreases and then increases and finally tends to stabilize and E. soil moisture has no obvious change characteristics. According to the change characteristics of soil moisture before and after the landslide disaster, it is judged which category of geological disasters the landslide disaster belongs to, and the risk probability value of the landslide disaster risk assessment model integrating soil moisture information is output.

[0015] As a preferred solution, the risk probability value of the landslide disaster risk assessment model integrating soil moisture information is specifically analyzed as follows: if the landslide disaster belongs to Class A of geological disasters, the expression is: where p A It represents the risk probability value of Class A geological disaster output by the landslide risk assessment model integrating soil moisture information. If the landslide disaster belongs to Class B geological disaster, the expression is: where p B It represents the risk probability value of Class B geological disaster output by the landslide risk assessment model integrating soil moisture information. If the landslide disaster belongs to Class C geological disaster, the expression is: where p C The risk probability value of Class C geological disaster output by the landslide risk assessment model integrating soil moisture information is expressed as follows: where p D It represents the risk probability value of Class D geological disaster output by the landslide risk assessment model integrating soil moisture information. If the landslide disaster belongs to Class E geological disaster, the expression is: where p E represents the risk probability value of Class E geological disasters output by the landslide risk assessment model integrating soil moisture information, p 1 It represents the landslide risk probability value obtained by the Logistic geological disaster risk assessment model based on precipitation information, V is the soil moisture value of the target point, and M is the average soil moisture value of the monitoring area.

[0016] As a preferred solution, step 3: introducing surface deformation parameters into the Logistic geological hazard risk assessment model, the specific analysis method is: by using InSAR interferometric synthetic aperture radar technology, the surface deformation variables of the surface in the monitoring area are accurately captured, the deformation rate of the underlying surface is obtained, and then the deformation precipitation sensitivity coefficient is evaluated, and the geological hazard risk probability value that integrates the deformation information is output.

[0017] As a preferred solution, the specific analysis method of the surface deformation amount is as follows: select appropriate SAR satellite data, convert the original SAR data into SAR images through a focusing algorithm, align the SAR images at different times into a unified coordinate system, obtain an interference map containing terrain and deformation information by calculating the phase difference between different SAR images, use the existing digital elevation model, remove the terrain phase in the interference map through differential interferometry, use multi-phase data to analyze and correct the atmospheric impact, restore the absolute phase value through phase unwrapping processing, thereby obtaining a continuous deformation phase, and convert the unwrapped deformation phase into the actual surface deformation amount.

[0018] As a preferred solution, the deformation precipitation sensitivity coefficient is specifically analyzed by: obtaining the deformation precipitation sensitivity coefficient of each monitoring number in the monitoring area, and the calculation formula is: where Kt m represents the deformation precipitation sensitivity coefficient of the mth monitoring, Δt represents the deformation rate of the underlying surface of the mth monitoring, R represents the precipitation of the mth monitoring, the maximum and minimum values ​​of the deformation precipitation sensitivity coefficient are screened out, and the deformation precipitation sensitivity coefficient is normalized. The calculation formula is: where Kt max Indicates the maximum value of the deformation precipitation sensitivity coefficient, Kt min Indicates the minimum value of the deformation precipitation sensitivity coefficient.

[0019] As a preferred solution, the geological disaster risk probability value of the fused deformation information is specifically analyzed by introducing the deformation precipitation sensitivity coefficient into the Logistic geological disaster risk assessment model, and finally achieving a geological disaster risk assessment model that integrates surface deformation. The calculation formula is p 3 =p 1 ×Kt(11), where p 3 is the meteorological risk probability value of geological disasters fused with deformation information, p 1 is the Logistic geological disaster meteorological risk probability value.

[0020] As a preferred solution, the step 4: constructing a geological disaster risk assessment model integrating surface deformation, soil moisture and rainfall information, the specific analysis method is: combining formulas (3), (4), (5), (6), (7), (8) and (11), respectively integrating soil moisture and deformation information into the rainfall-based Logistic model, and constructing a geological disaster risk assessment model integrating surface deformation, soil moisture and rainfall information. The calculation formula is: Among them, p 4 The risk probability value output by the geological disaster risk assessment model integrating surface deformation, soil moisture and rainfall information, R 0 is the precipitation on the day when the geological disaster occurs, R 1 is the precipitation one day before the geological disaster occurs, R 2 is the precipitation two days before the geological disaster occurs, R 3 is the maximum hourly precipitation on the day when the geological disaster occurs, β i is the i-th model coefficient, V is the soil moisture value at the time of geological disaster, M is the average soil moisture value in the monitoring area, p′ is the soil moisture information coefficient related to the soil moisture change characteristics, and Kt is the deformation precipitation sensitivity coefficient.

[0021] Compared with the prior art, the beneficial effects of the present invention are: 1. For the risk assessment of geological disasters induced by rainfall, not only the rainfall on the day, the rainfall on the previous day, and the rainfall on the previous two days are taken into account, but also the surface deformation characteristics of the monitored area and the soil moisture information.

[0022] 2. Compared with the geological disaster risk assessment model that only considers rainfall information, this model adopts a more comprehensive and multi-dimensional assessment method, and the prediction accuracy of the model has been significantly improved.

[0023] 3. Compared with single meteorological observation data, this model uses multi-source observation data to support the model's prediction results, thereby improving the reliability of the model. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0025] Figure 1 The present invention is a schematic flow chart of the steps for implementing the method.

[0026] Figure 2 This is a graph showing soil moisture changes before and after a Class A geological disaster occurs.

[0027] Figure 3 This is a graph showing soil moisture changes before and after a Class B geological disaster occurs.

[0028] Figure 4 This is a graph showing soil moisture changes before and after a Class C geological disaster occurs.

[0029] Figure 5 This is a graph showing soil moisture changes before and after a Class D geological disaster occurs.

[0030] Figure 6 This is a graph showing soil moisture changes before and after a Type E geological disaster occurs. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] Reference Figure 1 As shown, the present invention provides a geological disaster risk assessment model that integrates soil moisture, deformation and rainfall information, including: Step 1: Establishing a Logistic geological disaster risk assessment model based on precipitation information.

[0033] In a specific embodiment, the step 1: establishes a Logistic geological disaster risk assessment model based on precipitation information, and the specific analysis method is: assuming that under the influence of the rainfall independent variable, the probability of a disaster occurring is 1, and the probability of no disaster occurring is 0, p is the predicted probability of a disaster occurring, and the value range is [0,1], then (1-p) is the predicted probability of no disaster occurring, p / (1-p) is the disaster logistic regression occurrence ratio, and its natural logarithm ln[p / (1-p)] is taken, and the independent variable is x 1 ,x 2 ,...,x k , the dependent variable is p, then the Logistic linear regression function can be expressed as: where β i is the model coefficient, i=0,1,2,…,k, and the probability of geological disaster risk based on precipitation information is analyzed.

[0034] In a specific embodiment, the specific analysis method of the geological disaster risk probability based on precipitation information is as follows: According to formula (1), we can get Formula (2) can be used to predict the probability of disaster occurrence under rainfall conditions, obtain rainfall information in historical rainfall-type landslide events in the study area, and obtain rainfall factors, cumulative rainfall factors, and previous rainfall attenuation coefficients. By performing systematic Logistic regression analysis, correlation analysis, and partial correlation analysis on the relationship between rainfall factors, cumulative rainfall factors, and previous rainfall attenuation coefficients and the occurrence and number of disasters, appropriate rainfall factors and previous rainfall attenuation coefficients for the study area are screened out as independent variable factors of the model. where p 1 is the geological disaster risk probability based on precipitation information, R 0 is the precipitation on the day when the geological disaster occurs, R 1 is the precipitation one day before the geological disaster occurs, R 2 is the precipitation two days before the geological disaster occurs, R 3 The maximum hourly precipitation on the day when the geological disaster occurs is fitted with a large number of rainfall-type landslide events, and the model coefficient β is finally determined by using the least squares method. i , the Logistic geological hazard risk assessment model based on precipitation information in the study area can be obtained.

[0035] The present invention not only takes into account the rainfall on the day, the rainfall on the previous day, and the rainfall on the previous two days for the risk assessment of geological disasters induced by rainfall, but also takes into account the surface deformation characteristics of the monitored area and the soil moisture information.

[0036] Step 2: Introduce soil moisture parameters into the Logistic geological hazard risk assessment model.

[0037] In a specific embodiment, step 2: introducing soil moisture parameters into the Logistic geological disaster risk assessment model, the specific analysis method is: according to the change characteristics of soil moisture before and after the landslide disaster, the geological disasters are divided into five categories: A. soil moisture continues to be higher than a certain threshold M before and after the landslide occurs, B. frequency change, soil moisture fluctuates significantly before the disaster, C. overall change, soil moisture first increases and then continues to decrease, D. overall change, soil moisture first decreases and then increases and finally stabilizes, and E. soil moisture has no obvious change characteristics, refer to Figure 2-6 ,According to the changing characteristics of soil moisture before and after the landslide disaster, it is determined which type of geological disaster the landslide disaster belongs to, and the risk probability value of the landslide disaster risk assessment model integrating soil moisture information is output.

[0038] In a specific embodiment, the risk probability value of the landslide disaster risk assessment model integrating soil moisture information is specifically analyzed as follows: if the landslide disaster belongs to Class A of geological disasters, the expression is: where p AIt represents the risk probability value of Class A geological disaster output by the landslide risk assessment model integrating soil moisture information. If the landslide disaster belongs to Class B geological disaster, the expression is: Where pB represents the risk probability value of Class B geological disasters output by the landslide risk assessment model integrating soil moisture information. If the landslide disaster belongs to Class C geological disasters, the expression is: where p C The risk probability value of Class C geological disaster output by the landslide risk assessment model integrating soil moisture information is expressed as follows: where p D It represents the risk probability value of Class D geological disaster output by the landslide risk assessment model integrating soil moisture information. If the landslide disaster belongs to Class E geological disaster, the expression is: where p E represents the risk probability value of Class E geological disasters output by the landslide risk assessment model integrating soil moisture information, p 1 It represents the landslide risk probability value obtained by the Logistic geological disaster risk assessment model based on precipitation information, V is the soil moisture value of the target point, and M is the average soil moisture value of the monitoring area.

[0039] Compared with the geological disaster risk assessment model that only considers rainfall information, the model of the present invention adopts a more comprehensive and multi-dimensional assessment method, and the prediction accuracy of the model is significantly improved.

[0040] Step 3: Introduce surface deformation parameters into the Logistic geological hazard risk assessment model.

[0041] In a specific embodiment, the step 3: introducing the surface deformation parameters into the Logistic geological hazard risk assessment model, the specific analysis method is: by using InSAR interferometric synthetic aperture radar technology, the surface deformation variables of the surface in the monitoring area are accurately captured, the deformation rate of the underlying surface is obtained, and then the deformation precipitation sensitivity coefficient is evaluated, and the geological hazard risk probability value of the fused deformation information is output.

[0042] In a specific embodiment, the surface deformation amount is specifically analyzed by the following method: selecting appropriate SAR satellite data, converting the original SAR data into SAR images through a focusing algorithm, aligning SAR images at different times into a unified coordinate system, obtaining an interference map containing terrain and deformation information by calculating the phase difference between different SAR images, using an existing digital elevation model, removing the terrain phase in the interference map through differential interferometry, using multi-temporal data analysis and correction of atmospheric effects, restoring the absolute phase value through phase unwrapping processing, thereby obtaining a continuous deformation phase, and converting the unwrapped deformation phase into the actual surface deformation amount.

[0043] It should be noted that the selection of a suitable satellite requires consideration of factors such as its re-entry period, resolution and band. A shorter re-entry period can provide more frequent monitoring data, which helps capture rapidly occurring surface deformations, while high-resolution data can provide more detailed surface information. The interferogram also contains phase delays caused by atmospheric inhomogeneities, so multi-temporal data analysis and correction of atmospheric effects are required. The phase in the interferogram is periodic, so phase unwrapping processing is required to restore the absolute phase value.

[0044] In a specific embodiment, the deformation precipitation sensitivity coefficient is specifically analyzed by: obtaining the deformation precipitation sensitivity coefficient of each monitoring number in the monitoring area, and the calculation formula is: where Kt m represents the deformation precipitation sensitivity coefficient of the mth monitoring, Δt represents the deformation rate of the underlying surface of the mth monitoring, R represents the precipitation of the mth monitoring, the maximum and minimum values ​​of the deformation precipitation sensitivity coefficient are screened out, and the deformation precipitation sensitivity coefficient is normalized. The calculation formula is: where Kt max Indicates the maximum value of the deformation precipitation sensitivity coefficient, Kt min Indicates the minimum value of the deformation precipitation sensitivity coefficient.

[0045] In a specific embodiment, the geological disaster risk probability value of the fused deformation information is specifically analyzed by introducing the deformation precipitation sensitivity coefficient into the Logistic geological disaster risk assessment model, and finally achieving a geological disaster risk assessment model that integrates surface deformation. The calculation formula is p 3 =p 1 ×Kt(11), where p 3 is the meteorological risk probability of geological disasters fused with deformation information, p 1 is the Logistic geological disaster meteorological risk probability.

[0046] It should be noted that P 3 When it is greater than 100%, it is set to 100%, and the Kt deformation precipitation sensitivity coefficient is taken as 0.7-1.3.

[0047] Step 4: Construct a geological disaster risk assessment model that integrates surface deformation, soil moisture and rainfall information.

[0048] In a specific embodiment, the step 4: constructing a geological disaster risk assessment model integrating surface deformation, soil moisture and rainfall information, the specific analysis method is: combining formulas (3), (4), (5), (6), (7), (8) and (11), respectively integrating soil moisture and deformation information into the rainfall-based Logistic model, and constructing a geological disaster risk assessment model integrating surface deformation, soil moisture and rainfall information. The calculation formula is: Among them, p 4 The risk probability value output by the geological disaster risk assessment model integrating surface deformation, soil moisture and rainfall information, R 0 is the precipitation on the day when the geological disaster occurs, R 1 is the precipitation one day before the geological disaster occurs, R 2 is the precipitation two days before the geological disaster occurs, R 3 is the maximum hourly precipitation on the day when the geological disaster occurs, β i (i=0,1,2,…,k) is the model coefficient, V is the soil moisture value at the time of geological disaster, unit is cm 3 cm 3 , M is the average soil moisture in the monitoring area, unit: cm 3 cm 3 , p′ is the soil moisture information coefficient related to the soil moisture variation characteristics.

[0049] It should be noted that 0.60, 0.11, 0.02, 0.08 and 0.23 are all soil moisture information coefficients, and p′ is one of them.

[0050] Compared with single meteorological observation data, the model of the present invention utilizes multi-source observation data to support the prediction results of the model, thereby improving the reliability of the model.

[0051] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. A geological disaster risk assessment model integrating soil moisture, deformation and rainfall information, characterized by: include: Step 1: Establish a Logistic geological disaster risk assessment model based on precipitation information; Step 2: Introduce soil moisture parameters into the Logistic geological disaster risk assessment model; Step 3: Introduce the surface deformation parameters into the Logistic geological hazard risk assessment model; Step 4: Construct a geological disaster risk assessment model that integrates surface deformation, soil moisture and rainfall information.

2. The geological disaster risk assessment model integrating soil moisture, deformation and rainfall information according to claim 1 is characterized in that: Step 1: Establish a Logistic geological disaster risk assessment model based on precipitation information. The specific analysis method is as follows: Assume that under the influence of rainfall independent variable, the probability of disaster occurrence is 1, and the probability of no disaster occurrence is 0, p is the predicted probability of disaster occurrence, and the value range is [0,1]. Then (1-p) is the predicted probability of no disaster occurrence, p / (1-p) is the disaster logistic regression occurrence ratio, and its natural logarithm ln[p / (1-p)] is taken. The independent variables are x1, x2, ..., x k , the dependent variable is p, then the Logistic linear regression function can be expressed as: where β i is the i-th model coefficient, and the probability of geological disaster risk based on precipitation information is analyzed.

3. The geological disaster risk assessment model integrating soil moisture, deformation and rainfall information according to claim 2 is characterized in that: The specific analysis method of the geological disaster risk probability based on precipitation information is as follows: According to formula (1), we can get Formula (2) can be used to predict the probability of disaster occurrence under rainfall conditions, obtain rainfall information in historical rainfall-type landslide events in the study area, and obtain rainfall factors, cumulative rainfall factors, and previous rainfall attenuation coefficients. By performing systematic Logistic regression analysis, correlation analysis, and partial correlation analysis on the relationship between rainfall factors, cumulative rainfall factors, and previous rainfall attenuation coefficients and the occurrence and number of disasters, appropriate rainfall factors and previous rainfall attenuation coefficients for the study area are screened out as independent variable factors of the model. Among them, p1 is the geological disaster risk probability based on precipitation information, R0 is the precipitation on the day when the geological disaster occurs, R1 is the precipitation one day before the geological disaster occurs, R2 is the precipitation two days before the geological disaster occurs, and R3 is the maximum hourly precipitation on the day when the geological disaster occurs. By fitting the rainfall information in a large number of rainfall-type landslide events and using the least squares method, the model coefficient β is finally determined. i , the Logistic geological hazard risk assessment model based on precipitation information in the study area can be obtained.

4. The geological disaster risk assessment model integrating soil moisture, deformation and rainfall information according to claim 1 is characterized in that: Step 2: Introduce soil moisture parameters into the Logistic geological disaster risk assessment model. The specific analysis method is as follows: According to the changing characteristics of soil moisture before and after the landslide disaster, geological disasters are divided into five categories: A. soil moisture continues to be higher than a certain threshold M before and after the landslide occurs, B. frequency change, soil moisture fluctuates significantly before the disaster, C. overall change, soil moisture first increases and then continues to decrease, D. overall change, soil moisture first decreases, then increases and finally tends to stabilize, and E. soil moisture has no obvious change characteristics. According to the changing characteristics of soil moisture before and after the landslide disaster, it is determined which category of geological disasters the landslide disaster belongs to, and the risk probability value of the landslide disaster risk assessment model that integrates soil moisture information is output.

5. The geological disaster risk assessment model integrating soil moisture, deformation and rainfall information according to claim 4 is characterized in that: The risk probability value of the landslide disaster risk assessment model that outputs the fusion of soil moisture information is specifically analyzed by the following method: If the landslide disaster belongs to Class A geological disaster, the expression is: where p A It represents the risk probability value of Class A geological disaster output by the landslide risk assessment model integrating soil moisture information. If the landslide disaster belongs to Class B geological disaster, the expression is: where p B It represents the risk probability value of Class B geological disaster output by the landslide risk assessment model integrating soil moisture information. If the landslide disaster belongs to Class C geological disaster, the expression is: where p C The risk probability value of Class C geological disaster output by the landslide risk assessment model integrating soil moisture information is expressed as follows: where p D It represents the risk probability value of Class D geological disaster output by the landslide risk assessment model integrating soil moisture information. If the landslide disaster belongs to Class E geological disaster, the expression is: where p E represents the risk probability value of Class E geological hazards output by the landslide hazard risk assessment model that integrates soil moisture information, p1 represents the landslide risk probability value obtained by the Logistic geological hazard risk assessment model based on precipitation information, V is the soil moisture value of the target point, and M is the average soil moisture value of the monitoring area.

6. The geological disaster risk assessment model integrating soil moisture, deformation and rainfall information according to claim 1 is characterized in that: Step 3: Introducing the surface deformation parameters into the Logistic geological disaster risk assessment model, the specific analysis method is: By using InSAR interferometric synthetic aperture radar technology, the surface deformation variables in the monitoring area can be accurately captured, the deformation rate of the underlying surface can be obtained, and then the deformation-precipitation sensitivity coefficient can be evaluated, and the geological disaster risk probability value that integrates the deformation information can be output.

7. The geological disaster risk assessment model integrating soil moisture, deformation and rainfall information according to claim 6 is characterized in that: The specific analysis method of the surface deformation variable is as follows: Select appropriate SAR satellite data, convert the raw SAR data into SAR images through a focusing algorithm, align the SAR images at different times to a unified coordinate system, and obtain the interference map containing terrain and deformation information by calculating the phase difference between different SAR images. Use the existing digital elevation model to remove the terrain phase in the interference map through differential interferometry processing, use multi-temporal data analysis and correct the atmospheric impact, and restore the absolute phase value through phase unwrapping processing to obtain a continuous deformation phase, which is then converted into the actual surface deformation.

8. The geological disaster risk assessment model integrating soil moisture, deformation and rainfall information according to claim 7 is characterized in that: The specific analysis method for evaluating the deformation precipitation sensitivity coefficient is as follows: The deformation precipitation sensitivity coefficient of each monitoring number in the monitoring area is obtained, and the calculation formula is: (9), where Kt m represents the deformation precipitation sensitivity coefficient of the mth monitoring, Δt represents the deformation rate of the underlying surface of the mth monitoring, R represents the precipitation of the mth monitoring, the maximum and minimum values ​​of the deformation precipitation sensitivity coefficient are screened out, and the deformation precipitation sensitivity coefficient is normalized. The calculation formula is: where Kt max Indicates the maximum value of the deformation precipitation sensitivity coefficient, Kt min Indicates the minimum value of the deformation precipitation sensitivity coefficient.

9. The geological disaster risk assessment model integrating soil moisture, deformation and rainfall information according to claim 8 is characterized in that: The specific analysis method of the geological disaster risk probability value of the fused deformation information is as follows: The deformation-precipitation sensitivity coefficient is introduced into the Logistic geological hazard risk assessment model, and finally a geological hazard risk assessment model integrating surface deformation is obtained. The calculation formula is p3=p1×Kt(11), where p3 is the geological hazard risk probability value integrating deformation information, and p1 is the landslide risk probability value obtained by the Logistic geological hazard risk assessment model based on precipitation information.

10. The geological disaster risk assessment model integrating soil moisture, deformation and rainfall information according to claim 9 is characterized in that: Step 4: Constructing a geological disaster risk assessment model integrating surface deformation, soil moisture and rainfall information. The specific analysis method is as follows: Combining formulas (3), (4), (5), (6), (7), (8) and (11), soil moisture and deformation information are respectively integrated into the rainfall-based Logistic model to construct a geological disaster risk assessment model that integrates surface deformation, soil moisture and rainfall information. The calculation formula is: Among them, p4 is the risk probability value output by the geological disaster risk assessment model that integrates surface deformation, soil moisture and rainfall information, R0 is the precipitation on the day when the geological disaster occurs, R1 is the precipitation one day before the geological disaster occurs, R2 is the precipitation two days before the geological disaster occurs, R3 is the maximum hourly precipitation on the day when the geological disaster occurs, β i is the i-th model coefficient, V is the soil moisture value at the time of geological disaster, M is the average soil moisture value in the monitoring area, p′ is the soil moisture information coefficient related to the soil moisture change characteristics, and Kt is the deformation precipitation sensitivity coefficient.

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