Method for constructing a probability prediction model for water-soil coupled disaster fluid occurrence, and debris flow prediction method

Abstract

The present invention discloses a method for constructing a water-soil coupled disaster fluid occurrence probability prediction model and a debris flow prediction method. The present invention provides a method for constructing a water-soil coupled disaster fluid occurrence probability prediction model. The method utilizes the water-soil coupled movement information in rainfall represented by AEP, introduces the simulation calculation of I-D curve construction technology to generate correlation data between AEP and the range of changes in bulk density of water-soil mixture in the disaster scene to reflect this information, and then uses curve fitting and graphical tools to convert and simplify the information, and finally achieves the problem of reflecting the co-occurrence between AEP and disaster occurrence probability through simple geometric analytical relationships, thereby constructing a target model. The present invention also provides a debris flow occurrence probability calculation method, a debris flow prediction method, and the application of each technical solution. The present invention uses the previous effective precipitation / previous rainfall as the only variable to calculate the probability of disaster fluid occurrence, highly simplifies the disaster prediction scheme, and can quantify the possibility of disaster occurrence.

Description

Technical Field

[0001] The present invention relates to mountain disaster monitoring technology, in particular to a prediction and forecasting technology for mountain disaster fluids caused by rainfall, and belongs to the technical field of mountain disaster monitoring and prevention, and debris flow disaster monitoring and forecasting. Background Art

[0002] Natural disasters triggered by rainfall are a key component of disaster prevention and control in mountainous areas. Among these, floods, sediment-laden floods, and debris flows caused by coupled water-soil movement are of particular concern to both research and production communities. Predicting the likelihood of these disasters and determining their accurate probability of occurrence is a long-standing challenge in mountainous area disaster prevention and control.

[0003] Among the various types of hazardous fluids caused by coupled water-soil movement, debris flows are the most prominent in mountain disaster research due to their destructive power and difficulty in post-disaster recovery. Theoretical research on the formation, occurrence, and prediction of debris flows has become a top priority in mountain disaster prevention and control. Guided by these theoretical findings, a wealth of debris flow prediction technologies have been developed. With the accumulation of research experience, researchers have increasingly emphasized the complex causes of debris flows. The development of debris flow prediction technologies has shifted more towards simultaneously monitoring multiple environmental factors that trigger debris flows, then applying these various monitoring data to appropriate prediction models to ultimately predict the likelihood of a debris flow. This concept in existing technologies is based on the assumption that the more comprehensive the environmental factor data, the more accurate the prediction of debris flow events. However, practical work guided by sound understanding does not necessarily guarantee ideal results. The challenge facing debris flow prediction technology is that, while capital investment and hardware technology can help achieve sufficient data collection for a wide range of environmental factors in the data collection phase, the discrepancies in the quality of prediction models developed for each factor, as well as the degree of integration and coordination between the results of these models, limit the value of multi-factor prediction schemes in both research and production. Furthermore, some multi-factor, "all-round" monitoring and prediction technologies for debris flows and their environments require extremely high capital investment, which is completely disproportionate to the economic levels of debris flow-prone regions. This makes it nearly impossible to achieve genuine social benefits and to obtain technical feedback in disaster prevention practices, limiting further technological improvement.

[0004] In reality, the formation and occurrence of various types of hazard flows in mountainous areas caused by coupled water-soil movement, regardless of the combined effects of multiple factors, are primarily driven by precipitation (called induced precipitation). Taking debris flows as an example, induced precipitation is divided into pre-event precipitation and on-event precipitation based on the time of the debris flow. Pre-event precipitation refers to the precipitation that occurred up to the day before the debris flow. The metric used for measuring pre-event precipitation is the portion of precipitation remaining in the soil before the debris flow formed, namely antecedent effective precipitation (AEP), also known as antecedent soil moisture content, or simply antecedent rainfall. In fact, antecedent effective precipitation / antecedent soil moisture content / antecedent rainfall reflects not only the precipitation conditions prior to the debris flow but also the soil conditions prior to the debris flow, including soil saturation, shear strength, and the stability of loose reserves on the slope. Therefore, this metric is a comprehensive indicator that, to a certain extent, represents the starting point, or at least a key node, of the water-soil coupling process associated with the disaster. In the theoretical research and various technologies for predicting debris flow occurrence, the previous effective precipitation has always been regarded as the rainfall index that contributes the most to the formation of debris flow.

[0005] Existing technologies already provide basic empirical formulas or methods for calculating effective antecedent precipitation based on simulated hydrological processes. These methods, based on meteorological data provided by existing meteorological monitoring systems and regional underlying surface data from existing resource surveys, can cost-effectively calculate effective antecedent precipitation indicators for debris flow forecasting. On this basis, if a method for calculating the probability of occurrence of a hazardous flow (P = f(AEP)) based on a single indicator of effective antecedent precipitation could be developed, this would greatly simplify the prediction of precipitation-induced water-soil coupled disasters, thereby bringing numerous beneficial effects in various technical, social, and economic aspects.

[0006] However, existing technologies have not yet been able to achieve the above goals. They still rely on the comprehensive use of previous effective precipitation and other indicators to construct disaster prediction plans, and can only obtain qualitative descriptions of the possibility of disasters. Summary of the Invention

[0007] In the present invention, natural disasters such as floods, sand-laden floods, and debris flows caused by rainfall and due to water-soil coupled movement are collectively referred to as "water-soil coupled disaster fluids", or "disaster fluids" for short.

[0008] The purpose of the present invention is to address the deficiencies of the existing technology and provide a method for calculating the probability of occurrence of water-soil coupled disaster fluids based on the AEP single indicator of previous effective precipitation, as well as a method for calculating the probability of occurrence of debris flows.

[0009] To achieve the above objectives, the present invention first provides a method for constructing a probability prediction model for the occurrence of disaster fluids induced by rainfall, and the technical solution is as follows:

[0010] A method for constructing a water-soil coupled disaster fluid occurrence probability prediction model, characterized by:

[0011] Step S1: Delineate the study area, complete the field survey, and collect basic information about the study area;

[0012] Step S2: Determine the change range [ρ1, ρ2] of the bulk density of the disaster fluid to be predicted in the study area based on the basic data of the study area, determine the effective precipitation AEP' for the hydrological simulation calculation, and construct the ID curve threshold I = δD under the conditions of the effective precipitation AEP' in the study area. β ,Sure

[0013] ID curve ID1 corresponding to ρ1, ID curve ID2 corresponding to ρ2;

[0014] Step S3: Construct the area S between curve ID1 and curve ID2 on the DoI coordinate system expressed by AEP ID12 The function expression S ID12 =f(AEP), construct the triangle area S formed by the intersection of curve ID1 with the D axis and the I axis on the DoI coordinate system expressed by AEP ID1 Function expression

[0015] S ID1 =f(AEP);

[0016] Step S4: Construct S expressed by AEP ID12 With S ID1 The functional expression of the ratio

[0017] The function P that passes the test ID This is the constructed prediction model.

[0018] The construction of each ID curve in the above prediction model construction method can be achieved using the existing technology ZL 2018107475705.

[0019] The technical principle of the above prediction model construction method is mainly that the essence of precipitation-type disaster fluid is a mixture formed by the coupling of water and soil. The difference between different types of disaster fluids lies mainly in the difference in the proportion of solid and liquid phases. The physical index is reflected in the mixture bulk density ρ, which is also the bulk density ρ of the disaster fluid. For example, ρ = 1.2t / m 3 ~2.2t / m 3The hazardous fluid is called a debris flow. Therefore, monitoring water-soil mixtures with different bulk density ranges [ρ] induced by precipitation is equivalent to monitoring different types of hazardous fluids. ZL 2018107475705 links the preceding effective precipitation (AEP) with changes in water-soil coupling characteristics, providing a tool for simulating and calculating the dynamic changes in bulk density ρ of the water-soil coupling under different AEP conditions. Therefore, this existing technology can use AEP to capture the specific water-soil mixture / hazardous fluid [ρ] to be monitored. However, the numerical simulation method used in ZL 2018107475705 cannot yet reflect the co-occurrence probability of a certain AEP condition and a specific [ρ]. By graphically representing the dynamic process of bulk density ρ of the water-soil coupling triggered by AEP and then expressing it through a function, this co-occurrence probability can be quantified. Extensive simulation work conducted in the early stages of this invention has shown that the co-occurrence probability of AEP conditions and a specific [ρ] can indeed be accurately expressed through the construction of a function. The verified function expression can be used for disaster prediction.

[0020] The present invention also provides the following technical solutions:

[0021] The application of the above-mentioned method for constructing the probability prediction model of water-soil coupled disaster fluid occurrence in the monitoring and forecasting of water-soil coupled disaster fluid in mountainous areas.

[0022] A method and / or system for monitoring and forecasting water-soil coupled disaster fluids in mountainous areas is implemented using the above-mentioned method for constructing a water-soil coupled disaster fluid occurrence probability prediction model.

[0023] The above-mentioned method for constructing the probability prediction model of water-soil coupled disaster fluid occurrence is effective in calculating the probability p of disaster fluid induced by rainfall events that meet the ID combination conditions. ob The application of the ID combination rainfall condition is rainfall intensity I = 0 ~ log10 (I 1max ), rainfall duration D = 0 ~ log10 (D 1max ).

[0024] The method for predicting water-soil coupled disaster fluids is characterized by: demarcating a water-soil coupled disaster fluid forecast area, completing a field survey, building a water-soil coupled disaster fluid occurrence probability prediction model for the forecast area, collecting rainfall data for the forecast area, calculating the effective precipitation AEP before the forecast day, and calculating the probability of disaster fluid occurrence P according to the prediction model. ob , and generate forecasts.

[0025] The prediction model constructed by the method for constructing the probability prediction model of water-soil coupling disaster fluid occurrence in the present invention can answer the following questions: For a certain day in the study area, the number of rainfall conditions that meet the ID combination (rainfall intensity I = 0 ~ log10 (I 1max ), rainfall duration D = 0 ~ log10 (D 1maxHow likely is it that a rainfall event of this magnitude will induce a certain type of catastrophic fluid phenomenon? 1max With D 1max They respectively refer to the rainfall intensity (I value) at the intersection of curve ID1 and the I axis (corresponding to D = 1h) and the rainfall duration (D value) at the intersection with the D axis (corresponding to I = 1mm) in the ID curve diagram.

[0026] Accordingly, the present invention provides the following solutions:

[0027] Based on previous research results, this invention also provides a specific prediction model for the probability of debris flow, which is used to measure the probability of debris flow. Its technical solution is as follows:

[0028] A method for calculating the probability of debris flow occurrence, characterized by: calculating the probability p of a debris flow induced by a rainfall event that meets the ID combination rainfall conditions in a monitoring area dbf First, calculate the monitoring area, complete the on-site investigation of the monitoring area, and obtain the basic data of the monitoring area; secondly, calculate the effective precipitation AEP of the monitoring area in the early stage; finally, calculate P according to formula 1 dbf ,

[0029] P dbf =0.0061AEP 2 -0.2959AEP+10.728 Formula 1

[0030] The ID combined rainfall condition is rainfall intensity I=0~log10(I 1.2max ), rainfall duration D = 0 ~ log10 (D 1.2max ).

[0031] The present invention also provides the following technical solutions:

[0032] The application of the above-mentioned debris flow occurrence probability calculation method in debris flow monitoring and forecasting.

[0033] A debris flow prediction method using the above debris flow probability calculation method is characterized by: demarcating a debris flow forecast area, collecting rainfall data in the forecast area, calculating the effective precipitation AEP before the forecast day, and calculating the debris flow probability P according to formula 1. dbf , and generate forecasts.

[0034] The field investigation referred to in the above-mentioned technical solutions of the present invention includes various surveying and mapping, measurement, simulation experimental testing of the watershed / channel site, acquisition of historical disaster records, and acquisition of empirical data for reference and reference.

[0035] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The research field has always known that there is a high correlation between the AEP index and the water-soil coupled disaster fluid, and AEP is regarded as a key factor in debris flow prediction. However, the prior art has never been able to analyze the transmission process mechanism between AEP and disaster fluid, and therefore has never been able to construct an effective technical solution to fully display the process information of disaster formation and evolution contained in AEP, let alone capture, extract and fully utilize it. For a long time, the prior art's understanding of the role of AEP in the occurrence of water-soil coupled disaster fluid has remained at a qualitative description such as the larger the AEP value, the higher the possibility of a rainfall inducing debris flow, and it is impossible to quantify this "probability". Since it cannot be quantified, it cannot be effectively utilized, which is manifested in the following ways: on the one hand, there is a lack of accurate probability data, which cannot provide a calculation basis for further various technologies; on the other hand, although it is feasible in principle, the actual disaster occurrence prediction technology solution still cannot use AEP as the only variable, and must be combined with the use of other observation indicators. The present invention solves the above problems and provides a solution for constructing a water-soil coupled disaster fluid occurrence probability prediction model with the previous effective precipitation AEP as a single variable. This solution makes full use of the information on the coupled movement of water and soil during rainfall represented by AEP, introduces the simulation calculation process of the existing ID curve construction technology to generate the correlation data between the AEP conditions and the water-soil mixture bulk density change interval [ρ] in the disaster scene to reflect this information, and then uses curve fitting and graphical tools to convert and simplify this information, and finally achieves the problem of reflecting the co-occurrence between AEP conditions and disaster probability through simple geometric analytical relationships, thereby solving the problem of target model construction. This prediction model construction technical solution can be applied to the calculation of the probability of occurrence of various water-soil coupled disasters, and obtain an accurate probability value of occurrence, thereby providing a core technology for disaster monitoring and forecasting. (2) The method for calculating the probability of debris flow occurrence provided by the present invention adopts the technical concept of the above-mentioned prediction model construction method of the present invention, and the prediction model determined after repeated screening and refinement based on simulation calculations of a large amount of data can provide a quick and sensitive tool for the prediction and forecast of debris flow occurrence. (3) The ultimate goals of the technical concept of the present invention are twofold: first, to fully utilize the value of the AEP index to simplify the forecasting scheme for water-soil coupled disaster fluids represented by debris flows; second, to quantify the "high / low probability of disaster occurrence" that can only be described qualitatively by existing technologies. Each technical solution under this concept can greatly simplify the prediction scheme for water-soil coupled disasters induced by precipitation, improve the efficiency of the prediction process, enhance the timeliness of the prediction results, reduce the engineering investment in the debris flow prediction system, and increase the promotion and popularization of debris flow prediction, benefiting the society in areas prone to debris flows and feeding back to the improvement of prediction technology.By giving a quantitative result value to the possibility of disaster occurrence, each invention scheme can significantly improve the utilization value of existing disaster occurrence prediction and forecast results, provide a technical calculation basis for various disaster prevention and mitigation technology research after disaster prediction and forecast, and improve the clarity, pertinence and effectiveness of disaster response in prevention and control engineering practice. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is the digital elevation model (DEM) map of Jiangjiagou.

[0037] Figure 2 (a) Figure 2 (b) is the ID threshold curve of Jiangjiagou debris flow with different AEPs.

[0038] Figure 3 It's S ID12 and AEP change trend chart.

[0039] Figure 4 It's S ID1 and AEP change trend chart.

[0040] Figure 5 It's P ID and AEP change trend chart. DETAILED DESCRIPTION

[0041] The preferred embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0042] Example 1

[0043] like Figures 1 to 5 As shown, the method of the present invention is used to construct a probability prediction model for Jiangjiagou debris flow.

[0044] 1. Jiangjiagou Research Area

[0045] Jiangjiagou is the research area. Located in Dongchuan District, Kunming City, Yunnan Province, Jiangjiagou is a national-level field observation station. Currently, 7 fully automatic rain gauges have been deployed ( Figure 1 The basic data of the study area required to complete the subsequent steps mainly include DEM, geotechnical parameters, hydrological parameters, NDVI, soil type, land use, etc., which can be obtained by existing technologies and are therefore omitted. Figure 1 This is the digital elevation model (DEM) map of Jiangjiagou.

[0046] 2. Construct ID curve

[0047] According to the collected basic data of Jiangjiagou, the predicted disaster fluid is a typical debris flow, and the bulk density ρ varies in the range [1.2, 2.2], that is, the lower limit of the interval ρ1=1.2t / m 3 Upper limit ρ2=2.3t / m3 .

[0048] The ID curve threshold I = δD in Jiangjiagou under different conditions of effective antecedent precipitation AEP′ (20 mm, 25 mm, 30 mm, 35 mm, 40 mm, 45 mm, 50 mm, 55 mm, 60 mm, 65 mm, 70 mm, 75 mm, 80 mm, 85 mm, 90 mm, 95 mm, and 100 mm, respectively) was calculated using the method of ZL 2018107475705. β , a database of ID curves corresponding to different water-soil mixtures ρ under different AEP′ conditions is obtained. Figure 2 (a) Figure 2 (b) is the ID threshold curve of Jiangjiagou debris flow with different AEPs.

[0049] 3. Construct the area function S=f(AEP) on the coordinate axis

[0050] Count all curve IDs on the ID curve graph 1.2 (ρ1=1.2) and curve ID 2.2 (ρ2=2.2) Area S ID12 (Table 1), construct S expressed by AEP ID12 Function, there is formula 2. Figure 3 It's S ID12 and AEP change trend chart.

[0051] Table 1 Different AEPs and their corresponding S ID1 、S ID12

[0052] AEP <![CDATA[S ID1 ]]> <![CDATA[S ID12 ]]> 20 1.614 0.195 25 1.570 0.200 30 1.546 0.218 35 1.496 0.227 40 1.434 0.226 45 1.361 0.225 50 1.277 0.222 55 1.195 0.238 60 1.092 0.250 65 1.007 0.271 70 0.918 0.299 75 0.863 0.321 80 0.821 0.327 85 0.783 0.352 90 0.775 0.394 95 0.774 0.395 100 0.772 0.418

[0053] S ID12 =3*10 -5 AEP 2 -0.0014AEP+0.2169 Formula 2

[0054] Count all curve IDs on the ID curve graph 1.2 (ρ1=1.2) The area of the triangle formed by the intersection of the D axis and the I axis is S ID1 (Table 1), construct S expressed by AEP ID1 Function, there is formula 3. Figure 4 It's S ID1 and AEP change trend chart.

[0055] S ID1 =4*10 -6 AEP 3 -0.0007AEP 2 +0.0216AEP+1.4216 Equation 3

[0056] 4. Construct area ratio function P = f(AEP)

[0057] Using Equation 2 and Equation 3, construct the AEP expression S ID12 With S ID1 The ratio P ID The function expression of is formula 4. Figure 5 It's P ID and AEP change trend chart.

[0058] P ID =0.0061AEP 2 -0.2959AEP+10.728 Equation 4

[0059] Formula 4 is tested by regression R 2 =0.9872, which is the constructed Jiangjiagou debris flow prediction model, so there is formula 1.

[0060] Example 2

[0061] The model constructed by Example 1 is used to predict the occurrence of Jiangjiagou debris flow.

[0062] Since the rain gauges in the public meteorological monitoring system are generally located in the upstream of large and small river basins, so that the meteorological center can timely evaluate and respond to the hydrological changes of the entire river basin by monitoring the rainfall data in the upstream area of the river basin, in this specific implementation, the rain gauge located in the upstream of Jiangjiagou is selected ( Figure 1 The data in the black ellipse are used as the calculation data source.

[0063] After collecting the rainfall data from the above-mentioned rainfall stations, the rainfall attenuation empirical formula (n = 15 days in the calculation) was used to calculate the effective rainfall AEP of Jiangjiagou on the six forecast days (the calculation process reference: Zhang Shaojie et al., Determination of effective rainfall in the early stage of debris flow forecast based on hydrological process, Advances in Water Science. 2015, 26(01)). Then, each AEP was substituted into formula 1 to calculate the probability of debris flow occurrence P on each forecast day. dbf The results are shown in Table 2.

[0064] Table 2 AEP of Jiangjiagou on the six forecast days

[0065] Forecast Day 19980812 20060820 20170703 20170707 20070726 20070828 AEP 36.2mm 23.5mm 33.1mm 26.6mm 67.5mm 47.2mm <![CDATA[P dbf ]]> 8.7％ 7.1％ 8.7％ 7.1％ 18.5％ 10.3％

[0066] Each calculated probability value P in Table 2 dbfIn debris flow forecasting, this means that if the rainfall on a given day meets the specified conditions, the likelihood of a debris flow in the Jiangjiagou watershed can be quantified as a probability value. For example, on July 7, 2017, if the meteorological forecast rainfall data indicates that the rainfall on that day meets the ID combination rainfall conditions, the probability of a debris flow is 7.1%.

[0067] The ID combination rainfall conditions are:

[0068] Rainfall intensity I=0~log10(I 1.2max ). I 1.2max In the ID curve diagram, the curve ID 1.2 (ρ1=1.2) The I value of the intersection with the I axis. For example, on July 7, 2017, I 1.2max =26.24mm, log10(I 1.2max )=1.42mm.

[0069] Rainfall duration D = 0 ~ log10 (D 1.2max ). D 1.2max Refers to the curve ID in the ID curve diagram 1.2 (ρ1=1.2) D value of the intersection with D axis. For example, on July 7, 2017, D 1.2max =143h, log10(D 1.2max )=2.2h.

[0070] Verification of the calculation results: Since we can only observe whether a debris flow occurs or not, it is difficult to directly verify the probability of debris flow occurrence for each of the above-mentioned forecast days. Therefore, it is necessary to adopt another approach to verify the results of the calculation method of the present invention. We collected all rainfall data from 2006 to 2010 in Jiangjiagou, totaling 206 rainfalls. During these 206 rainfalls, a total of 36 rainfalls had an AEP of about 30 mm (25 mm to 35 mm), of which 3 had debris flow occurrences, with a probability of 3 / 36 = 8.3%, which is close to the probability value corresponding to AEP = 33.1 mm in Table 2 (the rainfall conditions corresponding to each debris flow also meet the ID combination rainfall conditions of the present invention). Although this result verification seems rough, it is something that current disaster fluid occurrence prediction and forecasting technologies cannot provide.

Claims

1. A method for constructing a probability prediction model for water-soil coupled disaster fluid occurrence, characterized by: Step S1: Delineate the study area, complete the field survey, and collect basic information about the study area; Step S2: Determine the change range [ρ1, ρ2] of the bulk density of the disaster fluid to be predicted in the study area based on the basic data of the study area, determine the effective precipitation AEP' for the hydrological simulation calculation, and construct the ID curve threshold I = δD under the conditions of the effective precipitation AEP' in the study area. β , determine the ID curve ID1 corresponding to ρ1 and the ID curve ID2 corresponding to ρ2; Step S3: Construct the area S between curve ID1 and curve ID2 on the DoI coordinate system expressed by AEP ID12 The function expression S ID12 =f(AEP), construct the triangle area S formed by the intersection of curve ID1 with the D axis and the I axis on the DoI coordinate system expressed by AEP ID1 The function expression S ID1 =f(AEP); Step S4: Construct S expressed by AEP ID12 With S ID1 The functional expression of the ratio The function P that passes the test ID This is the constructed prediction model.

2. Application of the method for constructing a water-soil coupled disaster fluid occurrence probability prediction model as described in claim 1 in monitoring and forecasting water-soil coupled disaster fluids in mountainous areas.

3. The method for constructing a water-soil coupled disaster fluid occurrence probability prediction model according to claim 1 is used to calculate the probability p of a disaster fluid induced by a rainfall event that meets the ID combined rainfall conditions. ob The application of the ID combination rainfall condition is rainfall intensity I = 0 ~ log10 (I 1max ), rainfall duration D = 0 ~ log10 (D 1max ), I 1max Refers to the rainfall intensity I value at the intersection of curve ID1 and I axis in the ID curve diagram. At this time, the corresponding D=1h, D 1max It refers to the rainfall duration D value at the intersection of curve ID1 and D axis in the ID curve diagram, and the corresponding I at this time is 1mm.

4. A method for monitoring and forecasting water-soil coupled disaster fluids in mountainous areas, implemented by utilizing the method for constructing a water-soil coupled disaster fluid occurrence probability prediction model as described in claim 1.

5. A method for predicting water-soil coupled hazard fluids using the method for constructing a water-soil coupled hazard fluid occurrence probability prediction model according to claim 1, characterized in that: Delineate the water-soil coupled disaster fluid forecast area, complete the on-site investigation, build the water-soil coupled disaster fluid occurrence probability prediction model in the forecast area, collect rainfall data in the forecast area, calculate the effective precipitation AEP before the forecast day, and calculate the probability of disaster fluid occurrence P according to the prediction model. ob , and generate forecasts.

6. A method for calculating the probability of debris flow occurrence using the method for constructing a water-soil coupled disaster fluid occurrence probability prediction model according to claim 1, characterized in that: Used to calculate the probability p of a debris flow induced by a rainfall event that meets the ID combination rainfall conditions in the monitoring area dbf First, calculate the monitoring area, complete the on-site investigation of the monitoring area, and obtain the basic data of the monitoring area; secondly, calculate the effective precipitation AEP of the monitoring area in the early stage; finally, calculate P according to formula 1 dbf , P dbf =0.0061AEP 2 -0.2959AEP+10.728 Formula 1 The ID combined rainfall condition is rainfall intensity I=0~log10(I 1.2max ), rainfall duration D = 0 ~ log10 (D 1.2max ), I 1.2max Refers to the curve ID in the ID curve diagram 1.2 I value and D at the intersection with the I axis 1.2max Refers to the curve ID in the ID curve diagram 1.2 D value of the intersection with the D axis, ID 1.2 This is the ID curve when ρ1=1.

2.

7. Application of the debris flow occurrence probability calculation method according to claim 6 in debris flow monitoring and forecasting.

8. A debris flow prediction method implemented using the debris flow probability calculation method according to claim 6, characterized in that: Delineate the debris flow forecast area, collect rainfall data in the forecast area, calculate the effective precipitation AEP before the forecast day, and calculate the probability of debris flow occurrence P according to formula 1 dbf , and generate forecasts.

9. A monitoring and forecasting system for water-soil coupled disaster fluids in mountainous areas realized by utilizing the method for constructing a water-soil coupled disaster fluid occurrence probability prediction model as described in claim 1.

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