1. A debris flow disaster early warning method based on runoff convergence simulation is characterized by comprising the following steps:

s1, performing small watershed division on the early warning area to serve as an early warning basic unit;

s2, obtaining a debris flow disaster risk probability background value H, runoff confluent water depth grading G and a gully opening distance factor L, and calculating a debris flow disaster early warning result Y, wherein the calculation mode of Y is as follows:

Y＝(aG+bH)×L

in the formula: y is a debris flow disaster early warning result, G is runoff confluent water depth grading, H is a debris flow disaster risk probability background value, and a and b are weights of the runoff confluent water depth and the debris flow disaster risk probability background value respectively;

s4, calculating the weight of a debris flow disaster risk probability background value H and the values of the weights a and b of runoff confluence water depth grade G based on historical debris flow disaster point data of the early warning area;

and S3, calculating a debris flow disaster early warning result Y based on the debris flow disaster risk probability background value H, the runoff confluent water depth grade G and the gully distance factor L within the time to be predicted in the early warning area, so as to perform debris flow disaster early warning judgment.

2. The method according to claim 1, wherein in S2, the background value H of the debris flow disaster risk probability is obtained by:

establishing a logistic regression relation between the debris flow disaster risk probability P and each individual induction factor:

wherein P is debris flow disaster risk probability, X₁…X_nIs a separate inducing factor affecting the occurrence of geological disasters, B₁…B_nIs a logistic regression coefficient corresponding to each individual induction factor, A is a regression equation constant term;

and calculating a logistic regression coefficient corresponding to each individual induction factor based on historical data, wherein the debris flow disaster risk probability P at the moment is used as a debris flow disaster risk probability background value H.

3. The method of claim 2, wherein the individual inducement factors comprise one of bare rock rate, basin area, vertical dip, river, road, fault density, land utilization, soil type, hidden danger point density, valley density, annual precipitation, or any combination thereof.

4. The method according to claim 1, wherein in the step S2, the radial flow confluence water depth grade G is obtained by:

carrying out grid mode decomposition on the Navier-Stokes equation, respectively calculating the water depth and water speed of each grid on a time slice according to an advection term, a pressure term and an external force term to obtain the runoff confluence water depth W, and grading the W to obtain G;

wherein the water balance formula in the Navier-Stokes equation is set as:

in the formula (I), the compound is shown in the specification,W_c ⁿ⁺¹respectively setting the initial water depth and the output water depth of the central grid c on the time slice; respectively the precipitation, glacier ablation, infiltration and evaporation on the central grid time slice;andis the advection term in the model.

5. The method of claim 4, wherein the glacier ablation volume is obtained by daily ablation volume calculation.

6. The method according to claim 1, wherein in S2, the notch distance factor L is obtained by:

and (3) carrying out reverse distance normalization on the groove distance to obtain a groove distance factor L:

wherein R is the buffer distance threshold and d is the trench opening distance.

7. The method according to claim 4, wherein the runoff confluent water depth W is graded based on an absolute value range of the runoff confluent water depth W, and a grade G score of the runoff confluent water depth is used as a debris flow disaster early warning factor.

8. The utility model provides a mud-rock flow calamity early warning system based on runoff confluence simulation which characterized in that, the system includes:

the debris flow disaster risk calculation module is used for determining a probability background value H of the debris flow disaster risk in the early warning area;

the water depth calculation module is used for calculating the runoff confluence water depth W based on the rasterized Navier-Stokes equation;

the gully distance factor calculation module is used for acquiring a gully distance factor L related to the debris flow disaster risk based on the debris flow buffer distance;

the disaster early warning result calculation module is used for calculating a debris flow disaster early warning result Y based on the debris flow disaster risk probability background value H, the runoff confluent water depth grading G and the gully distance factor L;

the early warning module is used for carrying out early warning on the debris flow disaster based on the debris flow disaster early warning result Y;

and the data input module is used for acquiring the debris flow disaster point data in the early warning area.

9. The system according to claim 8, wherein the debris flow disaster early warning result Y is calculated in a manner that:

Y＝(aG+bH)×L

in the formula: y is a debris flow disaster early warning result, G is runoff confluent water depth grading, H is a debris flow disaster risk probability background value, and a and b are weights of the runoff confluent water depth grading and the debris flow disaster risk probability background value respectively.

10. The system according to claim 8, wherein the background value H of the risk probability of debris flow disaster is obtained by:

establishing a logistic regression relation between the debris flow disaster risk probability P and each individual induction factor:

wherein P is debris flow disaster risk probability, X₁…X_nIs a separate inducing factor affecting the occurrence of geological disasters, B₁…B_nIs a logistic regression coefficient corresponding to each individual induction factor, A is a regression equation constant term;

and calculating a logistic regression coefficient corresponding to each individual induction factor based on historical data, wherein the debris flow disaster risk probability P at the moment is used as a debris flow disaster risk probability background value H.

Background

In the middle and western regions of China, the landform of mountains is abundant, subtropical regions are in humid climate, and summer is humid and rainy. Under the landform and climate conditions, frequent occurrence of debris flow disasters caused by heavy rainfall annually poses great threat to the safety of local lives and properties. In certain terrain conditions, sudden strong precipitation can cause the mountain object source to move and to be accumulated in the valley, and the scouring of the strong precipitation and the movement and accumulation of the object source are accompanied, namely, a mud-rock flow is formed. The debris flow has a high explosion speed and carries a large amount of sources, and the places where the sources pass by, especially residents, roads, farmlands and the like below the ditches, cause huge loss, thus causing destructive damage and irreparable loss to local people.

China is nationwide, particularly the areas in the middle and the west belong to areas where debris flow disasters are mainly prone to occur and high in incidence, and particularly during rainy seasons of 4-9 months, the rainfall is sufficient, so that the debris flow disasters are easily generated under local topographic and geological conditions, and the damage to local economy and population safety is caused; on the other hand, due to global warming, glaciers are ablated in a large area, and serious losses such as casualties of personnel and livestock, house and highway washout and the like are caused by debris flow disasters with different degrees of glaciers ablation every year, so that the debris flow disasters become problems which seriously restrict the development of the economy and the society.

Therefore, an early warning model of debris flow disasters is established, and debris flow is predicted in advance and cannot be predicted slowly. The method for constructing the debris flow disaster early warning model by exciting rainfall for several days is a construction method of most debris flow disaster early warning models at present. In addition, most of the existing domestic debris flow early warning realizes the monitoring and early warning of debris flow by burying a large number of detectors in high-power areas and acquiring information such as dip angles of specific address structures, rock-soil deformation and the like. However, the existing common modes and products cannot well perform early warning of debris flow disasters within a long time range, and the amount of equipment required to be invested in the early stage is large, so that effective monitoring and early warning of the debris flow disasters within a large range cannot be realized.

Disclosure of Invention

Aiming at the defects in the prior art, an inventor team discovers that debris flow disasters are mainly near river valleys by analyzing the correlation between river channel distances, gully distances and debris flow historical disasters, and the scheme is an improved mode, and introduces runoff confluence simulation into a debris flow disaster early warning model to simulate water depth to replace the traditional surface rainfall, and establishes a debris flow disaster early warning and forecasting model by integrating gully distance factors and danger division factors to improve the space precision of debris flow disaster early warning and provide scientific basis for disaster prevention and reduction.

Specifically, the scheme provides the following specific technical scheme:

on one hand, the invention provides a debris flow disaster early warning method based on runoff confluence simulation, which comprises the following steps:

s1, performing small watershed division on the early warning area to serve as an early warning basic unit;

s2, obtaining a debris flow disaster risk probability background value H, runoff confluent water depth grading G and a gully opening distance factor L, and calculating a debris flow disaster early warning result Y, wherein the calculation mode of Y is as follows:

Y＝(aG+bH)×L

in the formula: y is a debris flow disaster early warning result, G is runoff confluent water depth grading, H is a debris flow disaster risk probability background value, and a and b are weights of the runoff confluent water depth and the debris flow disaster risk probability background value respectively;

s4, calculating the weight of a debris flow disaster risk probability background value H and the values of the weights a and b of runoff confluence water depth grade G based on historical debris flow disaster point data of the early warning area;

and S3, calculating a debris flow disaster early warning result Y based on the debris flow disaster risk probability background value H, the runoff confluent water depth grade G and the gully distance factor L within the time to be predicted in the early warning area, so as to perform debris flow disaster early warning judgment.

Preferably, in S2, the debris flow disaster risk probability background value H is obtained by:

establishing a logistic regression relation between the debris flow disaster risk probability P and each individual induction factor:

wherein P is debris flow disaster risk probability, X₁…X_nIs a separate inducing factor affecting the occurrence of geological disasters, B₁…B_nIs a logistic regression coefficient corresponding to each individual induction factor, A is a regression equation constant term;

and calculating a logistic regression coefficient corresponding to each individual induction factor based on historical data, wherein the debris flow disaster risk probability P at the moment is used as a debris flow disaster risk probability background value H.

Preferably, the individual inducing factors include one of bare rock rate, river basin area, longitudinal gradient, river, road, fault density, land utilization, soil type, hidden danger point density, valley density, annual precipitation amount or any combination thereof.

Preferably, in S2, the radial inflow/drainage depth classification G is obtained by:

carrying out grid mode decomposition on the Navier-Stokes equation, respectively calculating the water depth and water speed of each grid on a time slice according to an advection term, a pressure term and an external force term to obtain the runoff confluence water depth W, and grading the W to obtain G;

wherein the water balance formula in the Navier-Stokes equation is set as:

in the formula (I), the compound is shown in the specification,respectively setting the initial water depth and the output water depth of the central grid c on the time slice; respectively the precipitation, glacier ablation, infiltration and evaporation on the central grid time slice;andis the advection term in the model.

Preferably, the glacier ablation amount is obtained by daily ablation amount calculation.

Preferably, in S2, the trench distance factor L is obtained by:

and (3) carrying out reverse distance normalization on the groove distance to obtain a groove distance factor L:

wherein R is the buffer distance threshold and d is the trench opening distance.

Preferably, the runoff confluence water depth W is graded based on the absolute value range of the runoff confluence water depth W, and the grade value of the runoff confluence water depth G is used as a debris flow disaster early warning factor.

In addition, the invention also provides a debris flow disaster early warning system based on runoff confluence simulation, which comprises:

the debris flow disaster risk calculation module is used for determining a probability background value H of the debris flow disaster risk in the early warning area;

the water depth calculation module is used for calculating the runoff confluence water depth based on the rasterized Navier-Stokes equation;

the gully distance factor calculation module is used for acquiring a gully distance factor L related to the debris flow disaster risk based on the debris flow buffer distance;

the disaster early warning result calculation module is used for calculating a debris flow disaster early warning result Y based on the debris flow disaster risk probability background value H, the runoff confluent water depth grading G and the gully distance factor L;

the early warning module is used for carrying out early warning on the debris flow disaster based on the debris flow disaster early warning result Y;

and the data input module is used for acquiring the debris flow disaster point data in the early warning area.

Preferably, the calculation mode of the debris flow disaster early warning result Y is as follows:

Y＝(aG+bH)×L

in the formula: y is a debris flow disaster early warning result, G is runoff confluent water depth grading, H is a debris flow disaster risk probability background value, and a and b are weights of the runoff confluent water depth and the debris flow disaster risk probability background value respectively.

Preferably, the acquisition mode of the background value H of the debris flow disaster risk probability is as follows:

establishing a logistic regression relation between the debris flow disaster risk probability P and each individual induction factor:

wherein P is debris flow disaster risk probability, X₁…X_nIs a separate inducing factor affecting the occurrence of geological disasters, B₁…B_nIs that each sheetThe logistic regression coefficient corresponding to the single induction factor, wherein A is a regression equation constant term;

and calculating a logistic regression coefficient corresponding to each individual induction factor based on historical data, wherein the debris flow disaster risk probability P at the moment is used as a debris flow disaster risk probability background value H.

Compared with the prior art, the technical scheme of the invention can more quickly and accurately early warn the debris flow disasters, greatly improve the early warning time range, greatly enhance the space precision of the debris flow disaster early warning, well realize disaster monitoring in a large area and a long time range, and effectively provide scientific basis for disaster prevention and reduction.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

FIG. 1 is a river buffering distance analysis of a debris flow historical disaster point according to an embodiment of the present invention;

FIG. 2 shows the distance between the gullies of the river at the historical disaster site of debris flow in accordance with the embodiment of the present invention;

FIG. 3 shows a debris flow disaster early warning model construction method according to an embodiment of the present invention;

FIG. 4 is a comparison of simulated water depth versus actual water depth for an embodiment of the present invention;

FIG. 5 is an illustration of an embodiment of the present invention showing an early warning of a debris flow disaster at a certain place and a certain day;

FIG. 6 is a flow chart of a method of an embodiment of the present invention.

Detailed Description

Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the described embodiments are only some embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention. It should be understood by those skilled in the art that the following specific examples or embodiments are a series of optimized arrangements of the present invention for further explaining specific contents of the invention, and the arrangements may be combined with or used in association with each other, and the following specific examples or embodiments are only used as the optimized arrangements and should not be construed as limiting the scope of the present invention.

In a specific embodiment, the derivation and operation of the present invention are described with reference to specific examples.

First, taking actual geological data of a certain city as an example, the incidence relation between the river channel distance, the ditch distance and the disaster point is analyzed.

1. Analysis of distance between disaster point and river channel and distance between ditch and mouth

Taking a city as an example, a river is provided with 1000m, 2000m, 3000m and 4000m multistage buffers, the number of disaster points in different buffers is counted, and the counting result is shown in fig. 1. 104 historical debris flow disaster points within 1000m away from the river channel account for 81.25%, 17 historical debris flow disaster points within 1000-2000m away from the river channel account for 13.28%, and the debris flow disaster points gradually decrease with the increase of the distance. Most of debris flow disasters are shown to occur at or around the river channel mouth.

Debris flow rushes out from the gully opening, because the topography is suddenly opened, the relative cross sectional area increase of channel, based on the relatively invariable prerequisite of instantaneous flow, the direct expression of debris flow motion is speed reduction to piling up takes place, until reaching the biggest pile length and width, generally think, when debris flow reaches the edge of piling up the fan, its velocity of motion is close to zero. When the debris flow is piled up, the movement direction of the debris flow can be divided into a longitudinal direction and a transverse direction, and the flow speed can also be divided into a longitudinal speed (a vertical direction) and a transverse speed (a horizontal direction). The longitudinal speed is maximum at the groove opening, the foremost end of the accumulation fan is minimum, and the longitudinal speed corresponds to the maximum accumulation length of the debris flow; the gully distance factor may exhibit to some extent the momentum and destructive power of the debris flow. And taking the outlet of the drainage basin as a groove, and calculating the linear distance between the grid pixel and the groove as the groove distance. In order to explore the relationship between debris flow disasters and debris flow gully distance, 500m is taken as a buffer distance, and the gully distance is subjected to inverse distance normalization calculation:

wherein R is the buffer distance threshold and d is the trench opening distance.

In a preferred embodiment, the threshold R may be set to, for example, 500, then at this point the L value calculation becomes:

and superposing the historical 128 debris flow disaster points to the image layer for statistics. The frequency of occurrence of debris flow disasters is higher as the distance from the gully is closer, wherein 18 disaster points account for 14.29 percent in the range of 0.8-0.6, and 79 disaster points account for 63.7 percent in the range of 0.8-1. The frequency of occurrence of debris flow disasters gradually increases as the quantified value of the trench opening distance increases, as shown in fig. 2. The debris flow disaster is mostly shown to occur at and near the gully opening.

Through analyzing the debris flow disaster point river channel distance and the gully distance, the debris flow disaster mostly occurs at the position of the gully of the drainage area and near the river channel part, and the runoff confluence simulation is based on the simulation on the gully and the river channel. Thus. Debris flow disaster early warning based on the runoff confluence model can improve space pertinence of disaster early warning.

2. Construction of debris flow disaster model

Debris flow disasters are the result of combined actions of factors such as geology, topography, landform, soil, vegetation, rainfall, glacier melt water and the like. The same scale of precipitation or snow melting can cause debris flow disasters of different degrees due to different conditions such as terrain, geological soil and the like. Because factors such as geology, topography and landform keep relatively stable within a certain time, the factors are used as background factors of the region, precipitation and snow melting are used as exciting factors of debris flow disasters, and a debris flow disaster early warning model is constructed.

Firstly, small watershed division is carried out according to the DEM. And incorporating the background factors into different watersheds, analyzing the influence of the background factors of the watersheds on the debris flow disasters, and constructing a debris flow disaster risk probability model based on the watershed units. The risk probability result is an objective display of the activity degree of the debris flow, and the higher the risk degree is, the higher the possibility of the debris flow disaster caused by the same precipitation and snow melting is. Then, a rainfall snow melting-runoff confluence model is introduced to simulate the runoff water depth. And establishing a debris flow disaster early warning model based on the runoff simulation water depth, the risk probability background value factor and the gully opening distance factor by taking the simulation water depth as the debris flow disaster early warning factor. The debris flow disaster early warning model construction idea is shown in fig. 3.

3. Debris flow disaster risk probability calculation

3.1 basin partitioning

At present, the dangerous area of the debris flow disaster is mostly established on the basis of a single grid unit. The single grid unit is difficult to quantify the integral characteristics of the watershed, such as the watershed area, the longitudinal gradient and the like, and the factors are important factors for constructing a debris flow model. Therefore, the method utilizes the regional DEM, divides the DEM into a large number of small watersheds based on the hydrological D8 method, and takes the small watersheds as the early warning basic unit of the debris flow disaster.

The hydrological D8 method is the basic method of grid hydrological analysis. The grid water flow can only flow from the center pixel to one of the neighboring pixels. Since each grid pixel has 8 neighboring pixels, it is called the hydrologic D8 method. The hydrological D8 method is actually a single-flow hydrological analysis method, and the hydrological analysis module of ArcGIS adopts the hydrological D8 method.

3.2 selection and logistic regression analysis of inducing factors of debris flow disasters

The factors inducing the debris flow disaster are more, and the forming mechanism is more complex. According to the debris flow occurrence mechanism, in a preferred embodiment, according to the scheme, bare rock rate, basin area, longitudinal gradient, river, road, fault density, land utilization, soil type, hidden danger point density, valley density and annual precipitation factor are selected as debris flow disaster inducing factors, and the debris flow disaster risk probability is determined based on an information quantity combined logistic regression method. Of course, for the debris flow disaster inducing factor, other factors may also be selected, or any combination manner of the above factors according to the present scheme may be used, and the setting may be specifically performed according to the disaster area difference, the disaster determination focus difference, and the like, which is not described here any more.

Information amount of each single factor I (K, x)_n) The calculation method of (2) is as follows:

in the formula: s is the total number of evaluation units in a certain city area; n is the total number of units distributed when debris flow disasters occur in certain urban areas; s_iFor a certain city containing evaluation factor x_nThe number of units of (a); n is a radical of_iTo be distributed in an evaluation factor x_nThe number of debris flow disaster units in a specific category; and K represents whether the disaster point is a debris flow disaster point or not.

The single-factor information quantity I is obtained by calculating the past historical data. The calculated single factor information quantity is used as historical data of factors influencing the occurrence of the address disaster in the logistic regression model, and the logistic regression coefficient B corresponding to each influence factor in the logistic regression model is calculated by combining the single factor information quantity I obtained by calculation of the historical data in a logistic regression mode₁…B_n。

The logistic regression model modeling method comprises the following steps:

in the above formula, P is the debris flow disaster risk probability, and the value is [0, 1](1-P) is the probability of no occurrence of debris flow disaster, X₁…X_nIs a factor affecting the occurrence of geological disasters, B₁…B_nIs a logistic regression coefficient corresponding to each influence factor, and A is a constant term (constant) of the regression equation.

Taking a city as an example, 701 potential hazard point data records from which error data is removed are used as modeling data, and 131 effective disaster point data records are used as inspection data. Extracting information quantity values of geological hidden danger points and disaster-causing factors of points without geological disasters by using an ArcGIS software grid data extraction tool, establishing a logistic regression model in SPSS software by taking the debris flow disasters as dependent variables and taking bare rock rate, basin area, longitudinal gradient, river, road, fault density, land utilization, soil type, hidden danger point density, valley density and annual precipitation quantity information quantity values as independent variables, and calculating by logistic regression to obtain a table 1.

TABLE 1 results of logistic regression analysis

Each factor passes Wald test with a significant level of 0.05, and the correlation coefficients among the factors are small and independent of each other; randomly generating a debris flow disaster point for many times and modeling circularly, wherein the result of the model is stable; finally, determining the risk probability of the debris flow disaster as follows:

in the above formula, H is the probability value of the danger of debris flow disaster, X₁～X₁₁The information values of bare rock rate, basin area, longitudinal gradient, river, road, fault density, land utilization, soil type, hidden danger point density, valley density and annual precipitation are respectively obtained.

The debris flow disaster probability is divided into five levels, the precision verification is carried out on the debris flow disaster probability by using 131 historical debris flow disaster points in a certain city, and the occupation ratio of the debris flow disaster risk probability of each disaster point is shown in table 2.

TABLE 2 debris flow disaster probability level ratio

The number of historical disaster points in the fifth stage is 110, which accounts for 83.79% of the total historical disaster points, the number of disaster points in the fourth stage is 2, which accounts for 0.02%, and the number of disaster points in the second stage and the first stage is 19, which accounts for 11.48%. As can be seen from Table 2, the matching degree of the verification data and the probability result of the risk of the debris flow disaster in a certain city is high. The rationality of the method for calculating the risk probability of the debris flow disasters lays a foundation for building a debris flow disaster early warning model.

4. Debris flow disaster early warning model

Rainfall is the condition of bringing forth that the debris flow calamity takes place, consequently, in the scheme of this application, as an preferred mode, early warning is carried out as debris flow early warning main factor with runoff confluence, three factors of debris flow calamity danger probability and gully apart from to debris flow calamity.

4.1 debris flow disaster early warning method

Debris flow disasters are mainly near the valleys of rivers. Therefore, the water depth simulated by the runoff confluence model replaces the traditional surface rainfall to carry out debris flow disaster early warning so as to improve the spatial pertinence of the early warning. The invention provides a method for establishing a debris flow disaster early warning model by establishing a relation between confluent water depth and a debris flow disaster occurrence background. And setting the runoff confluence water depth grade as G, the debris flow disaster risk probability background value as H and the gully opening distance factor L. Calculating the calculation weight of each factor to the debris flow disaster by using a binary logistic regression method, wherein the calculation weight is as follows:

Y＝(aG+bH)×L

in the formula: y is a debris flow disaster early warning result, G is the grade of the runoff merging water depth, H is a debris flow disaster danger probability background value, and a and b are the weight of the runoff merging water depth and the debris flow disaster danger probability background value respectively.

4.2 runoff confluence model

4.2.1 radial flow confluence simulation

In a preferred embodiment of the invention, the Navier-Stokes equation is subjected to grid mode decomposition, and the water depth and the water velocity of each grid on a time slice are calculated according to an advection term, a pressure term and an external force term. And simulating the runoff confluence process of the basin through iteration of the time slices. Runoff generated by glacier ablation is also an important factor for debris flow disaster, and a water balance formula in the model is set as follows:

in the formula (I), the compound is shown in the specification,respectively setting the initial water depth and the output water depth of the central grid c on the time slice; respectively the precipitation, glacier ablation, infiltration and evaporation on the central grid time slice;andis the advection term in the model.

The grid precipitation is obtained by the interpolation of the hourly precipitation of the meteorological station, and the glacier ablationThe daily ablation amount is converted into an hourly ablation amount.Andthe calculation method is as follows:

in the formula (I), the compound is shown in the specification,is the velocity component of the water stream as it flows adjacent grid b to the central grid c,Δ L is the distance between the centers of the two grids, which is the velocity component of the water flow as it flows from the center grid c to the adjacent grid b.

Based onAndlet the grid water flow velocity vector of the surrounding neighborhood beAmount of water S to center grid when neighborhood grid fluid is flowing_b→cComprises the following steps:

where (Δ x, Δ y) is the coordinate deviation produced between unit pixel grids; v. of_maxIs the maximum velocity generated by the water flow in equal time slices of the grids。

Calculating the residual water flow S of the runoff center grid_cComprises the following steps:

the calculated center grid water depth is:

in the formulaGrid neighborhood at t for center_nThe depth of the water over the time slice,is t_nThe proportion of water that on the time slice the neighborhood grid b enters the central grid c as the fluid moves,is t_nThe remaining water content ratio of the center grid on the time slice.

The water body meets the law of conservation of momentum in the process of simulating the interactive motion. Is provided withAndindicating the initial and terminal water velocities for the center grid. The termination water velocity is:

when the water body flows on the earth surface, the infiltration and evaporation capacity of the flowing water body are calculated due to the influence of soil and climate environment, and the evaporation rate at the same position in the model is considered to be unchanged with the simulation time period.

In the formula, e is a calibration parameter.

When the water body moves on the surface, the infiltration degree is influenced by various factors, and the simulation calculation is carried out by utilizing a sharp infiltration curve:

f_t＝f(t)＝0.5×S·t^-0.5+A

wherein S and A are parameters related to soil properties.

The surface water is influenced by gravity and pressure to generate corresponding acceleration in the flowing process by delta H_bTo represent the height difference of water body flow between units, to calculate the velocity vector generated by adjacent grids to the central grid

J and i are the difference of the row sequence number and the line sequence number of the neighborhood grid and the central grid in the water flow simulation process; α is a constant of a positive rate relating to acceleration, water density, friction force, and the like.

The external force term and the pressure term are simulated by using a hydrodynamic method. When the actual water depth is lower than a given threshold value, an attenuation coefficient is set to reduce the simulated water velocity, and the attenuation coefficient epsilon is approximately defined as:

in the formula d_minAnd d_maxThe lower and upper boundary depths of the friction force applied to the water flow are m, and the sigma is the friction of the fluidA scaling factor.

Each grid unit on the time slice only considers the water exchange with the 8-neighborhood grid water quantity, and the maximum distance of the water flow in the time slice does not exceed the grid resolution. Setting a maximum water velocity limit v for a basin_maxThe simulated water velocity is greater than v_maxForce setting to v_max. Under the condition, the time difference Δ t between two adjacent time slices is:

Δt＝C/v_max

wherein C is the grid resolution.

According to the above formula, the number of time slices N to be iterated per hour is:

N＝3600/Δt

4.2.2 model of snow melting in glacier

Glacier ablation type debris flow is also one of the main types of debris flow disasters, and moisture brought by glacier ablation also provides a certain foundation for the debris flow disasters formed by surrounding valley areas. The glacier ablation part is added into the runoff model simulation to serve as the initial water source part of the runoff simulation. In the prior art, most of the ice and snow ablation is simulated by adopting an energy balance method, the calculation of the energy balance is complex and needs a large amount of data, and in places with scarce data, an energy balance algorithm is difficult to popularize locally, so that the accurate calculation of the influence of ice and snow ablation factors on debris flow is seriously influenced. In a preferred embodiment of the invention, the following way is adopted as the snow-melting model of glaciers and added into the runoff confluence model:

the basic calculation formula of the ice and snow ablation model is as follows:

wherein M is daily ablation (mm), T is daily average air temperature (DEG C), and T is₀Is the glacier ablation temperature threshold (DEG C), and MF is a degree-day factor (mm DEG C-1. d-1).

And (4) interpolating the air temperature data and then substituting the interpolated air temperature data into the formula to calculate the daily ablation amount of the grid pixel where the glacier is located. The precipitation amount of the grid unit and the glacier ablation amount are used as water amount input of the model.

4.2.3 Water depth grading of radial flow confluence

Taking a certain city as an example, selecting simulation 18 days from 8 months and 19 days 00 in 2015, substituting meteorological stations and air temperature data into a runoff confluence model, simulating a runoff confluence process of a certain area, and comparing actual observed water depth at the downstream of a certain river section with simulated water depth. Fig. 4 is a comparison of simulated water depth versus actual water depth. As can be seen from fig. 4, the simulated water depth curve has a movement trend consistent with the actual water depth curve. And performing pearson correlation analysis on the actual water depth data and the simulated water depth data, wherein the correlation is 0.838, and is strong positive correlation, which shows that the energy runoff confluence model adopted by the invention simulates the change trend of the water depth.

Although the water depth change trend of the model simulation is close, the change value of the simulated water depth is not in the same order of magnitude as the actual water depth change value. The runoff confluence simulation is carried out under the condition of a dry riverway, and the initial water level of the riverway is not considered; meanwhile, the water depth output by the model is based on the grid pixel unit, which is equivalent to horizontally paving the pixel on the rectangular grid unit. Therefore, the absolute value of the simulated water depth cannot be directly substituted into the model to carry out debris flow early warning. In a more preferred embodiment, the debris flow disaster early warning model is constructed by a water depth grading method, and the table 3 is a runoff water depth grading table.

TABLE 3 runoff water depth grading

4.3 debris flow disaster early warning model construction based on logistic regression

A binary logistic regression model is established based on the debris flow disaster points, the runoff confluence simulation water depth classification and the debris flow disaster probability value of the region over the years. Taking a city as an example, selecting the mud-rock flow disaster point data of the past years from 2000 to 2015 to participate in logistic regression operation, taking whether the mud-rock flow disaster occurs as a dependent variable, and taking the runoff confluence simulation water depth and the background value of the risk probability of the mud-rock flow disaster as independent variables. A binary logistic regression model is established in SPSS software to obtain the final weight coefficient as follows:

TABLE 4 logistic regression equation Table

Wherein B is a logistic regression coefficient; s.e. standard error; wald is the chi-square value; df is the degree of freedom; significance is sig.

After the logistic regression is carried out on the debris flow disaster indexes, the significance of all factors is less than 0.05, the logistic regression model can be basically judged to have significance, and the logistic regression model passes the test. And quantizing the value B, using the quantized value B as the weight of an early warning factor, and constructing an expression of a debris flow disaster early warning model in a certain city by logistic regression according to the following formula:

Y＝(0.52G+0.48H)×L

4.4 debris flow disaster early warning precision evaluation

Taking a certain city as an example, the debris flow disaster early warning precision evaluation method is carried out. Putting the 2010-2019 debris flow disaster points and the same number of random points into a model, and obtaining the range of Y values through calculation, wherein the T value distribution of the random points without disasters is between 0 and 0.45, and the rare number of the random points is more than 0.45; while disaster points are mostly distributed between 0.55 and 1. And analyzing and determining that the debris flow disaster early warning Y value exceeds 0.55 to issue a yellow early warning signal, exceeds 0.65 to issue an orange early warning signal and exceeds 0.75 to issue a red early warning signal. The risk level of the debris flow disaster is divided into the following five levels:

TABLE 5 debris flow disaster early warning grading

The historical disaster points in part of the samples and the data of the annual mud-rock flow disaster points in 2011-2020 are selected as result verification data, and verification results are obtained as shown in the following table 6.

Table 6 debris flow disaster warning verification result

According to the forecast result, 41 disaster points in the case historical disaster point data are selected, and 27 disaster points with forecast grades above yellow account for 65.85%. The forecast grade is 26 disaster points above orange, and the forecast precision is 63.41%. The forecast grade is 26 disaster points above red, and the forecast precision is 63.41%. Most of reasons that disaster points are not predicted before 2016 are that few meteorological stations exist, rainfall data are lacked, and the runoff confluence simulation result of a part of areas is poor, namely the early warning effect is reduced. Since 2016, the new meteorological site that increases gradually, precipitation data are more comprehensive, and early warning model precision has also obtained improving to a certain extent.

Fig. 5 is a predicted debris flow disaster early warning diagram in 7 months and 7 days in 2019 of the city after the disaster early warning method provided by the invention is actually applied. Compared with the data of the actual day, 4 prewarning devices with different color levels (including 2 prewarning devices with 2 different prewarning levels) are arranged at the place where the debris flow disaster occurs 2 times on the day. The debris flow early warning model has high precision and can be applied to actual debris flow disaster early warning and forecasting work.

In addition, in another specific embodiment, the present invention can also be implemented by a debris flow disaster warning system based on runoff confluence simulation, the system including:

the debris flow disaster risk calculation module is used for determining a probability background value H of the debris flow disaster risk in the early warning area;

the water depth calculation module is used for calculating the runoff confluence water depth W based on the rasterized Navier-Stokes equation;

the gully distance factor calculation module is used for acquiring a gully distance factor L related to the debris flow disaster risk based on the debris flow buffer distance;

the disaster early warning result calculation module is used for calculating a debris flow disaster early warning result Y based on the debris flow disaster risk probability background value H, the runoff confluent water depth grading G and the gully distance factor L;

the early warning module is used for carrying out early warning on the debris flow disaster based on the debris flow disaster early warning result Y;

and the data input module is used for acquiring the debris flow disaster point data in the early warning area.

Preferably, the calculation mode of the debris flow disaster early warning result Y is as follows:

Y＝(aG+bH)×L

in the formula: y is a debris flow disaster early warning result, G is runoff confluent water depth grading, H is a debris flow disaster risk probability background value, and a and b are weights of the runoff confluent water depth and the debris flow disaster risk probability background value respectively.

Preferably, the acquisition mode of the background value H of the debris flow disaster risk probability is as follows:

establishing a logistic regression relation between the debris flow disaster risk probability P and each individual induction factor:

wherein P is debris flow disaster risk probability, X₁…X_nIs a separate inducing factor affecting the occurrence of geological disasters, B₁…B_nIs a logistic regression coefficient corresponding to each individual induction factor, A is a regression equation constant term;

and calculating a logistic regression coefficient corresponding to each individual induction factor based on historical data, wherein the debris flow disaster risk probability P at the moment is used as a debris flow disaster risk probability background value H.

More preferably, the system can also realize the debris flow disaster early warning method based on the runoff confluence simulation.

Moreover, it should be understood by those skilled in the art that the above system may also be implemented by a computer-readable medium or an apparatus including a storage device and a processor device, and the system may be enabled to implement the debris flow disaster warning method based on runoff convergence simulation described in the description of the present invention when the system is in operation, and the implementation manner and the conventional module adjustment and modification based on the implementation manner should be considered to fall within the protection scope of the present invention.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.

The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.