High-speed remote landslide sliding distance prediction method based on exit diffusion effect

Abstract

The invention discloses a high-speed remote landslide sliding distance prediction method based on an outlet diffusion effect, and the method comprises the following steps: obtaining the rushing-out speed of chippings flowing out of a groove through a high-speed camera according to a pre-built multi-stage chute, obtaining the final thickness and sliding distance of an accumulation body through a digital elevation camera, and calculating the sliding distance of the accumulation body; obtaining a first function relational expression between the rushing-out speed and the channel width of the multi-stage chute and a second function relational expression between the final accumulation body thickness and the width of the multi-stage chute; according to the width of the channel, the rushing-out speed of the chippings flowing out of the groove, the final accumulation body thickness and the sliding distance, the transverse dissipation item of the chippings is determined; establishing a constant flow model; and according to the satellite image, determining the flow channel width of the potential landslide on the measurement site, inputting the flow channel width into the constant flow model, and outputting the predicted sliding distance of the potential landslide on the measurement site through the constant flow model. The accuracy of landslide sliding distance prediction can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of landslide early warning, in particular to a prediction method, and in particular to a high-speed long-distance landslide sliding distance prediction method based on an outlet diffusion effect. Background Art

[0002] During the movement process after the slope loses stability, the rock mass gradually becomes fragmented and may develop into a high-speed long-distance landslide. This type of landslide usually has four characteristics: ① The movement speed must be greater than 20m / s, most of which are 40-80m / s, and some even exceed 200m / s; ② The volume is usually greater than 100,000m 3 ③ Extremely long movement distances: The tangent value Φ of the line connecting the source area and the front edge of the accumulation body is less than 0.33. Compared with ordinary landslides, high-speed and long-distance landslides are more destructive and have a higher disaster rate. They are also more likely to develop into chain disasters, causing serious casualties and economic losses.

[0003] With the large-scale development of hydropower facilities in high-altitude mountainous areas in my country, there is an urgent need to assess potential landslides in selected sites to reduce operational risks after commissioning. However, previous landslide distance prediction methods are typically based on empirical formulas derived from a large number of existing landslides, using vertical drop H, horizontal distance L, and volume V. These formulas lack practical physical significance and, due to variations in sliding topography, lead to poor prediction accuracy.

[0004] Some scholars have divided the flow patterns of landslides into three types based on their lateral confinement characteristics during sliding: unconfined flow, fully confined flow, and semi-confined flow. Semi-confined landslides are widely developed in high-altitude mountainous areas, such as the Yigong landslide and the Sedongpugou collapse-debris flow. This type of landslide is affected by internal pressure and exhibits a diffusion effect at the outlet, resulting in stronger fluidity. This invention studies the influence of the outlet diffusion effect on the sliding distance of the landslide by designing a new multi-stage chute device with variable width, and proposes a new sliding distance prediction model based on this. This model has important guiding significance for the safe construction and operation of future water conservancy and hydropower projects. Summary of the Invention

[0005] In view of the above-mentioned deficiencies in the prior art, the present invention provides a high-speed long-distance landslide sliding distance prediction method based on the outlet diffusion effect, which solves the problem of inaccurate landslide sliding distance prediction in the prior art.

[0006] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is: a high-speed long-distance landslide sliding distance prediction method based on the outlet diffusion effect, comprising the following steps:

[0007] S1. Using a pre-built multi-stage chute, obtain the velocity of debris flowing out of the chute using a high-speed camera, and obtain the final accumulation thickness and sliding distance using a digital elevation camera. Determine a first functional relationship between the velocity and the channel width of the multi-stage chute, and a second functional relationship between the final accumulation thickness and the width of the multi-stage chute.

[0008] S2. Determine the lateral dissipation term of the debris flow based on the channel width, the velocity of the debris when it flows out of the groove, the thickness of the final accumulation body, and the sliding distance;

[0009] S3. Establishing a constant flow model based on the first functional relationship, the second functional relationship, and a lateral dissipation term of the debris flow;

[0010] S4. Determine the width of the flow channel of the potential landslide at the measurement site based on the satellite image, input the flow channel width into a constant flow model, and output a predicted sliding distance of the potential landslide at the measurement site through the constant flow model.

[0011] The beneficial effects of the above scheme are:

[0012] (1) The present invention designs a novel variable-width chute device and, based on indoor debris flow tests, obtains the velocity of debris flow when it rushes out of the chute, the thickness of the final accumulation body, and the sliding distance under different flow widths. By introducing the dissipation term, an innovative landslide sliding distance prediction model that considers the outlet diffusion effect is proposed, solving the long-standing problem of poor sliding distance calculation accuracy for semi-side flow-restricted landslides. At the same time, the prediction model is combined with regional satellite identification technology for landslides to effectively realize rapid risk assessment of water conservancy and hydropower projects.

[0013] (2) The present invention can calculate the sliding distance of a landslide by establishing a mathematical model and using satellite images, thus eliminating the need for extensive on-site surveys and significantly saving costs. This is undoubtedly of great significance for the study of complex landslide activities in cold and high-altitude mountainous areas.

[0014] Furthermore, in step S1, the multi-stage chute is constructed based on the slope data of the landslide engineering area, and the field data includes the slope gradient and lithology of the slope;

[0015] The multi-stage chute is a three-stage chute with variable width, and the contact surface of the multi-stage chute is a 10 cm acrylic plate, the surface of the acrylic plate is covered with black matte material;

[0016] The first section of the multi-level chute is a stacking platform with a length and width of 3 meters and 2 meters respectively;

[0017] The second section of the multi-stage chute is the first chute, the length and width of the first chute are 2 meters and 0.5 meters respectively;

[0018] The third section of the multi-stage chute is the second chute, and the length and width of the second chute are 2 meters and 0.5 meters respectively;

[0019] The second chute is connected to the material area, which is located behind the second chute and has a length of 0.5 meters;

[0020] The first chute and the second chute are connected by bolts, and the inclination adjustment range of the first chute and the second chute is 0-65 degrees respectively;

[0021] Four MAL-mini cylinders are installed on the first chute and the second chute respectively. The MAL-mini cylinders are connected to the side plates and are used to adjust the width of the first chute and the second chute between 150 mm and 500 mm respectively.

[0022] Furthermore, in step S1, the first functional relationship is:

[0023]

[0024] Among them, v t (w) represents the functional relationship between the exit velocity and the channel width, v o represents the reference velocity of debris flow when it rushes out of the groove, A, B, and C represent constants determined by data fitting, π represents pi, e represents a natural constant, and w represents the channel width;

[0025] The second functional relationship is:

[0026]

[0027] Among them, h t (w) represents the functional relationship between the accumulation thickness and the channel width, h0 represents the accumulation thickness after the debris flow is flushed out of the outflow groove at a reference rate, A represents a constant determined by data fitting, e represents a natural constant, w represents the channel width, and t is the time effect coefficient determined by data fitting.

[0028] Furthermore, step S2 specifically includes:

[0029] S21, determining a channel width matrix according to the channel width;

[0030] The channel width matrix is:

[0031] ydata=ydata(l)

[0032] Among them, ydata represents the channel width matrix, l represents the channel width;

[0033] S22, determining an input variable matrix based on the velocity of debris flowing out of the groove, the thickness of the final accumulation body, and the sliding distance;

[0034] The input variable matrix is:

[0035]

[0036] Among them, F(x,xdata) represents the input variable matrix, F(x,xdata(v t ))、F(x,xdata(h t )), F(x,xdata(w)) are the outflow velocity matrix, outflow velocity matrix, and sliding distance matrix respectively determined according to the outflow velocity, final accumulation thickness, and sliding distance when the debris flows out of the groove;

[0037] S23, defining a model function, initializing initial parameters of the model function, and obtaining a prediction channel width matrix based on the input variable matrix;

[0038] S24. Using the Lsqcurvefit function, the initial parameters of the model function are updated to obtain the final parameters of the model function.

[0039] The Lsqcurvefit function is:

[0040]

[0041] Among them, F(x,xdata) represents the input variable matrix, ydata represents the channel width matrix, F(x,xdata i ) represents the predicted input variable matrix, ydata i Represents the predicted channel width matrix;

[0042] S25. Determine the lateral dissipation term of the debris flow by using a formula for determining the lateral dissipation term of the debris flow according to the final parameter of the model function;

[0043] The formula for determining the lateral dissipation term of debris flow is:

[0044]

[0045] Where ξ represents the lateral dissipation term of the debris flow, a, b, and c represent the final parameters of the model function, e represents a natural constant, and w represents the channel width.

[0046] The beneficial effect of the above further solution is that by introducing the lateral dissipation term of the debris flow, a more accurate constant flow model can be obtained, which can further improve the accuracy of the predicted sliding distance.

[0047] Furthermore, in step S3, the constant flow model is:

[0048]

[0049] Where l represents the channel width, μ t represents the friction coefficient of the flow channel, θ represents the slope angle of the accumulation area, and θ t represents the slope angle of the flow channel, g represents the acceleration of gravity, h t (w) represents the functional relationship between the thickness of the stack and the channel width, v t (w) represents the functional relationship between the outflow velocity and the channel width, and ξ represents the lateral dissipation term of the debris flow.

[0050] Furthermore, the method further comprises:

[0051] Based on the predicted sliding distance, the risk level of potential landslide at the measurement site is determined.

[0052] The beneficial effect of this further approach is that by predicting the slip distance, high-risk areas can be identified, allowing preventive measures such as construction restrictions and enhanced protection to reduce the likelihood of landslides. Clarifying risk levels helps identify key monitoring areas, allowing for timely implementation of safety measures to protect residents and projects, and minimizing casualties and property losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a flow chart of a method for predicting the sliding distance of high-speed and long-distance landslides based on the outlet diffusion effect;

[0054] Figure 2 This is a schematic diagram of the construction of a multi-level chute;

[0055] Figure 3 Schematic diagram of the composition of a multi-stage chute;

[0056] Figure 4 This is a schematic diagram obtained after the data collected by the high-speed camera and the digital elevation camera are converted;

[0057] Figure 5 Schematic diagram comparing the landslide sliding distance calculated by the model and the monitoring results of the actual test. DETAILED DESCRIPTION

[0058] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0059] like Figure 1 As shown in FIG, a method for predicting the sliding distance of a high-speed long-distance landslide based on the outlet diffusion effect includes the following steps:

[0060] S1. Based on the pre-built multi-level chute, the discharge velocity of the debris when it flows out of the chute is obtained by a high-speed camera, and the final accumulation thickness and sliding distance are obtained by a digital elevation camera. The first functional relationship between the discharge velocity and the channel width of the multi-level chute and the second functional relationship between the final accumulation thickness and the width of the multi-level chute are obtained.

[0061] In this embodiment, in step S1, the multi-stage chute is constructed based on the slope data of the landslide engineering area, and the field data includes the slope gradient and lithology of the slope;

[0062] The multi-stage chute is a three-stage chute with variable width, and the contact surface of the multi-stage chute is a 10 cm acrylic plate, the surface of the acrylic plate is covered with black matte material;

[0063] The first section of the multi-level chute is a stacking platform with a length and width of 3 meters and 2 meters respectively;

[0064] The second section of the multi-stage chute is the first chute, the length and width of the first chute are 2 meters and 0.5 meters respectively;

[0065] The third section of the multi-stage chute is the second chute, and the length and width of the second chute are 2 meters and 0.5 meters respectively;

[0066] The second chute is connected to the material area, which is located behind the second chute and has a length of 0.5 meters;

[0067] The first chute and the second chute are connected by bolts, and the inclination adjustment range of the first chute and the second chute is 0-65 degrees respectively;

[0068] Four MAL-mini cylinders are installed on the first chute and the second chute respectively. The MAL-mini cylinders are connected to the side plates and are used to adjust the width of the first chute and the second chute between 150 mm and 500 mm respectively.

[0069] In this embodiment, in step S1, the first functional relationship is:

[0070]

[0071] Among them, v t (w) represents the functional relationship between the exit velocity and the channel width, v o represents the reference velocity of debris flow when it rushes out of the groove, A, B, and C represent constants determined by data fitting, π represents pi, e represents a natural constant, and w represents the channel width;

[0072] The second functional relationship is:

[0073]

[0074] Among them, ht (w) represents the functional relationship between the accumulation thickness and the channel width, h0 represents the accumulation thickness after the debris flow is flushed out of the outflow groove at a reference rate, A represents a constant determined by data fitting, e represents a natural constant, w represents the channel width, and t is the time effect coefficient determined by data fitting.

[0075] In a specific embodiment, the specific parameters of the high-speed camera can be a frame rate of 1000 frames and a resolution of 1920×1080 pixels. Figure 2 As shown, Figure 2 This is a schematic diagram of the construction of a multi-level chute. When designing and constructing a multi-level chute with variable width, you can refer to Figure 2 The contact surface is made of a 10cm thick acrylic sheet (polymethyl methacrylate), covered with a black matte material.

[0076] like Figure 3 As shown, Figure 3 The figure is a schematic diagram of the composition of a multi-stage chute. Figure 3 The chute is composed of three parts from bottom to top: the stacking platform is 3m long and 2m wide; the first section of the chute (i.e., the first chute) is 2m long and 0.5m wide; the second section of the chute (i.e., the second chute) is 2m long and 0.5m wide, and the material area is located at the rear edge of the second section of the chute and is 0.5m long; the first and second sections of the chute are connected by bolts and can be independently adjusted in slope within the range of 0-65° (approximately 0-3.172m) (see attached Figure 2 To change the channel width, four MAL-mini cylinders were installed on each of the two chute sections and connected to the side panels. The cylinders were retracted to adjust the channel width within a range of 150–500 mm. To capture the velocity of the debris flow at the exit, a high-speed camera with a frame rate of 1000 frames and a resolution of 1920 × 1080 pixels was installed above the accumulation platform. A digital elevation camera was used to measure the thickness of the landslide deposit.

[0077] For example, the slope data of the landslide engineering area can be obtained by conducting on-site investigations of possible landslides in the engineering area. When conducting on-site investigations of possible landslides in the engineering area, the main engineering geological conditions such as slope and lithology can be obtained. For example, the Sedongpugou landslide-debris flow occurred on the left bank of the Yarlung Zangbo River near Gala Village, Pai Town, Milin County, with the gully mouth coordinates E94°56'13" and N29°45'03". The Sedongpugou drainage basin covers an area of ​​approximately 67.15km 2 , of which about 61.6% of the flow area has a slope of about 30°. The surface of the rock mass is mostly covered by glacial deposits and alluvial deposits, and affected by glacial action and strong physical weathering, the surface rock and soil are relatively loose and broken.

[0078] In a specific embodiment, in the pre-built multi-stage chute, the debris flow accumulation area is the chute platform, the flow area is the first section of the chute, the accumulation area slope angle θ can be 0°, and the flow channel slope angle θ t Can be 30°; μ t The friction coefficient of the flow channel can be set to 0.35. Based on the pre-built multi-level chute, the outflow velocity of the debris when it flows out of the chute is obtained by a high-speed camera, and the final accumulation thickness and sliding distance are obtained by a digital elevation camera.

[0079] For example, different initial conditions can be set to explore the diffusion effect and accumulation characteristics of debris flow under semi-confined conditions. The particle size, debris flow mass, and flow channel width can also be set. The release height is the height difference between the rear edge of the material box and the diffusion platform, which is 2.53m. A total of 75 sets of experiments were completed, such as Figure 4 As shown, Figure 4 This is a schematic diagram obtained after the data collected by the high-speed camera and the digital elevation camera are converted. Figure 4 middle, Figure 4 a and Figure 3 b is the debris flow outflow velocity v measured by high-speed camera and PIV technology t Schematic diagram, Figure 4 c and Figure 3 Where d is the sliding distance l and the final accumulation thickness h measured by the digital elevation camera t Schematic diagram.

[0080] Therefore, the first functional relationship between the ejection speed and the channel width of the multi-stage chute can be obtained as follows:

[0081]

[0082] Among them, v t (w) represents the functional relationship between the ejection velocity and the channel width, π represents pi, e represents a natural constant, and w represents the channel width;

[0083] The second functional relationship between the final stack thickness and the width of the multi-stage chute can be obtained as follows:

[0084]

[0085] Among them, h t (w) represents the functional relationship between the stack thickness and the channel width, e represents a natural constant, and w represents the channel width.

[0086] In the first and second functional relationships, w represents the channel width, which can be given by adjusting the width of the chute. And in subsequent tests, v t The square of the correlation coefficient R of (w)2 =0.998,h t The square of the correlation coefficient R of (w) 2 =0.998. This indicates that the correlation between the first and second functional relationships is high.

[0087] S2. Determine the lateral dissipation term of the debris flow based on the channel width, the velocity of the debris when it flows out of the groove, the thickness of the final accumulation body, and the sliding distance.

[0088] In this embodiment, step S2 specifically includes:

[0089] S21, determining a channel width matrix according to the channel width;

[0090] The channel width matrix is:

[0091] ydata=ydata(l)

[0092] Among them, ydata represents the channel width matrix, l represents the channel width;

[0093] S22, determining an input variable matrix based on the velocity of debris flowing out of the groove, the thickness of the final accumulation body, and the sliding distance;

[0094] The input variable matrix is:

[0095]

[0096] Among them, F(x,xdata) represents the input variable matrix, F(x,xdata(v t ))、F(x,xdata(h t )), F(x,xdata(w)) are the outflow velocity matrix, outflow velocity matrix, and sliding distance matrix respectively determined according to the outflow velocity, final accumulation thickness, and sliding distance when the debris flows out of the groove;

[0097] S23, defining a model function, initializing initial parameters of the model function, and obtaining a prediction channel width matrix based on the input variable matrix;

[0098] S24. Using the Lsqcurvefit function, the initial parameters of the model function are updated to obtain the final parameters of the model function.

[0099] The Lsqcurvefit function is:

[0100]

[0101] Among them, F(x,xdata) represents the input variable matrix, ydata represents the channel width matrix, F(x,xdata i) represents the predicted input variable matrix, ydata i Represents the predicted channel width matrix;

[0102] S25. Determine the lateral dissipation term of the debris flow by using a formula for determining the lateral dissipation term of the debris flow according to the final parameter of the model function;

[0103] The formula for determining the lateral dissipation term of debris flow is:

[0104]

[0105] Where ξ represents the lateral dissipation term of the debris flow, a, b, and c represent the final parameters of the model function, e represents a natural constant, and w represents the channel width.

[0106] For example, to determine the lateral dissipation term of the debris flow, the channel width, the velocity of the debris flowing out of the groove, the thickness of the final accumulation body, and the sliding distance obtained in step S3 can be collected first, and the data can be stored in the corresponding MATLAB variables, which are output variables (l) and input variables (v t (w), h t (w), w), as shown in Table 1. And execute steps S21-S24.

[0107] Table 1 Parameter statistics of the chute test

[0108] variable Name (Unit) Measurement method l Channel width (m) - <![CDATA[v t ]]> Exit speed (m / s) High-speed camera <![CDATA[h t ]]> Final accumulation thickness (m) Digital elevation camera w Sliding distance (m) Digital elevation camera

[0109] In a specific embodiment, the final parameters of the model function may be: a=1.41, b=1.02, c=1.12. Thus, the formula for determining the lateral dissipation term of the debris flow may be:

[0110]

[0111] From this, the lateral dissipation term of the debris flow can be determined.

[0112] S3. A constant flow model is established based on the first functional relationship, the second functional relationship and the lateral dissipation term of the debris flow.

[0113] In this embodiment, in step S3, the constant flow model is:

[0114]

[0115] Where l represents the channel width, μ t represents the friction coefficient of the flow channel, θ represents the slope angle of the accumulation area, and θ t represents the slope angle of the flow channel, g represents the acceleration of gravity, h t (w) represents the functional relationship between the thickness of the stack and the channel width, v t(w) represents the functional relationship between the outflow velocity and the channel width, and ξ represents the lateral dissipation term of the debris flow.

[0116] In a specific embodiment, the constant flow model can be determined as:

[0117]

[0118] Where l represents the channel width, v t (w) represents the functional relationship between the exit velocity and the channel width, g represents the acceleration due to gravity, and h t (w) represents the functional relationship between the stack thickness and the channel width, e represents a natural constant, and w represents the channel width.

[0119] S4. Determine the width of the flow channel of the potential landslide at the measurement site based on the satellite image, input the flow channel width into a constant flow model, and output a predicted sliding distance of the potential landslide at the measurement site through the constant flow model.

[0120] In this embodiment, after step S4, the method further includes: determining the risk level of potential landslide at the measurement site according to the predicted sliding distance.

[0121] In a specific embodiment, the risk level S of the potential landslide at the measurement site determined according to the predicted sliding distance l may be as shown in Table 2.

[0122] Table 2 Classification of landslide potential sliding distance risk levels

[0123] Risk Level Sliding distance l(km) Distance from the project S (km) one l≥10 S≤10 two 1＜l＜10 10＜S＜20 three l≤1l S≥20

[0124] Alternatively, if the landslide occurring upstream is large in volume and has the potential to form a landslide dam, this risk can be considered in advance during the early identification of the landslide.

[0125] Alternatively, as Figure 5 As shown, Figure 5 The figure is a comparison diagram between the landslide sliding distance calculated by the model and the monitoring results of the actual test. Figure 5 The comparison results show that the calculation accuracy of the constant flow model is better.

[0126] In this embodiment, the landslide sliding distance can be calculated by establishing a mathematical model and using satellite images. Therefore, a large amount of on-site surveys is not required, which can greatly save costs. This is undoubtedly of great significance for the study of complex landslide activities in high-altitude and cold mountainous areas.

[0127] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and it should be understood that the scope of protection of the present invention is not limited to such specific descriptions and embodiments. Those skilled in the art can make various other specific variations and combinations based on the technical teachings disclosed in the present invention without departing from the essence of the present invention, and such variations and combinations are still within the scope of protection of the invention.

Claims

1. A method for predicting the sliding distance of high-speed long-distance landslides based on the export diffusion effect, characterized in that: The method comprises: S1. According to the pre-built multi-stage chute, the outflow speed of the debris when it flows out of the chute is obtained by a high-speed camera, and the final accumulation body thickness and sliding distance are obtained by a digital elevation camera, and a first functional relationship between the outflow speed and the channel width of the multi-stage chute and a second functional relationship between the final accumulation body thickness and the width of the multi-stage chute is obtained; S2, determining a lateral dissipation term of the debris flow according to the channel width, the rushing velocity of the debris when it flows out of the groove, the thickness of the final accumulation body and the sliding distance; S3, establishing a constant flow model according to the first functional relationship, the second functional relationship and the lateral dissipation term of the debris flow; S4. Determine the flow channel width of the potential landslide at the measurement site based on the satellite image, input the flow channel width into the constant flow model, and output the predicted sliding distance of the potential landslide at the measurement site through the constant flow model.

2. The method according to claim 1, characterized in that In step S1, the multi-stage chute is constructed according to the slope data of the landslide engineering area, and the field data includes the slope and lithology of the slope; The multi-stage chute is a three-stage chute with a variable width, and the contact surface of the multi-stage chute is a 10 cm acrylic plate, the surface of the acrylic plate is covered with a black matte material; The first section of the multi-stage chute is a stacking platform, the length and width of the stacking platform are 3 meters and 2 meters respectively; The second section of the multi-stage chute is a first chute, the length and width of the first chute are 2 meters and 0.5 meters respectively; The third section of the multi-stage chute is a second chute, and the length and width of the second chute are 2 meters and 0.5 meters respectively; The second chute is connected to the material area, the material area is located behind the second chute and has a length of 0.5 meters; The first chute and the second chute are connected by bolts, and the inclination adjustment range of the first chute and the second chute is 0-65° respectively; Four MAL-mini cylinders are respectively installed on the first chute and the second chute, and the MAL-mini cylinders are connected to the side plates for adjusting the width of the first chute and the second chute between 150 mm and 500 mm respectively.

3. The method according to claim 2, characterized in that In step S1, the first functional relationship is: Among them, v t (w) represents the functional relationship between the exit velocity and the channel width, v o represents the reference velocity of the debris flow when it rushes out of the groove, A, B, C represent constants determined by data fitting, π represents pi, e represents a natural constant, and w represents the channel width; The second functional relationship is: Among them, h t (w) represents the functional relationship between the thickness of the deposit and the width of the channel, h0 represents the thickness of the deposit after the debris flow is flushed out of the outflow groove at a reference rate, A represents a constant determined by data fitting, e represents a natural constant, w represents the channel width, and t is the time effect coefficient determined by data fitting.

4. The method according to claim 1, characterized in that The step S2 specifically includes: S21, determining a channel width matrix according to the channel width; The channel width matrix is: ydata=ydata(l) Among them, ydata represents the channel width matrix, l represents the channel width; S22, determining an input variable matrix according to the ejection velocity of the debris when it flows out of the groove, the thickness of the final accumulation body and the sliding distance; The input variable matrix is: Among them, F(x,xdata) represents the input variable matrix, F(x,xdata(v t ))、F(x,xdata(h t )), F(x,xdata(w)) are respectively the outflow velocity matrix, outflow velocity matrix, and sliding distance matrix determined according to the outflow velocity, final accumulation thickness, and sliding distance when the debris flows out of the groove; S23, defining a model function, initializing initial parameters of the model function, and obtaining a prediction channel width matrix according to the input variable matrix; S24, using the Lsqcurvefit function to update the initial parameters of the model function to obtain the final parameters of the model function; The Lsqcurvefit function is: Among them, F(x,xdata) represents the input variable matrix, ydata represents the channel width matrix, and F(x,xdata i ) represents the prediction input variable matrix, ydata i Represents the prediction channel width matrix; S25, determining the lateral dissipation term of the debris flow according to the final parameter of the model function and by using a formula for determining the lateral dissipation term of the debris flow; The formula for determining the lateral dissipation term of the debris flow is: Among them, ξ represents the lateral dissipation term of debris flow, a, b, c represent the final parameters of the model function, e represents a natural constant, and w represents the channel width.

5. The method according to claim 1, characterized in that In step S3, the constant flow model is: Where l represents the channel width, μ t represents the friction coefficient of the flow channel, θ represents the slope angle of the accumulation zone, and θ t represents the slope angle of the flow channel, g represents the acceleration of gravity, h t (w) represents the functional relationship between the thickness of the deposit and the width of the channel, v t (w) represents the functional relationship between the ejection velocity and the channel width, and ξ represents the lateral dissipation term of the debris flow.

6. The method according to claim 1, characterized in that The method further comprises: The risk level of the potential landslide at the measurement site is determined based on the predicted sliding distance.

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