Method and system for predicting dam burst disaster chain based on landslide river blocking form

Abstract

The invention discloses a dam burst disaster chain prediction method based on a landslide river blocking form, and the method comprises the steps: analyzing the overall surface deformation of a to-be-predicted region in a set time period, carrying out the geocoding of a deformation monitoring result, obtaining a time sequence deformation diagram, and finding a landslide hidden danger disaster point according to the time sequence deformation diagram; acquiring landslide data of landslide hidden danger disaster points and processing the landslide data to obtain a landslide key parameter table and a related engineering chart; calculating a stability calculation result according to the landslide data, and constructing a landslide deformation scene; calculating the river blocking height of the barrier lake; and forming a barrier lake according to landslide river blocking, performing flood routing two-dimensional simulation, and performing disaster evaluation and analysis before and after flood outburst according to a flood routing result to obtain an analysis evaluation result. According to the method, dynamic prediction of dam burst on the whole link of the disaster chain can be realized, the dynamic process of the disaster chain can be accurately inverted, a scientific and comprehensive basis is provided for disaster prevention and reduction decisions, and disaster loss is reduced to the greatest extent.

Description

Technical Field

[0001] This invention relates to the field of disaster identification technology, specifically to a method and system for predicting the disaster chain of a landslide-blocked river dam failure. Background Technology

[0002] High mountain and canyon areas have complex topography, active geological structures, and abundant water resources, making them high-risk areas for landslide-river damming-flood disaster chains. Landslides in these areas are triggered by a variety of factors, including endogenic geological processes such as earthquakes and tectonic movements, as well as exogenic factors such as heavy rainfall and snowmelt. Once a landslide occurs, a large amount of landslide material can easily be washed into the river channel, forming a dam and subsequently blocking the river to form a barrier lake.

[0003] Traditional methods for preventing and controlling geological disasters in high mountain and canyon areas have focused on monitoring and early warning of single disaster types, such as independent landslides or single floods. They lack a systematic understanding and prediction of the landslide-river dam-flood disaster chain, which makes it difficult to meet the needs of disaster prevention and mitigation decision-making for high-precision prediction of the entire disaster chain.

[0004] With the economic and social development of high mountain and canyon areas, infrastructure such as transportation arteries, water conservancy hubs, towns, and villages are becoming increasingly densely distributed along with the population. The amplified effect of the landslide-river damming-flood disaster chain is posing an increasingly severe threat. Floods triggered by the collapse of landslide dams can not only destroy downstream infrastructure but also cause significant casualties and ecological damage. At the same time, people's requirements for the scientific and forward-looking nature of geological disaster prevention and control are constantly increasing, and there is an urgent need for a prediction method that can cover the entire disaster chain and integrate multi-source data and multidisciplinary models. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for predicting the disaster chain of landslide-blocked river dam failure, so as to accurately invert the dynamic process of the disaster chain, provide a reliable scientific basis for disaster scenario analysis, risk assessment and disaster prevention and mitigation deployment, and minimize disaster losses.

[0006] This invention is achieved through the following technical solution:

[0007] In a first aspect, the first embodiment of the present invention provides a method for predicting the disaster chain of a landslide-blocked river dam failure, comprising: The overall surface deformation of the area to be predicted is analyzed within a set time period to obtain deformation monitoring results. After geocoding the deformation monitoring results, a time series deformation map is obtained. Based on the time series deformation map, potential landslide hazard points are identified. The landslide data of potential landslide sites were acquired and processed to obtain a table of key landslide parameters and related engineering charts. Based on the deformation characteristics of the landslides, the landslides were divided into primary and secondary landslide areas. Stability calculation results are calculated based on landslide data. Landslide deformation scenarios are constructed using stability calculation results and combined with landslide spatiotemporal evolution characteristics and trend analysis results. The landslide deformation scenarios include strong disturbance-triggered landslide deformation scenarios and weak disturbance-triggered landslide deformation scenarios. The height of the barrier lake blocking the river was calculated based on landslide data and landslide deformation scenarios; Based on the landslide blocking the river and forming a barrier lake, we conducted upstream barrier lake parameter analysis, barrier body stability assessment, upstream inundation assessment, and downstream breach flood disaster assessment. We also completed a two-dimensional simulation of flood evolution and conducted disaster evaluation and analysis before and after flood breach based on the flood evolution results, obtaining the analysis and evaluation results.

[0008] Secondly, another embodiment of the present invention provides a landslide-blocking river morphology-based barrier dam failure disaster chain prediction system, used to implement the landslide-blocking river morphology-based barrier dam failure disaster chain prediction method described in the above embodiments, including: a hazard identification module, an acquisition module, a stability calculation module, a river blockage height calculation module, and an evaluation module. The hazard identification module is used to analyze the overall surface deformation of the area to be predicted within a set time period, obtain deformation monitoring results, and obtain a time series deformation map after geocoding the deformation monitoring results. Based on the time series deformation map, landslide hazard points are found. The acquisition module is used to acquire and process landslide data at potential landslide sites, obtain a key landslide parameter table and related engineering charts, and divide the landslide into a primary landslide area and a secondary landslide area based on the deformation characteristics of the landslide. The stability calculation module is used to calculate the stability calculation results based on the landslide data, and to construct landslide deformation scenarios using the stability calculation results and the landslide spatiotemporal evolution characteristics and trend analysis results. The landslide deformation scenarios include strong disturbance-triggered landslide deformation scenarios and weak disturbance-triggered landslide deformation scenarios. The blockage height calculation module is used to calculate the blockage height of the landslide dammed lake based on landslide data and landslide deformation scenarios. The evaluation module is used to analyze upstream barrier lake parameters, determine the stability of the barrier body, assess upstream inundation, and assess downstream flood disaster based on the formation of a barrier lake by a landslide blocking the river. It also completes a two-dimensional simulation of flood evolution, evaluates and analyzes the disaster before and after the flood breach based on the flood evolution results, and obtains the analysis and evaluation results.

[0009] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0010] This invention provides a method and system for predicting the disaster chain of a landslide-blocked river dam failure, which integrates multi-source data collection, multi-dimensional analysis, and multi-disciplinary models. It can not only achieve dynamic prediction of the entire disaster chain of a landslide-blocked river dam failure, but also accurately invert the dynamic process of the disaster chain, providing a scientific and comprehensive basis for disaster prevention and mitigation decision-making and minimizing disaster losses. Attached Figure Description

[0011] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be considered as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the drawings: Figure 1 A flowchart of a method for predicting the disaster chain of a landslide-blocked river dam failure based on the landslide blocking morphology provided in the first embodiment of the present invention; Figure 2 This is a schematic diagram of landslide hazard identification based on InSAR in the first embodiment of the present invention; Figure 3 shows the calculation of the blockage height of the landslide dammed lake based on MatDEM in the first embodiment of the present invention; Figure 4 is a schematic diagram of the flood disaster assessment of the upstream of the landslide dammed lake based on GIS in the first embodiment of the present invention; Figure 5 is a two-dimensional evolution diagram of downstream outburst flood in the first embodiment of the present invention; Figure 6 The diagram below shows a structural block diagram of a landslide-blocking dam failure disaster chain prediction system based on landslide blocking morphology, which is provided as another embodiment of the present invention. Detailed Implementation

[0012] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0013] like Figure 1 As shown, the first embodiment of the present invention provides a method for predicting the disaster chain of a landslide-blocked river dam failure, comprising: S1: Identification of potential landslide hazards in the region: Analyze the overall surface deformation of the region to be predicted within a set time period, obtain deformation monitoring results, geocode the deformation monitoring results to obtain a time series deformation map, and find the points with potential landslide hazards based on the time series deformation map; S2: Acquisition and processing of landslide parameters: Acquire and process landslide data at potential landslide sites to obtain key landslide parameter tables and related engineering charts, and classify the landslide into primary and secondary landslide areas based on the deformation characteristics of the landslide. S3: Stability Calculation and Scenario Assumptions: Stability calculation results are calculated based on landslide data. Landslide deformation scenarios are constructed using the stability calculation results and combined with the spatiotemporal evolution characteristics and trend analysis results of landslides. The landslide deformation scenarios include strong disturbance-triggered landslide deformation scenarios and weak disturbance-triggered landslide deformation scenarios. S4: Calculation of the blocking height of the landslide dammed lake: Calculate the blocking height of the landslide dammed lake based on landslide data and landslide deformation scenarios; S5: Disaster assessment before and after flood breach: Based on the landslide blocking the river and forming a barrier lake, conduct upstream barrier lake parameter analysis, barrier body stability assessment, upstream inundation assessment, and downstream breach flood disaster assessment, and complete a two-dimensional simulation of flood evolution. Based on the flood evolution results, conduct disaster assessment and analysis before and after flood breach, and obtain the analysis and evaluation results.

[0014] Given the complex terrain and challenging data acquisition characteristics of high mountain and canyon areas, preprocessing of various raw data is necessary during data collection and processing. Satellite remote sensing imagery (InSAR) requires atmospheric correction and phase unwrapping to remove interference; UAV aerial imagery requires distortion correction and stitching to ensure the accuracy of the 3D model; and field survey data requires standardized entry and verification to avoid data errors affecting prediction results.

[0015] Specifically, S1, identification of potential landslide hazards in the region.

[0016] In the landslide hazard identification phase, based on InSAR (In-Satellite Remote Sensing) technology, large-scale, high-precision monitoring of target watersheds in high mountain and canyon areas is conducted to capture surface deformation characteristics. Combined with regional geological background data, potential landslide hazard points are screened out, achieving preliminary location and identification of potential landslide hazards and laying the foundation for subsequent precise investigations. This embodiment uses SBAS-InSAR technology to analyze the overall surface deformation of a certain area in Ganzi Prefecture over a certain period. The basic data used are divided into three categories, all from the ASF website: First, Sentinel-1 ASLC image data, including ascending and descending orbit data (imaging mode IW, polarization VV, resolution 5×20m, C-band, incident angles of 41.85° and 40.49° respectively, and a revisit period of 12 days); second, AUX_POEORB POD precise orbit data (including return orbit data with positioning accuracy better than 10cm, generated within 3 hours of GNSS data reception and covering one satellite orbital period); and third, 12.5m high-resolution reference DEM data for the area.

[0017] The data processing follows a specific workflow: First, data is read and preprocessed, importing SAR images and performing corrections and filtering to improve data quality. Next, image registration and resampling are performed to ensure consistent spatial correspondence among multiple images, facilitating subsequent operations. Then, a connectivity map is generated, with the program automatically selecting the master image and the others as slave images to generate interferometric pairs (spatial baseline mean 42.76m, maximum 110m, minimum 3.34m; temporal baseline mean 46d, maximum 84d, minimum 12d), ensuring ideal spatiotemporal baseline conditions for all data. Finally, differential interferometric flow processing is performed to compare different... Temporal SAR images are used to monitor minute changes on the Earth's surface. Short-spatial-time baseline SAR data subsets are combined to overcome the loss of correlation. External DEMs are used to remove topographic phase and generate time-series differential interferometric atlases, removing atmospheric delay, orbital errors, and other phase errors. Phase unwrapping is then performed to address discontinuities caused by phase periodicity, obtaining continuous phase information while eliminating image pairs with low coherence and poor unwrapping performance. Finally, geocoding is performed to convert the SAR slant-range coordinate system-based image results to the WGS-84 geographic coordinate system, completing topographic distortion correction and generating digital elevation models and deformation maps.

[0018] like Figure 2 As shown, the time-series deformation map obtained after geocoding indicates that the deformation rate ranges from -11.84 to 2.76 cm / y in the monitoring area, with an availability rate of approximately 78%. Negative values ​​represent subsidence deformation away from the satellite antenna, while positive values ​​represent uplift deformation closer to the satellite antenna. Data also shows a potential landslide hazard point near Anangzhai Village, Danba County, Ganzi Prefecture. The overall deformation rate of this hazard point is -8.26 cm / y, which is considered medium to high. Targeted monitoring and prevention measures are needed to prevent the escalation of the disaster.

[0019] S2. Acquisition and processing of landslide parameters.

[0020] For the identified landslide hazard points, on-site geological surveys and mapping, as well as drone aerial photography, were conducted. The on-site surveys focused on recording geological information such as the lithology, structural features, and signs of slope deformation of the landslide body. The drone aerial photography, through multi-angle, high-resolution image acquisition and combined with photogrammetry technology, constructed a three-dimensional model of the landslide body, accurately extracting key parameters such as landslide volume, landslide boundaries, and sliding surface morphology, providing data support for stability analysis.

[0021] Geological and topographic maps, regional tectonic outline maps, previous exploration reports of Danba County, and meteorological and hydrological data were collected. Combined with SBAS-InSAR deformation monitoring results, the core exploration area was delineated. Simultaneously, RTK receivers, multi-rotor UAVs (equipped with high-resolution cameras), and small geological drilling rigs were matched and debugged, and UAV flight paths were planned (80% overlap in the flight direction and 60% in the lateral direction). On-site exploration was conducted, including UAV aerial photography (flying at 100-300m altitude based on the landslide scale, generating DOM and DSM data, and then outlining and correcting the landslide boundary), geological mapping (laying out longitudinal and transverse profiles at a scale of 1:2000, recording lithology, structural plane orientation, and fracture characteristics), and borehole exploration (laying 3-5 boreholes at key locations, recording core samples, probing the sliding surface, conducting SPT (in-situ borehole testing), and collecting undisturbed soil samples). Multi-dimensional data on landslide boundaries, surface deformation, potential sliding surfaces, and landslide thickness were collected.

[0022] Further, indoor analysis was conducted. First, combining borehole slip surface thickness and DSM data, a slip surface thickness contour map was generated using Kriging interpolation. Then, the landslide volume was calculated using the vertical section method and verified using the grid method (error ≤ 5%). Subsequently, the morphology of the potential slip surface was fitted by integrating borehole slip surface attitude, surface crack direction, and inclinometer data. The most dangerous slip surface was determined by combining the results of indoor direct shear tests. At the same time, the parameters obtained in the field were compared with the SBAS-InSAR deformation results (error ≤ 10%), and areas with deviations were supplemented by further investigation and correction. Finally, the data were compiled into a key landslide parameter table, and engineering geological plan maps, engineering geological profile maps, and other charts were drawn. Based on the deformation characteristics of the landslide, it was divided into primary and secondary slip zones.

[0023] S3. Stability Calculation and Scenario Assumptions

[0024] In the landslide stability analysis phase, a multivariate analysis method was adopted to comprehensively consider factors such as the landslide's own weight, groundwater seepage, seismic load, and rainfall infiltration, and a stability evaluation model was established to calculate the stability coefficient of the landslide under different working conditions. At the same time, based on regional historical disaster data and the probability of extreme weather and geological events, multiple landslide instability scenarios were assumed, including instability modes of different scales such as local collapse and overall instability, covering possible disaster situations.

[0025] Specifically, the sliding body density obtained from the borehole in-situ test (SPT) in step S2, the natural shear strength (cohesion c, internal friction angle φ) of the sliding body measured by the indoor direct shear test, and the residual shear strength of the sliding surface are used as basic input parameters. The parameters are adjusted for three typical working conditions: natural, rainstorm, and earthquake. Under the rainstorm condition, referring to the regional rainfall infiltration pattern, the saturated unit weight of the sliding body is increased by 10%-15%, and the shear strength is decreased by 20%-25% (simulating the softening effect of rainwater). Under the earthquake condition, based on the peak ground acceleration (e.g., 0.15g) of the study area, the seismic inertial force is calculated by the quasi-static method and superimposed on the stress analysis of the sliding body. The calculation process is divided into two categories: First, using the Swedish slice method empirical formula, limit equilibrium models are established for the main and secondary sliding zones of the landslide, and the stability coefficient (K) under different working conditions is manually iterated to preliminarily determine the stable state; Second, using GeoStudio software (the SEEP / W module simulates the seepage field distribution after rainstorm infiltration, and the SLOPE / W module performs stability calculations based on limit equilibrium theory), the longitudinal and transverse profiles of the landslide and sliding body parameters generated by S2 are imported, and working conditions consistent with the empirical formula are set. The software automatically divides the calculation blocks, iterates and solves, and outputs the stability coefficient and the safety margin of the sliding surface, realizing mutual verification of the results of the two calculation methods (error must be ≤5%) to ensure data reliability.

[0026] Specifically, using stability calculation results and combining the spatiotemporal evolution characteristics of landslides monitored by SBAS-InSAR (continuous deformation of the main landslide area and phased stability of the secondary landslide area) and trend analysis results, two landslide deformation scenarios were further constructed: Scenario 1 is triggered by strong disturbance (such as heavy rainfall superimposed with an earthquake), where the pushing force generated after the main landslide area becomes unstable exceeds the anti-sliding threshold of the secondary landslide area, causing the main landslide area and the secondary landslide area to deform synchronously, and the entire landslide body slides along the deep potential sliding surface, which may trigger large-scale soil displacement; Scenario 2 is triggered by weak disturbance (such as local rainstorm or small earthquake), where only the main landslide area deforms first because the stability coefficient is lower than the critical value (K<1.0), while the secondary landslide area does not become unstable because it still maintains a certain anti-sliding capacity. The landslide body slides locally along the shallow sliding surface with the main landslide area as the core, and the scope of influence is relatively limited.

[0027] S4. Calculation of the height of the barrier lake blocking the river.

[0028] For hypothetical landslide instability scenarios, this study combines data such as landslide volume, river cross-sectional morphology, and river flow velocity and flow rate. Through multi-dimensional calculation methods (coupling empirical formulas and numerical calculations), the study simulates the deposition process of the landslide mass after it enters the river channel. It analyzes the water-blocking degree of the river channel under different landslide mass deposition volumes, thereby determining the stable height and the maximum possible height of the landslide dammed lake after the river is blocked, and delineating risk boundaries for subsequent disaster assessment.

[0029] Based on the landslide volume and boundary obtained in step S2 and the landslide deformation scenario constructed in step S3, the height of the barrier lake formed by the Anangzhai landslide under different scenarios was determined through a three-step process: river blocking condition identification, multi-method height calculation, and result fusion verification. The specific implementation process is as follows:

[0030] First, a landslide-river closure condition assessment is conducted to determine whether the landslide body has the capacity to completely block the river. On one hand, an empirical formula for the minimum landslide volume required for river closure is adopted: ; Among them, V min H represents the minimum volume (m³) of a landslide blocking a river. r B represents the average depth of the river (m); r Indicates the width of the riverbed (m); This represents the internal friction angle (°) of a landslide body under saturated water conditions.

[0031] The minimum volume threshold for complete damming of the Xiaojinchuan River in the study area was calculated. Combining the total volume of the Anangzhai landslide (main landslide area + secondary landslide area) measured in step S2 and the volume of landslides involved in the two deformation scenarios in step S3, it was determined that it far exceeds the minimum volume threshold for damming.

[0032] On the other hand, applying the hydrodynamic formula: ; Among them, Q s This represents the volume of rock and soil entering the river per unit time (m³ / s). Q represents a coefficient related to the saturated unit weight of the soil / rock mass, the unit weight of the river water, the geometric characteristics of the riverbed, and the friction angle between the soil / rock mass and the riverbed. r This represents the flow rate of the river per unit time (m³ / s).

[0033] Analysis of the current flow capacity of the river channel and the potential water-blocking area formed after the landslide enters the river shows that the landslide can significantly compress the flow cross-section after sliding into the river channel, meeting the hydrodynamic conditions for complete river closure. Based on comprehensive analysis, it is determined that the Anangzhai landslide possesses the basic conditions for complete river closure.

[0034] Subsequently, a combination of empirical formulas and numerical simulations was used to calculate the blockage height under different scenarios. Calculations were performed for the two landslide deformation scenarios proposed in step S3: one using empirical formulas, including a slip distance estimation method. ; in, is the friction coefficient; V is the landslide volume (m³); a and b are empirical coefficients, a = -0.15666, b = 0.62419; ; in, L represents the vertical drop (m) between the highest point at the rear edge of the landslide and the point where the sliding distance is calculated. max The maximum sliding distance of the landslide (m); ; Where L1 is the distance from the landslide front to the opposite bank of the river (m); h1 is the average thickness of the landslide mass (m); B is the width of the river (m); H d The height of the dammed river (m).

[0035] Dynamic equilibrium method: ; Where f is the equivalent friction coefficient, taken as 0.25; L2 is the horizontal displacement (m) of the sliding body's center of mass. v is the drop in mass of the sliding body (m); s The sliding speed of the sliding body after sliding down a unit distance (horizontal displacement L2) along the sliding surface; ; Where F1 is the remaining sliding force (kN / m), which is taken as 0 when the landslide ends; F2 is the landslide sliding force (kN / m); γ is the landslide weight (kN / m³), γ=24kN / m³; H is the height of the landslide blocking the river (m). Let be the friction angle of the sliding soil. During the sliding process, the soil becomes loose. .

[0036] Second, the discrete element method (DEM) numerical simulation is used. Using the DEM MatDEM software, a three-dimensional geological model of the landslide (including lithological parameters of the landslide body and mechanical parameters of the sliding surface) constructed in S2 is imported, along with river topographic data. The landslide movement process is simulated under two scenarios: Scenario 1 (simultaneous deformation of the main and secondary landslide areas) and Scenario 2 (deformation of only the main landslide area). The software calculates the depositional morphology and top elevation of the landslide body after it enters the river, and outputs the river blockage height. Figure 3 As shown.

[0037] Finally, the calculation results were integrated and determined. Since the two empirical formula methods are affected by the parameter values ​​(such as the sliding body friction coefficient and the river channel roughness) and the assumptions, and the MatDEM numerical simulation method can more realistically reflect the sliding body accumulation process, but there are boundary condition simplification errors, the calculation results of the three methods were statistically analyzed, and the average value was taken as the final blockage height after removing outliers.

[0038] S5. Disaster Assessment Before and After Flood Breach

[0039] The landslide at Anangzhai formed a barrier lake blocking the river. Following a logical process of upstream barrier lake parameter analysis, barrier body stability assessment, upstream inundation assessment, determination of breach parameters for the burst flood, flow process line calculation, burst flood evolution simulation, and disaster assessment, a full-chain disaster assessment was conducted before and after the flood burst. The specific implementation process is as follows:

[0040] S51. Analysis of upstream landslide dam parameters: Based on the digital elevation model (DEM) data of the Anangzhai area, basic data processing was carried out using the hydrological analysis function of ArcGIS software. First, depression filling was performed to eliminate depression errors in the DEM data and ensure the accuracy of the water flow path simulation. Then, the water flow direction was calculated using the flow direction analysis tool, and the effective catchment area of ​​the upstream area was determined by combining the flow calculation function. Finally, the "surface volume" function in the 3D analysis tool was called to calculate the inundated area and water storage volume corresponding to different water level elevations (such as 2090m, 2100m, 2110m up to the overflow dam level), and the water level-storage capacity curve of the watershed in the study area was derived accordingly.

[0041] S52. Stability assessment of the landslide dam: Based on the derived water level-storage capacity curves and the core parameters of the two landslide dam scenarios determined in S4, the stability assessment is conducted using the CASAGLI empirical discrimination formula. Specifically, two discrimination formulas are selected: ; ; Among them, H D V represents the maximum dam height. L A represents the water storage volume. C The area of ​​the upstream backwater zone; I s and I a Here are the stability criteria parameters for the landslide dam, where: I s < -3, the landslide dam is unstable; -3 < I s <0, the stability of the landslide dam is uncertain; I s >0, the landslide dam is stable; I a >3, the landslide dam is stable; I a <3 The stability of the landslide dam is uncertain.

[0042] Calculation results show that the stability of both landslide dams is unstable, and the risk of dam overflow and breach due to continuous rise in water level should be noted.

[0043] S53. Upstream Inundation Assessment: During the landslide dam inundation disaster assessment stage, the calculated landslide dam height data is overlaid with the target watershed digital elevation model (DEM) using GIS hydrological analysis functions to extract the inundation range of the landslide dam. Combined with socio-economic data such as towns, villages, farmland, and transportation arteries within the watershed, the affected population, property losses, and ecological damage within the inundation area are assessed to form a visualized inundation disaster distribution map.

[0044] Specifically, based on the 3D simulation capabilities of ArcScene software, and combining the overtopping water level elevations for two scenarios, the upstream inundation range and depth were simulated: First, the overtopping water level elevation parameters were input into the software, and the inundation analysis boundary was set as the effective upstream catchment area; then, the software's spatial analysis module was used to calculate and generate inundation depth distribution maps and inundation range vector maps for different scenarios, such as... Figure 4 As shown; finally, combining the geographical location data of upstream villages (Guanzhou Village, Banshanmen Town, Tuanjie Village, etc.) and transportation trunk lines (National Highway G350), the flood impact was statistically analyzed and a flood disaster assessment was conducted before the breach.

[0045] S54. In the downstream flood disaster assessment stage, on the one hand, hydraulic empirical formulas are used to preliminarily estimate parameters such as the maximum peak flow and flood propagation speed when the landslide dam breaks; on the other hand, a hydrodynamic model is introduced to simulate the evolution of the flood in the downstream river channel, taking into account factors such as river roughness, tributary inflow, and dike engineering, to accurately calculate the flood level, flow velocity and inundation duration at different cross sections, and comprehensively assess the impact risk of the flood on the downstream area.

[0046] Specifically, flood evolution simulation was conducted after the breach of the landslide dam, and breach parameters were determined. First, the breach shape was determined to be trapezoidal (consistent with water erosion characteristics). The breach geometry was controlled by three parameters: width coefficient k1, depth coefficient k2, and breach slope ratio z. The width coefficient k1 and depth coefficient k2 were set according to the degree of breach (complete breach k1=k2=1.0, 1 / 2 breach k1=k2=0.50, 1 / 3 breach k1=k2=0.33); the breach slope ratio z was calculated based on the mechanical parameters of the landslide dam. ; ; ; ; in, B0 is the width of the dam body (m); B1 is the width of the dam crest (m); k1 is the width coefficient. ρ is the dam height (m); h is the breach depth (m); k2 is the depth coefficient; b is the breach bottom width (m); z is the breach slope ratio; according to the data, the internal friction angle of the landslide dam body is ( The value is 16°.

[0047] The dam body parameters for the two scenarios were determined, and the breach parameters for different working conditions were further calculated.

[0048] S55 calculates the breach flow hydrograph at the time of dam failure using the peak flow calculation formula for the breach: ; in, The peak flow rate at the breach is m³ / s; g is the acceleration due to gravity, 9.8 m² / s²; parameters It can be obtained from the following formula: ; In the above formula, m is the cross-sectional shape parameter, and its calculation formula is: ; The peak flood discharge at the breach was calculated under different operating conditions. Furthermore, the flow process curves at the breach under different operating conditions were obtained using the Capart hydrodynamic calculation model: ; Among them, V B Q represents the corresponding dam break discharge volume (m³). B Q is the outflow rate from the breach (m³ / s); p The peak flow rate at the breach (m³ / s); T p The moment (s) when the breach reaches its peak flow rate.

[0049] The proposed method equals the time integral of the outflow rate when the landslide dam breaks. The final result is a function formula relating the outflow rate at the breach, the dam release volume, and the evolution of the landslide dam's water level over time. ; in, Let the breakdown time (s) be a fixed time interval, and its relationship with the time interval t be: ; In the formula, t is a fixed time interval (s).

[0050] Based on the above formulas, breach parameters, and reservoir capacity data of S51, the flood process curves for breaches under different working conditions can be calculated.

[0051] S56. Simulation and Disaster Assessment of Breach Flood Evolution: A two-dimensional unsteady flow flood evolution model was constructed using HEC-RAS software (combined with the HEC-GeoRAS plugin). First, the topographic data of the study area was imported and converted to an adapted coordinate system. A 20.8km stretch of river channel from the landslide dam to Danba County was selected as the simulation area, with a grid resolution of 20m×20m. The upstream and downstream boundaries were then defined. Next, typical cross-sections were laid out within the simulation area, and the locations of bridges crossing the flood were marked. Then, boundary conditions were set (the breach flow process line was input for the upstream boundary, and normal water depth was used for the downstream boundary). Based on the regional surface characteristics, Manning roughness coefficients were assigned (0.04 for the river channel, 0.05 for the riverbank, and 0.06 for tree-covered hillsides). Model parameter configuration and verification were completed, and a two-dimensional flood evolution simulation was performed. Figure 5 As shown.

[0052] A comprehensive and all-round assessment and analysis of flood disasters will be conducted based on the results of flood evolution, providing support and basis for disaster prevention and mitigation.

[0053] This invention provides a method for predicting the disaster chain of a landslide-blocked river dam failure based on landslide damming morphology. This method integrates multi-source data collection, multi-dimensional analysis, and multi-disciplinary models, enabling dynamic prediction of all aspects of the disaster chain and accurate inversion of the disaster chain dynamics. This provides a scientific and comprehensive basis for disaster prevention and mitigation decision-making, minimizing disaster losses.

[0054] like Figure 6 As shown, another embodiment of the present invention provides a landslide-blocking dam failure disaster chain prediction system, used to implement the landslide-blocking dam failure disaster chain prediction method described in the first embodiment above. The system includes: a hazard identification module, an acquisition module, a stability calculation module, a dam height calculation module, and an evaluation module. The hazard identification module is used to analyze the overall surface deformation of the area to be predicted within a set time period, obtain deformation monitoring results, and obtain a time series deformation map after geocoding the deformation monitoring results. Based on the time series deformation map, landslide hazard points are found. The acquisition module is used to acquire and process landslide data at potential landslide sites, obtain a key landslide parameter table and related engineering charts, and divide the landslide into a primary landslide area and a secondary landslide area based on the deformation characteristics of the landslide. The stability calculation module is used to calculate the stability calculation results based on the landslide data, and to construct landslide deformation scenarios using the stability calculation results and the landslide spatiotemporal evolution characteristics and trend analysis results. The landslide deformation scenarios include strong disturbance-triggered landslide deformation scenarios and weak disturbance-triggered landslide deformation scenarios. The blockage height calculation module is used to calculate the blockage height of the landslide dammed lake based on landslide data and landslide deformation scenarios. The evaluation module is used to analyze upstream barrier lake parameters, determine the stability of the barrier body, assess upstream inundation, and assess downstream flood disaster based on the formation of a barrier lake by a landslide blocking the river. It also completes a two-dimensional simulation of flood evolution, evaluates and analyzes the disaster before and after the flood breach based on the flood evolution results, and obtains the analysis and evaluation results.

[0055] The present invention provides a disaster chain prediction system for landslide-blocked river dam failure based on landslide dam morphology. This system integrates multi-source data collection, multi-dimensional analysis and multi-disciplinary models, enabling dynamic prediction of all links in the disaster chain and accurate inversion of the disaster chain dynamics. This provides a scientific and comprehensive basis for disaster prevention and mitigation decision-making, and minimizes disaster losses.

[0056] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for predicting the disaster chain of landslide-dammed river blockage based on landslide-blocking morphology, characterized in that, include: The overall surface deformation of the area to be predicted is analyzed within a set time period to obtain deformation monitoring results. After geocoding the deformation monitoring results, a time series deformation map is obtained. Based on the time series deformation map, potential landslide hazard points are identified. The landslide data of potential landslide sites were acquired and processed to obtain a table of key landslide parameters and related engineering charts. Based on the deformation characteristics of the landslides, the landslides were divided into primary and secondary landslide areas. Stability calculation results are calculated based on landslide data. Landslide deformation scenarios are constructed using stability calculation results and combined with landslide spatiotemporal evolution characteristics and trend analysis results. The landslide deformation scenarios include strong disturbance-triggered landslide deformation scenarios and weak disturbance-triggered landslide deformation scenarios. The height of the barrier lake blocking the river was calculated based on landslide data and landslide deformation scenarios; Based on the landslide blocking the river and forming a barrier lake, we conducted upstream barrier lake parameter analysis, barrier body stability assessment, upstream inundation assessment, and downstream breach flood disaster assessment. We also completed a two-dimensional simulation of flood evolution and conducted disaster evaluation and analysis before and after flood breach based on the flood evolution results, obtaining the analysis and evaluation results.

2. The method according to claim 1, characterized in that, The specific methods for obtaining and processing landslide data at potential landslide sites include: For the identified landslide hazard points, geological and topographic maps, regional tectonic outline maps, previous survey reports of the areas to be predicted, and meteorological and hydrological data were collected. Combined with deformation monitoring results, the core survey area was delineated. On-site investigation was carried out, and a three-dimensional geological model of the landslide was constructed by combining drone aerial photography, geological mapping and borehole exploration with photogrammetry technology. Data related to landslide volume, landslide boundary, surface deformation, potential sliding surface and thickness of the landslide body were extracted. Combining borehole slip body thickness with DSM data, a slip body thickness contour map was generated using Kriging interpolation. The landslide volume was then calculated using the vertical section method and verified using the grid method. The potential slip surface morphology was fitted by integrating borehole slip surface attitude, surface crack direction, and inclinometer data. The most dangerous slip surface was determined by combining indoor direct shear test results. The parameters obtained on-site were compared with deformation monitoring results, and areas with deviations were supplemented and corrected through investigation.

3. The method according to claim 1, characterized in that, The specific method for calculating the blocking height of the landslide dammed lake based on landslide data and landslide deformation scenarios includes: Based on the landslide volume, landslide boundary, and landslide deformation scenario, the conditions for blocking the river are assessed to determine whether the landslide body has the capacity to completely block the river. The minimum volume threshold for complete river blocking by the landslide body is calculated using an empirical formula for minimum landslide volume. The calculation formula is as follows: ； Among them, V min H represents the minimum volume of a landslide that blocks a river. r B represents the average depth of the river. r Indicates the width of the riverbed. This represents the internal friction angle of a landslide body under saturated water conditions. The hydrodynamic formula is used to analyze the current flow capacity of the river channel and the potential water-blocking area formed after the landslide enters the river. The hydrodynamic formula is as follows: ； Among them, Q s This represents the volume of rock and soil entering the river per unit time. Q represents a coefficient related to the saturated unit weight of the soil / rock mass, the unit weight of the river water, the geometric characteristics of the riverbed, and the friction angle between the soil / rock mass and the riverbed. r This represents the flow rate of the river per unit time. The height of the blocked river under different landslide deformation scenarios was calculated by combining empirical formulas and numerical simulation. The calculation results were statistically analyzed, and the average value was taken as the final height of the blocked river after removing outliers.

4. The method according to claim 3, characterized in that, The specific method for calculating the river blockage height under different landslide deformation scenarios using empirical formulas includes the sliding distance estimation method, the calculation formula of which is: ； in, Here, V is the friction coefficient, a is the landslide volume, and b is an empirical coefficient, where a = -0.15666 and b = 0.62419. ； in, L represents the vertical drop between the highest point at the rear edge of the landslide and the point where the sliding distance is calculated. max This represents the maximum sliding distance of the landslide. ； Where L1 is the distance from the landslide front to the opposite bank of the river, h1 is the average thickness of the landslide mass, B is the width of the river, and H... d The height of the dammed river.

5. The method according to claim 4, characterized in that, The specific method for calculating the river blockage height under different landslide deformation scenarios using empirical formulas includes the dynamic equilibrium method, and the calculation formula for the dynamic equilibrium method is as follows: ； in, Let L2 be the equivalent friction coefficient, taken as 0.25, and L2 be the horizontal displacement of the sliding body's center of mass. For the difference in the center of mass of the sliding body, v s The sliding speed of the sliding body after sliding down a unit distance along the sliding surface; ； Where F1 is the residual sliding force, which is taken as 0 when the landslide ends; F2 is the landslide impact force; γ is the landslide weight, γ = 24 kN / m³; and H is the height of the landslide blocking the river. Let be the friction angle of the sliding soil. During the sliding process, the soil becomes loose. =10°.

6. The method according to claim 5, characterized in that, The specific methods for calculating the height of the blocked river under different landslide deformation scenarios using numerical simulation include: Import the three-dimensional geological model of the landslide and the topographic data of the river channel into the Discrete Element Method (DEM) software, simulate the movement process of the landslide body under the conditions of synchronous deformation of the main landslide area and the secondary landslide area and deformation of only the main landslide area, respectively. The DEM software calculates the deposition morphology of the landslide body after it enters the river and the elevation of the top of the deposition body, and outputs the height value of the blockage.

7. The method according to claim 6, characterized in that, The analysis of parameters of the upstream landslide dammed lake specifically includes: Based on the digital elevation model data of the area to be predicted, basic data processing is performed using the software's hydrological analysis function. The direction of water flow is calculated using flow direction analysis tools, and the effective catchment area upstream is determined by combining the flow rate calculation function. By calling the surface volume function in the analysis tool, the area of ​​the inundated area and the water storage volume corresponding to different water level elevations are calculated in turn, and the water level-storage capacity curve of the area to be predicted is derived. The stability assessment of the landslide dam specifically includes: Based on the water level-reservoir capacity curve and relevant parameters of the landslide dam, a stability assessment was conducted using the CASAGLI empirical discrimination formula. The discrimination formula is as follows: ； ； Among them, H D For the maximum dam height, V L For the water storage volume, A C I represents the area of ​​the upstream backwater zone. s and I a I is the stability discrimination parameter for the landslide dam. s < -3, the landslide dam is unstable, -3 < I s <0, the stability of the landslide dam is uncertain, I s >0, the landslide dam is stable, I a >3, the landslide dam is stable, I a <3 The stability of the landslide dam is uncertain.

8. The method according to claim 7, characterized in that, The specific methods for upstream inundation assessment include: Using GIS hydrological analysis functions, the calculated height data of the landslide dammed lake is overlaid with the digital elevation model of the target watershed to extract the inundation range of the landslide dammed lake; By combining geographical location data of towns, villages, farmland and transportation arteries within the basin, the impact of inundation is statistically analyzed and a flood disaster assessment is conducted before the breach, resulting in a visualized inundation disaster distribution map.

9. The method according to claim 8, characterized in that, The downstream outburst flood disaster assessment includes: Using empirical hydraulic formulas, the maximum peak flow and flood propagation speed at the time of the landslide dam failure were preliminarily estimated. A hydrodynamic model was used to simulate the evolution of floods in downstream river channels, taking into account factors such as river roughness, tributary inflows, and levee engineering. The flood level, flow velocity, and inundation duration at different cross sections were calculated to comprehensively assess the impact risk of floods on downstream areas.

10. A disaster chain prediction system for landslide-blocked river dam failure based on landslide damming morphology, characterized in that, The method for predicting the disaster chain of a landslide-blocked dam failure based on the landslide-blocked river morphology as described in any one of claims 1-9 includes: a hazard identification module, an acquisition module, a stability calculation module, a river blockage height calculation module, and an evaluation module. The hazard identification module is used to analyze the overall surface deformation of the area to be predicted within a set time period, obtain deformation monitoring results, and obtain a time series deformation map after geocoding the deformation monitoring results. Based on the time series deformation map, landslide hazard points are found. The acquisition module is used to acquire and process landslide data at potential landslide sites, obtain key landslide parameter tables and related engineering charts, and classify the landslide into primary and secondary landslide areas based on the deformation characteristics of the landslide. The stability calculation module is used to calculate the stability calculation results based on the landslide data, and to construct landslide deformation scenarios using the stability calculation results and the landslide spatiotemporal evolution characteristics and trend analysis results. The landslide deformation scenarios include strong disturbance-triggered landslide deformation scenarios and weak disturbance-triggered landslide deformation scenarios. The blockage height calculation module is used to calculate the blockage height of the landslide dammed lake based on landslide data and landslide deformation scenarios. The evaluation module is used to analyze upstream barrier lake parameters, determine the stability of the barrier body, assess upstream inundation, and assess downstream flood disaster based on the formation of a barrier lake by a landslide blocking the river. It also completes a two-dimensional simulation of flood evolution, evaluates and analyzes the disaster before and after the flood breach based on the flood evolution results, and obtains the analysis and evaluation results.

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