CN113283802B - Landslide risk assessment method for complicated and difficult mountain areas - Google Patents
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Abstract
The invention discloses a landslide risk assessment method in a complicated difficult mountain area, which comprises the steps of acquiring landslide geological phenomenon distribution conditions of an assessment area through medium-high resolution remote sensing interpretation and on-site regulation and drawing, comprehensively analyzing influence factors, selecting an assessment model influence factor, and constructing a grid of each factor layer; then, a probability statistical model is applied to calculate the conditional probability of each influence factor on landslide action; and finally, synthesizing the conditional probabilities of all index factors based on grid geospatial superposition of all factor layers, and carrying out prediction accuracy test on landslide risk assessment results by using a prediction accuracy curve. According to the invention, index factors of influence factors in all aspects are synthesized through the combination of a geospatial information technology and a probability statistical model, the occurrence probability of landslide geological action is calculated, the danger degree of landslide geological disaster in a complicated and difficult mountain area can be intuitively reflected, and a scientific basis is provided for bypassing the landslide disaster hidden danger or optimizing an engineering construction scheme.
Description
Technical Field
The invention relates to the field of geological disaster prevention and control, in particular to a complex difficult mountain landslide risk assessment method based on a geospatial information technology and a probability statistical model.
Background
Landslide refers to the action and phenomenon that a certain part of rock and soil on a mountain slope generates shearing displacement along a certain weak structural surface (belt) under the action of gravity (including the gravity of the rock and soil and the dynamic and static pressure of underground water) so as to integrally move to the lower side of the slope. Landslide not only threatens the nearby ergonomic activities, but may even cause injury and death of persons and property loss within a certain range. Therefore, how to timely and effectively discover and master the landslide condition of potential landslide in advance, predict and evaluate the landslide hazard, prevent and treat landslide geological disasters, and are particularly important for mountain area engineering construction activities and infrastructure operation and maintenance. The risk evaluation of landslide disasters is comprehensive analysis of the possibility and the risk degree of landslide disasters in the area. At present, the existing domestic and foreign regional landslide risk assessment method or model mainly comprises the following steps: (1) Expert system analysis methods typically evaluate landslide susceptibility and risk based on expert judgment. (2) Statistical or probabilistic analysis methods are based on statistical analysis of various influencing factors and landslide distribution. The evaluation mode for carrying out statistical analysis on the correlation between the influence factors and the landslide ensures the objectivity of the vulnerability or the danger partition to a great extent. The limitation of the method is mainly limited by the quality of data, such as incomplete data record, insufficient data precision and the like. (3) Methods based on classical slope stability theory, such as infinite slope analysis, limit balancing, finite element techniques, etc. However, these existing models or modified models have limited applicability, and relatively have insufficient applicability.
In recent years, geospatial information technology and a geographic information model have become important support technologies for geological disaster research and analysis. The method combines the advantages of the developed model, fully plays the comprehensive technical advantages of the geospatial information technology, can comprehensively consider various factors, not only considers the combined action of various factors such as topography, geology, soil, vegetation, hydrology, climate and the like, but also can solve the problem that various parameters are uncertain (probability) in a larger area, so that the method has feasibility in rapid evaluation in a larger similar area, can be applied in different geographic geological environments, and has certain universality to be a landslide hazard evaluation application requirement. The invention provides a complex difficult mountain landslide risk assessment method based on a geospatial information technology and a probability statistical model to solve the problems.
Disclosure of Invention
The invention aims to provide a complex difficult landslide risk assessment method based on a geospatial information technology and a probability statistical model, and in order to achieve the purposes, the technical scheme adopted by the invention is as follows, and mainly comprises the following steps:
and A, acquiring landslide geological phenomenon distribution conditions of the investigation region through remote sensing visual interpretation and on-site investigation, and establishing a landslide risk assessment space database of the investigation region by combining mountain live-action three-dimensional digital real environment, geological cloud 3.0 resource and geographical national condition monitoring data.
The remote sensing image can provide surface information of a large area, is an important data source for carrying out difficult and complicated mountain area investigation, and plays a great role in geological disaster investigation. The remote sensing image can be visually interpreted to determine various adverse geological phenomena (such as fracture zones, collapse, landslide, mud-rock flow) and spatial range distribution in the research area. Typically, interpretation of undesirable geological phenomena typically requires remote sensing image data with spatial resolution better than 5m. The landslide phenomenon presents special shape, tone, texture and other characteristics on the image, and has obvious interpretation marks. In addition, under the three-dimensional visualization environment, the ground surface features are clearer and more distinguishable than the ground surface features which are only used for a two-dimensional remote sensing image. Therefore, inoculation features and scale forms of landslide geological disasters can be interpreted and defined on a high-resolution remote sensing image. The AOI (area of interest) functional module of the remote sensing image processing software is used for realizing digital sketching and interpretation of the spatial position of the geological phenomenon of the landslide of the remote sensing image, and storing attribute data such as the distribution range, the area, the perimeter and the like of the output vector. And superposing the landslide vector data which are interpreted and identified in the three-dimensional terrain model to construct a three-dimensional geographic environment model of the vector grid integrated investigation region.
In the investigation region, other acquired non-remote sensing geological information in the forms of characters, figures, tables and the like are not digitized and cannot be directly used in a geographic information system, and the data are required to be digitized and normalized and used as the supplement of the digitized remote sensing geological information to jointly form the digitized geological information. The detailed distribution condition (such as geologic age, geologic lithology, fault characteristics, rock layer thickness and the like) of rock layers at a drilling point can be directly obtained through engineering drilling and sampling, and the method is an important data source for carrying out rock layer simulation analysis and three-dimensional stratum modeling. The national geological big data sharing service platform 'geological cloud 3.0' developed by the China geological survey bureau, and the data resources and the system on the cloud construct 12 types of archive databases and 8 types of geological information product data, so that data support is provided for geological disaster early warning evaluation, quick acquisition of ground disaster information, three-dimensional fine modeling, efficient simulation and multi-parameter self-adaptive early warning.
B, comprehensively analyzing landslide formation conditions and disaster-causing factors of the complicated and difficult mountain areas, determining evaluation model factors (such as lithology, gradient, slope direction, rainfall, valley distribution and the like), and constructing a grid of each factor layer of the evaluation model.
The inoculation of the geological disaster is affected by various factors to different degrees, so that in the corridor geological risk evaluation model, the specific situation of a research area is combined, key factors affecting the occurrence of the geological disaster are reasonably grasped, and meanwhile, the characteristics that the evaluation factors have spatial distribution characteristics and can be digitally patterned are considered. In general, the occurrence of geological disasters is mainly affected by factors such as topography, geological conditions, land types, seismic influences, atmospheric precipitation, engineering activities and the like.
And C, calculating the conditional probability of each evaluation model factor on landslide action by using a probability statistical model.
The probability statistical model is widely applied to the spatial prediction of geological environment, and is a mathematical prediction model which is established on the basis of estimating the contribution degree of landslide risk according to the existing or known disaster development conditions. The accuracy of the probability statistical model prediction is closely related to the development status of geological disasters and the selection of index evaluation factors. The method comprises the steps of comprehensively analyzing geological conditions and pregnancy and disaster factors in a research area, classifying factors with larger influence on pregnancy and disaster as index factors for evaluating geological hazards of the area, calculating probability of occurrence of the geological hazards under the influence of single index factors by using a probability statistical model, combining and superposing the probability of the single index factors to measure the degree of the geological hazards, expanding the probability to surrounding areas according to an analogy principle, and evaluating the geological hazards of the whole area and predicting the ground hazards, namely evaluating the relation between the geological hazards and the occurrence of bad geological hazards by using the influence degree.
The model construction process comprises the following steps: suppose that the poor geology Y is affected by n-type factors, i.e., y=f (x 1 ,x 2 ,x 3 ,L,x n ) Whether an adverse geological disaster occurs is related to the amount and quality of information in the adverse geological evaluation. The probability statistical model is conditional probability operation, and sample frequency is adopted to replace conditional probability to calculate the contribution rate of single factors to geological disaster occurrence during actual operation, and the specific model establishment process is as follows:
separately calculating each factor x i Contribution values I (Y, x) provided to poor geological disaster Y i ) There is
Wherein: i (Y, x) 1 ,x 1 ,L,x n ) -combining factor x 1 ,x 2 ,x 3 ,L,x n A contribution value to the poor geology;
P(Y,x 1 ,x 1 ,L,x n )——x 1 ,x 2 ,x 3 ,L,x n probability of occurrence of poor geology under the condition of combined factors;
p (Y) -probability of occurrence of undesirable geological disasters.
The conditional probability (1) expression can be written as:wherein: i x1 (Y,x 2 ) Factor x 1 Factor x when present 2 Contribution to poor geology.
Set factor x i The contribution value of the poor geological disaster occurrence event (L) is I (L, X) i ) Calculating sampling frequency:
wherein: n (N) i Distributed in factor x i The number of bad geological units of the category;
n, the total number of poor geological units of landslide;
S i landslide of mountain area containing x i The number of units of the evaluation factor;
s, total landslide evaluation units.
Calculating the contribution value of each index factor of the evaluation unit
Wherein: i-the sum of the contribution values of the evaluation units;
n-the number of influencing factors.
And D, synthesizing the conditional probability of each single index factor based on the geographic space superposition, generally measuring the landslide hazard degree, expanding the landslide hazard degree to the whole region to be tested according to the analogy principle, and realizing the landslide geological disaster hazard risk evaluation of the whole research region.
In the geospatial stacking operation process utilizing the geospatial information technology, research scales, namely the grid size of the evaluation unit, are reasonably selected, and the accuracy of geospatial data and the accuracy of the geological risk evaluation result are significantly influenced. The formula for selecting the grid unit size is as follows:
wherein: g s -a suitable cell size; s-map scale reciprocal.
And respectively carrying out statistics on the total number of the units of the research area according to the classification by using a geospatial information technology, and carrying out spatial analysis on the single evaluation factor sub-layer and the poor geological area in the geospatial information technology to obtain the number of the units of the poor geological area in each single factor class. And analyzing the spatial topological relation between the poor geological disaster area range and the single index factors, researching the distribution rule of the poor geological disaster area range, obtaining the probability of occurrence of geological disasters under the condition of the single index factors according to the probability statistical model, and superposing, combining and analyzing the disaster occurrence probability in the prediction area by each factor.
And E, carrying out superposition analysis on the known landslide disaster distribution and the risk prediction result diagram, and carrying out statistical analysis on the risk level partition, the corresponding geological disaster units and the evaluation results. Meanwhile, the landslide hazard evaluation result is checked by utilizing a prediction accuracy curve.
And assigning the influence degree value of each evaluation factor category to a corresponding layer, constructing an evaluation factor grid layer containing influence degree attributes, and carrying out space superposition analysis on each factor grid layer to obtain a total influence degree value.
The reliability of the analysis result of the geological disaster risk has direct influence on disaster risk division and risk assessment, and the evaluation result of the geological disaster needs to be verified. The prediction accuracy curve is a comprehensive index reflecting sensitivity and specificity continuous variables, and is widely applied to verification of geological disaster risk analysis results. The horizontal axis of the prediction accuracy rate curve represents the area percentage accumulation amount of the dangerous from high to low in the analysis result, the vertical axis represents the percentage accumulation amount occupied by the geological disaster point in the corresponding dangerous index range, the area under the prediction accuracy rate curve represents the result precision, and the larger the occupied area is, the better the result is indicated.
Specifically, comprehensive analysis is performed on geological conditions and pregnancy and disaster factors in a research area, and factors with larger or obvious influence on the pregnancy and disaster are listed as model factors for landslide risk evaluation of the area.
Specifically, the probability statistical model is a mathematical prediction model which is established on the basis of estimating the influence degree of the existing or known disaster development conditions on landslide hazard.
Specifically, the analogy principle refers to the evaluation of the relationship between the geological influence factor and the occurrence of the bad geological disaster by using the magnitude of the influence degree. And expanding the same relationship to the surrounding area to realize the geological disaster risk evaluation of the whole investigation area.
Further, the spatial topological relation between the poor geological disaster area range and the evaluation factors is analyzed, the distribution rule is researched, the conditional probability of each factor on landslide geological disaster is calculated according to the probability statistical model, and the occurrence probability of landslide disaster in the prediction area is analyzed by superposition and combination of each factor.
Further, in the geospatial information technology, each factor is respectively counted in the total number of units of the research area according to classification, each factor layer is respectively subjected to spatial analysis with the known landslide distribution data area in the geospatial information technology, and the number of units of the landslide geological area in each factor type can be obtained. If the influence degree of a certain factor class is calculated to be minus infinity, the area is extremely unlikely to generate landslide hazard.
Specifically, the influence degree value of each evaluation factor category is assigned to a corresponding layer, an evaluation factor grid layer containing influence degree attributes is constructed, and space superposition analysis is carried out on each factor grid layer to obtain a total influence degree value. When the calculated result value is smaller than 0, the region is not easy to generate geological disasters, and when the calculated result value is larger than 0, the probability that landslide is generated and the larger the value is, the larger the probability that geological disasters are generated is.
Specifically, the regions are classified according to the influence degree values into extremely low dangerous regions (-8.57 to-2), low dangerous regions (-2 to 0), medium dangerous regions (0 to 1), high dangerous regions (1 to 3) and extremely high dangerous regions (3 to 4.96).
Further, stacking analysis is carried out on known landslide disaster distribution and a dangerous prediction result graph, statistics is carried out on a dangerous grade area and a corresponding geological disaster unit, statistical inspection is carried out on an evaluation result, and a prediction accuracy rate curve is adopted to verify the landslide geological disaster dangerous analysis result. The horizontal axis of the prediction accuracy rate curve represents the area percentage accumulation amount of the dangerous from high to low in the analysis result, the vertical axis represents the percentage accumulation amount occupied by the geological disaster point in the corresponding dangerous index range, the area under the prediction accuracy rate curve represents the result precision, and the larger the occupied area is, the better the result is indicated.
Compared with the prior art, the invention has the following beneficial effects: according to the method, probability of occurrence of landslide geological disasters is comprehensively calculated by combining the geospatial information technology and the mathematical statistics method probability model, and probability of occurrence of the landslide geological disasters is comprehensively calculated by combining the probability of the plurality of influence index factors, so that dangerous situations of the landslide geological disasters in a complicated and difficult mountain area research area are visually reflected.
Drawings
FIG. 1 is a flow diagram of a complex mountain landslide geological disaster risk assessment method;
FIG. 2 is a classification chart of landslide risk evaluation influence factors in a mountain area;
fig. 3 is a graph showing the result of evaluation of landslide risk in a certain mountain area.
Detailed Description
The invention will be further illustrated by the following description and examples, which include but are not limited to the following examples.
A complex difficult mountain area in the southwest of China is selected for landslide risk assessment, and fig. 1 is a flow diagram of a complex landslide geological disaster risk assessment method. The main execution flow is as follows:
and A, acquiring landslide geological phenomenon distribution conditions of the investigation region through remote sensing visual interpretation and on-site investigation, and establishing a landslide risk assessment space database of the investigation region by combining mountain live-action three-dimensional digital real environment, geological cloud 3.0 resource and geographical national condition monitoring data.
In the embodiment, the remote sensing image with the spatial resolution of 4.33m is adopted, so that the accuracy requirement of interpretation is met. Meanwhile, non-remote sensing geological information such as other graphic data in a research area is acquired through geological cloud 3.0, so that a plurality of geological information data are synthesized to carry out subsequent evaluation work. In this embodiment, 14 drilling point data in the research area are acquired through "geological cloud 3.0". The high-spatial-resolution remote sensing image is combined with the digital elevation model DEM of the region to construct a mountain region digital three-dimensional real environment, a AOI (area of interest) functional module of remote sensing image processing software is applied to realize digital sketching interpretation of the spatial position of the geological phenomenon of the remote sensing image landslide, and attribute data such as the distribution range, the area, the perimeter and the like of an output vector are stored. And superposing the landslide vector data of interpretation and identification in the three-dimensional terrain model to construct a three-dimensional geographic environment model of the vector-grid integrated research area.
B, comprehensively analyzing landslide formation conditions and disaster-causing factors of the complicated and difficult mountain areas, determining evaluation model factors (such as lithology, gradient, slope direction, rainfall, valley distribution and the like), and constructing a grid of each factor layer of the evaluation model.
Comprehensively analyzing and combining the landslide specific conditions, selecting seven evaluation factors including gradient, slope direction, road factors, engineering rock groups, water system factors, land types and precipitation, classifying each evaluation factor in a grading manner, and fig. 2 is a grading classification chart of landslide risk evaluation influence factors in a certain mountain area.
(1) Gradient of slope
The slope characterizes the degree of steepness of the surface of the terrain. The gradient represents potential energy of slope geological disasters, and meanwhile, the gradient is closely related to the stress state inside the slope. In general, when the gradient is between 10 ° and 45 °, an advantage is created for the occurrence of geological disasters (especially landslides). And carrying out surface analysis on the DEM with the resolution of 30m in the landslide area to obtain a gradient map, wherein the maximum gradient of the research area reaches 50.85 degrees, and the average gradient is 10.32 degrees. The gradient interval distribution rule is combined, and the gradient interval distribution rule is divided into six grades, namely 0-5 degrees, 5-10 degrees, 10-15 degrees, 15-20 degrees, 20-30 degrees and >30 degrees, as shown in fig. 2 (a).
(2) Slope direction
The projection direction of the slope normal on the horizontal plane is called the slope direction. Due to the comprehensive effects of natural conditions such as ground surface temperature, sunlight intensity, atmospheric precipitation, air temperature change, crust movement and the like, different slopes can influence the ground surface to different degrees, so that the surrounding ecological environment is changed. The difference of microclimate and hydrothermal ratio can be brought to different slope directions, so that the difference exists between the front and the back of the hillside, and particularly, the difference is obvious in the mountain area, and the condition of the sunny slope is better than that of the cloudy slope. Slope analysis was performed using a 30m resolution DEM of the investigation region, dividing the slope into 9 categories, plane, north, northeast, east, southeast, south, southwest, west, northwest, as shown in fig. 2 (b).
(3) Road factor
The human engineering activities change the surrounding geographical environment to a great extent, and when large infrastructure projects such as railways, highways and the like are constructed, the problems of filling and supporting of high steep slopes are involved. The implementation of engineering will cause the redistribution of the stress state of the side slope body, and the rock body can be loosened and collapsed, if the supporting structure facilities are unreasonable, bad geological disasters are extremely easy to occur, and the road plays an important role in the occurrence of the geological disasters as an influence factor. The road information is vectorized and extracted through remote sensing images, buffer area analysis is carried out in four stages at intervals of 500m, and a road factor layer is built, as shown in fig. 2 (c).
(4) Engineering rock group
Engineering geology of the rock stratum is closely related to original rock lithology, rock mass structure and hardness degree, and is also influenced by later stage fold weathering. When the rock mass has high mechanical strength and good integrity, the probability of occurrence of geological disasters is relatively small. The formation lithology of landslide area is converted into engineering rock group according to the degree of softness and hardness, and the engineering rock group is divided into soft and hard inter-phase rock, hard rock, loose sediment and soft rock, as shown in fig. 2 (d).
(5) Water system factor
The erosion and scouring action of the river can cause the side slope on the periphery of the water system to become larger, and landslide disasters can occur when the side slope reaches a critical state to enable the sliding control surface to reach an exposed or cut-out state. In addition, the river can generate dynamic water pressure and interstitial water pressure through the action of groundwater, soften the soil body and reduce the stratum strength. The extracted river water system vector layer is subjected to 6-level buffer analysis at equal intervals of 500m based on the geospatial information technology to obtain a water system factor graph, as shown in fig. 2 (e).
(6) Land type
Along with the continuous expansion of the range of human production and life, unreasonable land development and utilization modes and extensive operation modes accelerate the degradation of geological environment, so that the vegetation in partial areas is rare and even the ground surface is exposed, the phenomenon of extremely reduced water and soil conservation capability occurs, and the occurrence probability of local geological disasters is greatly increased. The land coverage type is comprehensively analyzed, and the land utilization of a research area is divided into six land types of water bodies, woodlands, barren lands, grasslands, construction lands and cultivated lands, as shown in fig. 2 (f).
(7) Precipitation amount
Precipitation is closely related to the formation of geological disasters, and is a main cause of the formation of numerous geological disasters. If the water and soil conservation capability is weak, a large amount of precipitation can soften the rock and soil mass and reduce the resistance of the rock and soil mass. The size, strength, duration and the like of the precipitation affect the formation of geological disasters, and particularly, the strong rainfall in a short time is extremely easy to induce the geological disasters, so that the precipitation is closely related to the occurrence of the geological disasters. The corridor zone area is divided according to the equal precipitation line according to the annual average precipitation of the Longyan city, as shown in fig. 2 (g).
And C, calculating the conditional probability of each evaluation model factor on landslide action by using a probability statistical model.
And D, synthesizing the conditional probability of each single index factor based on the geographic space superposition, generally measuring the landslide hazard degree, expanding the landslide hazard degree to the whole region to be tested according to the analogy principle, and realizing the landslide geological disaster hazard risk evaluation of the whole research region.
The example refers to a topography scale of 1:50000, so the size of the evaluation unit grid can be calculated from equation (5), i.e. the suitable grid size is 32.8525m× 32.8525m. In order to facilitate calculation, the basic geographic data set of each factor of landslide geological disasters is subjected to rasterization by using a geographic space information technology, and the sizes of grid units are uniformly determined to be 30m multiplied by 30m.
In the classification of 2500-3000 m of water system factor and the classification of water body of land type factor, the statistical result of the number of geological disaster units is 0, the contribution value of the classification of the evaluation factor is- -infinity according to the formula (3), the contribution value of the region is still- -infinity according to the formula (4), the region is extremely unlikely to generate geological disaster, the result is neglected by the special single factor condition, and the effect of other factors is different from the actual effect, so that the contribution value of the classification of the other factors is referred to, and the contribution value of the water system factor 2500-3000 m and the contribution value of the water body of the land type factor is set to be-2.5. The statistics of each evaluation factor unit are shown in Table 1. The contribution value of each factor category can be calculated according to the formula (3), and the calculation result is shown in table 2.
Table 1 number of evaluation factor units
Table 2 contribution values of evaluation factors
And E, carrying out superposition analysis on the known landslide disaster distribution and the risk prediction result diagram, and carrying out statistical analysis on the risk level partition, the corresponding geological disaster units and the evaluation results. Meanwhile, the landslide hazard evaluation result is checked by utilizing a prediction accuracy curve.
And assigning the influence degree value of each evaluation factor category to a corresponding layer, constructing an evaluation factor grid layer containing influence degree attributes, and carrying out space superposition analysis on each factor grid layer to obtain a total influence degree value. The analysis result shows that the contribution value in the research area is-8.57-4.96, when the contribution value is smaller than 0, the area is not easy to generate geological disasters, and when the contribution value is larger than 0, the geological disasters are possibly generated, and the larger the contribution value is, the larger the probability of the geological disasters is. And (3) classifying the regions according to the contribution values, wherein the regions are extremely low dangerous areas (-8.57 to-2), low dangerous areas (-2 to 0), medium dangerous areas (0 to 1), high dangerous areas (1 to 3) and extremely high dangerous areas (3 to 4.96). Fig. 3 is a graph showing the result of evaluation of landslide risk in a certain mountain area.
And (3) carrying out superposition analysis on the known geological disasters and the geological risk level layers, carrying out statistics on the geological risk level areas and corresponding geological disaster units, and carrying out statistical verification on evaluation prediction results, wherein the results are shown in Table 3.
TABLE 3 statistical results of risk rating
In the research area, the dangerous area and the dangerous area above the dangerous area in the prediction result account for 28.42 percent; in known bad geological disasters, dangerous areas distributed in and above the middle dangerous area account for 88.57%; the total area of the extremely high risk area is 5.84%, while the total number of known geological disaster units is 56%. The prediction results accord with the geological dangerous condition of the research area to a certain extent. And (3) performing curve fitting on the data to obtain a prediction accuracy curve, wherein the area ratio of the lower part of the curve can be calculated to be 0.879 from the curve graph, which shows that the result of model simulation evaluation has higher accuracy.
Further, the risk assessment result of the implementation area shows that the influence factors which are easier to induce the geological disaster of the corridor area are as follows: (1) terrain slope >30 °; (2) the terrain slope is in a south-right direction; (3) distance from road 0-500 m; (4) The wind resistance is low, and the thickness of the weathered layer is changed greatly; (5) a distance from the water system of 0 to 500m; (6) the land use type is grassland; (7) areas with more abundant precipitation.
The embodiment shows that the landslide geological disaster risk evaluation method constructed based on the geospatial information technology and the probability statistical model has higher accuracy of evaluation results and better universality in landslide evaluation application, can intuitively reflect the risk of landslide geological disasters in complicated and difficult mountains, and can provide scientific basis for the safety control of geological disasters in mountain engineering facilities, and avoid landslide disaster hidden danger or optimize engineering construction schemes. The method has practical value for guiding engineering construction (such as line engineering, highways, railways, pipelines, power lines and the like) in complicated and difficult mountain areas.
The above embodiment is only one of the preferred embodiments of the present invention, and should not be used to limit the scope of the present invention, but all the insubstantial modifications or color changes made in the main design concept and spirit of the present invention are still consistent with the present invention, and all the technical problems to be solved are included in the scope of the present invention.
Claims (6)
1. The landslide risk assessment method for the complicated and difficult mountain areas is characterized by comprising the following steps of:
the method comprises the steps of A, obtaining landslide geological phenomenon distribution conditions of a survey area through remote sensing visual interpretation and on-site investigation, and establishing a landslide risk assessment spatial database of the survey area by combining mountain live-action three-dimensional digital real environments, geological cloud 3.0 resources of geological survey bureau and geographical national condition monitoring data;
b, comprehensively analyzing landslide formation conditions and disaster-pregnant factors of the complicated and difficult mountain areas, determining evaluation model factors, wherein the evaluation model factors comprise lithology, gradient, slope direction, rainfall and valley distribution, and constructing a grid of each factor layer of the evaluation model;
c, calculating the conditional probability of each evaluation model factor on landslide action by using a probability statistical model;
the probability statistical model is a mathematical prediction model which is established on the basis of estimating the influence degree of the existing or known disaster development conditions on landslide hazard;
analyzing the space topological relation between the poor geological disaster area range and the evaluation factors, researching the distribution rule of the poor geological disaster area range, calculating the conditional probability of each factor on landslide geological disaster occurrence according to a probability statistical model, and superposing, combining and analyzing each factor to predict the landslide disaster occurrence probability in the area;
the probability statistical model construction process comprises the following steps: assuming poor geologyIs subject to->Influence of class factors, i.e.) Whether an adverse geological disaster occurs is related to the quantity and quality of information in the adverse geological evaluation; the probability statistical model is conditional probability operation, and sample frequency is adopted to replace conditional probability to calculate the contribution rate of single factors to geological disaster occurrence during actual operation, and the specific model establishment process is as follows:
separately calculating each factorProviding bad quality disaster +.>Tribute value of->) The method comprises the following steps:
（1）
wherein:-combination of factors->A contribution value to the poor geology;
——/>probability of occurrence of poor geology under the condition of combined factors;
-probability of occurrence of bad geological disasters;
the conditional probability (1) expression can be written as:
（2）
wherein:-factor (s)/(herb)>Factor->A contribution value to the poor geology;
setting factorsIncident on adverse geological disaster->The contribution value of +.>Calculating the sampling sample frequency:
（3）
wherein:distributed in the aspect of factor->The number of bad geological units of the category;
-total number of landslide poor geological units in the mountain area;
-landslide of mountain area contains +.>The number of units of the evaluation factor;
-total number of landslide evaluation units;
calculating the contribution value of each index factor of the evaluation unit（4）
Wherein:-evaluating the sum of the unit contribution values;
n-the number of influencing factors;
the conditional probability of each evaluation model factor is integrated on a grid-by-grid basis on the basis of geographic space superposition, the landslide hazard degree of each grid unit area is generally measured, and the landslide geological disaster hazard risk evaluation of the whole investigation area is realized according to an analogy principle;
respectively carrying out statistics on the total number of units of a research area according to classification in a geospatial information technology, respectively carrying out spatial analysis on each factor layer and a known landslide distribution data area in the geospatial information technology to obtain the number of units of a landslide geological area in each factor class, and if the influence degree of a certain factor class is calculated to be minus infinity, indicating that landslide disasters are extremely unlikely to occur in the area;
the grid size of the evaluation unit has important influence on the accuracy of the geospatial data and the accuracy of landslide risk evaluation results; the formula for selecting the grid unit size is as follows:
；
wherein:-a suitable cell size; />-map scale reciprocal;
and E, carrying out superposition analysis on the known landslide disaster distribution and the dangerous prediction result map, carrying out statistical analysis on the dangerous grade partition and the corresponding geological disaster unit and evaluation result, and simultaneously, utilizing a prediction accuracy curve to test the landslide dangerous evaluation result.
2. The method for evaluating the landslide risk of a complicated and difficult mountain area according to claim 1, wherein the geological conditions and the pregnancy and disaster factors in the research area are comprehensively analyzed, and the factors which have obvious influence on the pregnancy and disaster are listed as model factors for evaluating the landslide risk of the area.
3. The method for evaluating the landslide risk of the complicated and difficult mountain area according to claim 1, wherein the analogy principle is to evaluate the relation between the geological influence factors and the occurrence of bad geological disasters by using the influence degree, and expand the relation to the surrounding area by using the same relation so as to evaluate the geological disaster risk of the whole investigation area.
4. The method for evaluating landslide risk in complex difficult mountainous areas according to claim 1, wherein the influence degree value of each evaluation factor category is assigned to a corresponding layer, an evaluation factor grid layer containing influence degree attributes is constructed, and space superposition analysis is carried out on grids of each factor layer to obtain a total influence degree value; when the calculated result value is smaller than 0, the region is not easy to generate geological disasters, and when the calculated result value is larger than 0, the probability that landslide is generated and the larger the value is, the larger the probability that geological disasters are generated is.
5. The method for evaluating the landslide risk of a complicated difficultly-restricted mountain area according to claim 1, wherein the areas are classified into extremely low risk areas, medium risk areas, high risk areas, and extremely high risk areas according to the influence degree value.
6. The method for evaluating the landslide risk of a complicated and difficult mountain area according to claim 1, wherein the known landslide disaster distribution and a risk prediction result diagram are subjected to superposition analysis, the risk level area and the corresponding geological disaster units are counted, the evaluation result is subjected to statistic inspection, and the landslide geological disaster risk analysis result is verified by adopting a prediction accuracy curve; the horizontal axis of the prediction accuracy rate curve represents the area percentage accumulation amount of the dangerous from high to low in the analysis result, the vertical axis represents the percentage accumulation amount occupied by the geological disaster point in the corresponding dangerous index range, the area under the prediction accuracy rate curve represents the result precision, and the larger the occupied area is, the better the result is indicated.
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