CN109584510A - A kind of road landslide of high slope disaster alarm method based on valuation functions training - Google Patents
A kind of road landslide of high slope disaster alarm method based on valuation functions training Download PDFInfo
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Abstract
The present invention proposes a kind of road landslide of high slope disaster alarm method based on valuation functions training, belongs to the prediction and research field of road landslide of high slope disaster.Include: road high slope value based on digital complex demodulation data acquisition and analyzed using the passback monitoring data and topographic and geologic information for the high slope for being equipped with sensor, building fuzzy synthetic evaluation model carries out the division of landslide disaster grade；Landslide disaster valuation functions relevant to the position coordinates of high slope are established, the assessment of landslide disaster risk is carried out to the high slope for not installing sensor；Threshold value is set, when landslide risk assessment value is greater than threshold value, automatic alarm.The present invention passes through the training and study to data with existing, the geographical location of innovative consideration high slope, the limitation for overcoming side slope sensor deployment cost in real implementation process is higher and sensor monitoring information lacks etc., has great importance for the research and prediction of road landslide of high slope disaster.
Description
Technical field
The present invention relates to the predictions and research field of road landslide of high slope disaster, and in particular to one kind is based on valuation functions
Trained road landslide of high slope disaster alarm method.
Background technique
Effective monitoring means and control measure are taken to Road landslide, for prevention and harnessing landslide, avoid and reduce warp
Ji loss is of great significance (bibliography [1]).The method of landslide monitoring and technology type are more, common traditional monitoring side
Method has matter monitoring method, the earth precision measurement, GPS method etc. (bibliography [2]) on a macro scale.In recent years, with electronic technology and
The development of computer technology, remote auto remote control monitoring system are widely applied into disaster monitoring forecast system, and real-time prison is provided
Functions, the function and effect such as control and publication early warning are significant.Remote monitoring system is mainly by intelligent sensing, acquisition, emission system and intelligence
Analysis system two parts composition (bibliography [3]) can be received, is the point-to-point monitoring for specific region, does not arrange monitoring
The high slope of point is the blind area of monitoring and early warning.
So far, it has proposed and develops on digital complex demodulation (Digital Elevation Model, DEM)
The calculating mathematical model of a variety of gradient slope aspects (bibliography [4]).By the research to ground line gradient and combinations thereof rule, with
High accuracy DEM is information source, and extracting road high slope has been more mature technical method (bibliography [5]).In practical work
In Cheng Yingyong, especially elongated area road periphery in extensive range carries out point-to-point monitorings to all high slopes identified,
Manpower and material resources consumption is huge, and enforcement difficulty is high.
Common landslide evaluation model have univariate model, fuzzy mathematics method, decision-tree model, Logic Regression Models,
Supporting vector machine model and rough set model etc. (bibliography [6]).Wherein, the synthesis established on the basis of fuzzy mathematics is commented
Valence model has the characteristics that convenience is high, adaptable, can well solve multifactor, multi-level challenge, much
Scholar attempts to be used for this method in the evaluation of risk of landslip grade, but complete model of fuzzy synthetic evaluation not yet establishes (reference
Document [7]).
Bibliography:
[1] Luo Zhiqiang Slope Monitoring technology analysis [J] highway, 2002, (5): 45-48.
[2] improvement and analyzing of applying effects [J] rock mechanics and engineering of the landslide disaster remote monitoring system such as roc are opened
Journal, 2011, (10): 2026-2032.
[3] the long-range monitoring and forecasting system of He Manchao Landslide Hazards and its engineer application [J] rock mechanics and engineering
Journal, 2009, (6): 1081-1090.
[4] analysis and research [J] mapping journal of the such as Liu Xuejun based on DEM gradient slope aspect arithmetic accuracy, 2004, (8):
258-263.
[5] comparative studies [J] the soil and water conservation of the such as soup Guoan based on gradient stage division in the drawing of DEM slope map
Report, 2006, (4): 157-192.
[6] the models for hazard assessment of landslide such as Zhao Jianhua compares [J] natural calamity journal, and 2006, (2): 129-
134.
[7] individual landslides risk assessment [J] building basis of the such as Pan Xiaocheng based on Field Using Fuzzy Comprehensive Assessment, 2018,
(6):330-334.
Summary of the invention
There is complete landslide disaster for big, not yet foundation is consumed there are manpower and material resources to landslide disaster modeling evaluation at present
The problems such as Early-warning Model, the present invention, to landslide disaster divided rank, are provided a kind of based on assessment using fuzzy synthetic evaluation model
The road landslide of high slope disaster alarm method of function training.
Road landslide of high slope disaster alarm method provided by the invention based on valuation functions training, including walk as follows
It is rapid:
Step 1: being based on digital complex demodulation data, extract road high slope, obtain road slope value；
Step 2: the high slope for being equipped with sensor, recording and storage sensor passback High Slope Monitoring data with
And the topographic and geologic information of known high slope；
Step 3；Using the monitoring data and topographic and geologic information of the high slope in step 2, fuzzy comprehensive evoluation mould is constructed
Type calculates the landslide disaster grade of high slope；
Step 4: establishing landslide disaster valuation functions relevant to the position coordinates of high slope, pass through known landslide disaster etc.
The evaluation factor of the high slope of grade, training landslide disaster valuation functions；
The landslide disaster valuation functions established be expressed as E=F (h, slope, terrain, hydrology,
location)；Wherein, E represents landslide disaster grade point, and h indicates height, and slope indicates the gradient, terrain indicate geology because
Son, hydrology indicate the hydrology factor, and location indicates the location of high slope coordinate；
It is trained and learns by the sample data of the high slope of known landslide disaster grade, obtain landslide disaster assessment
Function F；
Step 5: landslide calamity being carried out to the high slope for not installing sensor using the landslide disaster valuation functions F that training obtains
The assessment of evil risk；
Step 6: according to the requirement of early warning accuracy rate, the threshold value that landslide disaster assesses risk being set, landslide risk assessment is worked as
When value is greater than threshold value, landslide early warning is carried out.
Fuzzy synthetic evaluation model described in step 3 is expressed as B=AR；Wherein, B indicates the landslide disaster of high slope
The evaluation result of grade；A indicates evaluation factor weight vector；R is fuzzy relation matrix, corresponds to each landslide for each factor of evaluation
The membership of disaster loss grade.
The present invention have compared with prior art the utility model has the advantages that
High slope information extraction is carried out using high-precision meter level digital complex demodulation data, on this basis, by right
The passback monitoring data and topographic and geologic information for being equipped with the high slope of sensor are analyzed, and fuzzy synthetic evaluation model is constructed
Carry out the division of landslide disaster grade.By the training and study to data with existing, the geographical position of innovative consideration high slope
It sets, landslide disaster valuation functions is constructed between landslide disaster grade and slope geological terrain information, finally to not installing sensing
The high slope of device carries out the assessment of landslide disaster grade and early warning, overcome in real implementation process side slope sensor deployment cost compared with
The limitation of high and sensor monitoring information missing etc., research and prediction for road landslide of high slope disaster have important
Meaning.
Detailed description of the invention
Fig. 1 is the flow chart of road landslide of high slope disaster alarm method of the invention.
Specific embodiment
Invention is further described in detail in the following with reference to the drawings and specific embodiments.
The present invention carries out high slope information extraction using digital complex demodulation data, on this basis, by installation
The passback monitoring data and topographic and geologic information for having the high slope of sensor are analyzed, and building fuzzy synthetic evaluation model carries out
The division of landslide disaster grade.The present invention innovatively considers the geography of high slope by the training and study to data with existing
Position constructs landslide disaster valuation functions between landslide disaster grade and slope geological terrain information, finally to not installing biography
The high slope of sensor carries out the assessment of landslide disaster grade and early warning, overcomes side slope sensor deployment cost in real implementation process
The limitation of higher and sensor monitoring information missing etc., research and prediction for road landslide of high slope disaster have weight
The meaning wanted.
As shown in Figure 1, for the road landslide of high slope disaster alarm method process trained the present invention is based on valuation functions, under
Face illustrates each step.
Step 1: being based on dem data, and combine through the analysis to elevation and the gradient in road buffering area, it is high to carry out road
The identification and extraction of side slope.According to DEM calculate road earth's surface on certain point (x, y) value of slope S be terrain surface function z=f (x,
Y) in thing, North and South direction elevation change rate function, expression formula are as follows:
Wherein, fxIt is east-west direction elevation change rate, fyIt is North and South direction elevation change rate.Since Grid DEM is with discrete
The form of point stores earth's surface elevation, but terrain surface and toroidal function are unknown.Therefore, DEM solves fxAnd fyUsually exist
In subrange, carried out by numerical differentiation or the method for local surface fitting.The present invention carries out slope using second differnce 2FD
Angle value calculates.
Step 2: the high slope for being equipped with sensor, High Slope Monitoring data to sensor passback and from known material
The topographic and geologic information of the high slope obtained in material data carries out data record and stores.
Step 3: using the High Slope Monitoring data and topographic and geologic information in step 2, by constructing fuzzy comprehensive evoluation
Model calculates the landslide disaster grade of the high slope.
The sensor real-time monitoring arranged in high slope and the data for returning high slope gradient, stress, displacement and water,
Data foundation is provided to the variation of real-time monitoring high slope morphological feature and strata structure etc., and the geology landform of high slope is believed
Breath include height, the gradient, prime factor, the hydrology factor etc., by being slided based on multiple-factor building fuzzy synthetic evaluation model
The division of slope disaster loss grade.Fuzzy synthetic evaluation model formula is as follows:
B=AR (2)
Wherein, B indicates evaluation result vector, is to draw the graduate degree expression of object integrated status to each commented, by A
It is synthesized under appropriate operator with R.A indicates evaluation factor weight vector, and R is fuzzy relation matrix, and expression is factor of evaluation
The membership of each each value of evaluation index corresponding grade domain in domain.Geology calamity is carried out using fuzzy synthetic evaluation model
The key of evil evaluation is the determination of A and R.In terms of the determination of factor of evaluation weight vector A, related document such as " county can be referred to
(city) Geological Hazards Investigation and zoning basic demand " weight vector suggested in detailed rules for the implementation determines (8: Zhao Zhonghai of bibliography
Beijing area research of abrupt geological hazard easily sends out zoning and Hazard degree assessment [J] resource investigation and environment, and 2009.30 (3): 213-
221.), can also using analytic hierarchy process (AHP) (AHP), (bibliography [9-11]: [9] Gao Yongli, Fei Xianjun, Ma Zhanqin is based on
Study of Hazard Evaluation [J] Chinese coal geology of ahp-Fuzzy, 2009,21 (AOI): 29-31. [10] Liu
Positive Regional Landslide mud-stone flow disaster alarm theory and methods [J] hydrogeological engineering geology is passed, 2004,31 (3): foretelling 6.
[11] Lu Daohong based on the fuzzy comprehensive evoluation of ahp Highway Geological Disaster the Sichuan hazard assessment [J] I geology journal,
2009,29 (3): 357-360.) or expert's method of discrimination obtains.Fuzzy relationship matrix r is the side by expert analysis mode at present
Method determine, due to it is this scoring be built upon to evaluation index grade domain divide on the basis of by the way of degree of membership, because
And there is bigger science and objectivity.Operator between A and R is stated between each evaluation index generally using the form multiplied
Weighted sum.
Further, according to influence the high slope height of landslide of high slope disaster, the gradient, prime factor, the hydrology factor, incline
8 gradient, stress, displacement and water evaluation indexes, establish the fuzzy judgment matrix of each index of high slope.For These parameters one
One corresponding building factor set Ai(i=1,2 ..., 8) uses the concept quantitative expression qualitative description of fuzzy mathematics, first with the gradient
For, in general, the gradient is more trembled, occur landslide disaster a possibility that it is bigger, can determine accordingly different gradient for landslide
The factor of evaluation weight vector R (A of disasteri) indicate, the correspondence landslide disaster grade domain of each evaluation index similarly can be obtained
The membership of each value.On this basis, by the influence contribution degree for each evaluation index factor of landslide disaster that adds up, i.e., above
That mentions is multiplied with A with R, is obtained the weighted sum B of each evaluation index, is exactly the landslide disaster grade of the high slope.
Step 4: by the training and study to a large amount of data with existing, in conjunction with slope and land slide disaster loss grade and side slope landform
Matter information architecture landslide disaster valuation functions.
Landslide is affected by numerous factors, and the input parameter of traditional training pattern is all very much, available
The evaluation result more accurately whether to come down, but the missing of the high slope in view of not installing sensor side slope inclination,
The data of stress, displacement and water not can be used directly in traditional training pattern.The innovative position by high slope of the invention
It sets coordinate and is added to training pattern, it is generally the case that a possibility that landslide disaster occurs for the high slope near High and dangerous slope
Also it will increase.By analyze the height h of high slope for being equipped with sensor, gradient slope, geology factor t errain, the hydrology because
The location of sub- hydrology and high slope coordinate location etc. is between topographic and geologics information and landslide disaster value-at-risk
Corresponding relationship, by being trained and learning to data with existing, building takes into account landslide disaster value-at-risk (etc. of regional location
Grade) and topographic and geologic information between landslide disaster valuation functions E=F (h, slope, terrain, hydrology,
location)。
It can get the high slope of monitoring data to sensor is equipped with, landslide disaster grade point be calculated by step 3
B, that is, E, then in conjunction with do not need monitoring obtain data evaluation factor h, slope, terrain, hydrology,
Location obtains the function F of landslide disaster assessment models to train, and the assessment models obtained in this way are unrelated with monitoring data, fits
For not installing the disaster loss grade assessment of the high slope of the not monitoring data of sensor.
Step 5: landslide disaster grade being carried out to the road high slope for not installing sensor using landslide disaster valuation functions
Assessment.
The road high slope that other are not installed with sensor is equipped with using topographic and geologic information and other of the side slope
Geographical location locating for relationship and side slope between the slope geological terrain information of sensor, utilizes the landslide disaster of acquisition
Valuation functions carry out the assessment of landslide disaster value-at-risk (grade) to the high slope for not installing sensor.It is high that road is inputted when assessment
Parameter h, slope, terrain, hydrology, the location of side slope, output obtain landslide disaster evaluation grade E.
It, can be by being equipped with the real-time monitoring of the high slope of sensor to the landslide disaster valuation functions F obtained has been trained
Data and geology terrain data carry out function amendment, so that valuation functions are more accurate.
Step 6: according to the requirement of early warning accuracy rate, adjusting the threshold value of landslide disaster assessment risk, carry out landslide early warning.One
As for, accuracy rate require it is higher, threshold value can be also arranged bigger, carry out early warning high slope occur landslide disaster risk
It is higher.
Embodiment:
By Hangzhou, Zhejiang province city go up a hill High Slope on Expressway landslide disaster grade assessment for.
The first step is the analysis by dem data to elevation and the gradient in road buffering area, carries out highway height of going up a hill
The identification and extraction of side slope；
Second step is to identify the high slope gone up a hill on highway that extracts according to high score image, to partially having installed
There is the high slope of the monitoring sensor such as dipmeter, stress meter, displacement meter and rainfall gauge to be monitored, sensor is returned in real time
Monitoring data, including side slope inclination, stress value, shift value and water value etc., and the side slope obtained according to existing material
Matter terrain information, including height, the gradient, prime factor, the hydrology factor etc. integrated and recorded；
Third step is the monitoring data and topographic and geologic information according to high slope in step 2, constructs fuzzy comprehensive evoluation mould
Type B=AR, wherein B indicates evaluation result vector, is to draw the graduate degree expression of object integrated status to each commented,
It is synthesized under appropriate operator by A and R.A indicates evaluation factor weight vector, and R is fuzzy relation matrix, expression be evaluation because
The membership of each each value of evaluation index corresponding grade domain in plain domain.Operator between A and R is generally using the shape multiplied
Formula states the weighted sum between each evaluation index；
4th step be by analyze the height of high slope for being equipped with sensor, the gradient, the ground such as prime factor, the hydrology factor
Corresponding relationship between the location of shape geological information and high slope coordinate and landslide disaster grade, by data with existing into
Row training and study, building take into account the assessment of the landslide disaster between the landslide disaster grade of regional location and topographic and geologic information
Function E=F (h, slope, terrain, hydrology, location)；
5th step is that other are not installed with the road high slope of sensor, first with the topographic and geologic information of the side slope
Geographical location locating for relationship and side slope between slope geological terrain information of sensor is installed with other, to landslide
Disaster Assessment function E carries out function amendment, finally carries out landslide disaster etc. to the high slope for not installing sensor using the function
The assessment of grade.
Finally, according to the requirement of early warning accuracy rate, the threshold value of adjustment landslide disaster assessment risk carries out landslide early warning.One
As for, accuracy rate require it is higher, threshold value can be also arranged bigger, carry out early warning high slope occur landslide disaster risk
It is higher.
Claims (4)
1. a kind of road landslide of high slope disaster alarm method based on valuation functions training characterized by comprising
Step 1 is based on digital complex demodulation data, extracts road high slope, obtains road slope value；
Step 2, the high slope for being equipped with sensor, the High Slope Monitoring data and of recording and storage sensor passback
The topographic and geologic information for the high slope known；
Step 3, monitoring data and topographic and geologic information using the high slope in step 2 construct fuzzy synthetic evaluation model, meter
Calculate the landslide disaster grade of high slope；
The fuzzy synthetic evaluation model is expressed as B=AR；Wherein, B indicates the judge of the landslide disaster grade of high slope
As a result；A indicates evaluation factor weight vector；R is fuzzy relation matrix, corresponds to each landslide disaster grade for each factor of evaluation
Membership；
Step 4 establishes landslide disaster valuation functions relevant to the position coordinates of high slope, passes through known landslide disaster grade
The evaluation factor of high slope, training landslide disaster valuation functions；
The landslide disaster valuation functions established are expressed as E=F (h, slope, terrain, hydrology, location)；Its
In, E is landslide disaster grade point, and h indicates height, and slope indicates the gradient, and terrain indicates ground prime factor, hydrology table
Show the hydrology factor, location indicates the location of high slope coordinate；Pass through the sample of the high slope of known landslide disaster grade
Notebook data is trained and learns, and obtains landslide disaster valuation functions F；
Step 5, the assessment for carrying out landslide disaster grade to the high slope for not installing sensor using landslide disaster valuation functions F；
Step 6, according to the requirement of early warning accuracy rate, the threshold value that landslide disaster assesses risk is set, when landslide risk assessment value is big
When threshold value, landslide early warning is carried out.
2. the method according to claim 1, wherein being calculated in road earth's surface in the step 1 according to DEM
By terrain surface function z=f (x, y), the elevation change rate in thing, North and South direction obtains the value of slope S, S of certain point (x, y), such as
Under:
Wherein, fxIt is east-west direction elevation change rate, fyIt is North and South direction elevation change rate.
3. the method according to claim 1, wherein in the step 3, the fuzzy synthetic evaluation model
It is expressed as B=AR；Wherein, B indicates the evaluation result of the landslide disaster grade of high slope；A indicates evaluation factor weight vector；R
For fuzzy relation matrix, the membership of each landslide disaster grade is corresponded to for each factor of evaluation.
4. method according to claim 1 or 3, which is characterized in that in the step 3, in fuzzy synthetic evaluation model
8 evaluation factors comprising high slope: height, the gradient, prime factor, the hydrology factor, gradient, stress, displacement and water.
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