CN109584510B - Road high slope landslide hazard early warning method based on evaluation function training - Google Patents
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Abstract
The invention provides a road high slope landslide hazard early warning method based on evaluation function training, and belongs to the field of prediction and research of road high slope landslide hazard. The method comprises the following steps: the method comprises the steps that road high slope values obtained based on digital elevation model DEM data are analyzed by utilizing returned monitoring data of a high slope with a sensor and topographic and geological information, and a fuzzy comprehensive evaluation model is constructed to divide landslide disaster grades; establishing a landslide disaster evaluation function related to the position coordinates of the high slope, and evaluating the landslide disaster risk of the high slope without a sensor; and setting a threshold value, and when the landslide risk assessment value is larger than the threshold value, automatically alarming. The method provided by the invention creatively considers the geographical position of the high slope by training and learning the existing data, overcomes the limitations of high cost of slope sensor layout, sensor monitoring information loss and the like in the practical implementation process, and has important significance for research and prediction of road high slope landslide disasters.
Description
Technical Field
The invention relates to the field of prediction and research of landslide disasters of high slopes of roads, in particular to a warning method for landslide disasters of high slopes of roads based on evaluation function training.
Background
Effective monitoring means and prevention measures are taken for road landslide, and important significance is provided for preventing and treating landslide and avoiding and reducing economic loss (reference 1). Landslide monitoring methods and techniques are widely used, and conventional monitoring methods commonly used include a macro geological monitoring method, a geodetic precision measurement method, a GPS method, and the like (reference [2 ]). In recent years, with the development of electronic technology and computer technology, remote automatic remote monitoring systems are widely applied to disaster monitoring and forecasting systems, so that the functions of real-time monitoring, early warning issuing and the like are provided, and the effect is obvious. The remote monitoring system mainly comprises an intelligent sensing, collecting and transmitting system and an intelligent receiving and analyzing system (reference document [3]), is used for point-to-point monitoring of a specific area, and a high slope without monitoring points is a monitoring and early warning blind area.
To date, computational mathematical models of various slope directions have been proposed and developed on Digital Elevation Models (DEMs) (reference [4 ]). Through research on the ground gradient and the combination rule thereof, the method for extracting the road high slope by taking the high-precision DEM as an information source is a mature technical method (reference document [5 ]). In practical engineering application, especially in the periphery of roads with wide extension area range, all identified high slopes are monitored point to point, manpower and material resource consumption is huge, and implementation difficulty is extremely high.
Common landslide evaluation models include univariate models, fuzzy mathematical methods, decision tree models, logistic regression models, support vector machine models, rough set models, and the like (reference [6 ]). The comprehensive evaluation model established on the basis of fuzzy mathematics has the characteristics of high convenience, strong adaptability and the like, can well solve multi-factor and multi-level complex problems, and a plurality of scholars try to apply the method to landslide risk level evaluation, but the complete fuzzy comprehensive evaluation model is not established (reference document [7 ]).
Reference documents:
[1] the monitoring technology of slope engineering analyzes [ J ] road, 2002, (5) 45-48.
[2] Zhangpeng et al. improvement and application effect analysis of landslide disaster remote monitoring system [ J ]. report on rock mechanics and engineering 2011, (10): 2026-.
[3] The flood, landslide geological disaster remote monitoring and forecasting system and the engineering application thereof [ J ]. rock mechanics and engineering science, 2009, (6):1081-1090.
[4] Liu academic Jun et al, analysis and research [ J ] of accuracy based on DEM gradient slope algorithm, survey and drawing, 2004, (8): 258) 263.
[5] Touguen et al, comparative study of gradient grading method in DEM gradient map drawing [ J ] Water and soil conservation academic newspaper 2006, (4): 157-.
[6] Zhao Jianhua, etc. landslide hazard risk evaluation models comparison [ J ] Natural disaster study, 2006, (2) 129 + 134.
[7] Panxiancheng, etc. monomer landslide risk evaluation based on fuzzy comprehensive evaluation method [ J ] civil foundation, 2018, (6) 330-.
Disclosure of Invention
Aiming at the problems that the landslide disaster modeling evaluation is large in manpower and material resource consumption, a complete landslide disaster early warning model is not established, and the like, the invention provides a road high slope landslide disaster early warning method based on evaluation function training by grading landslide disasters through a fuzzy comprehensive evaluation model.
The invention provides a road high slope landslide hazard early warning method based on evaluation function training, which comprises the following steps:
step 1: extracting a road high slope based on digital elevation model DEM data to obtain a road slope value;
step 2: for the high slope with the sensor, recording and storing high slope monitoring data returned by the sensor and known topographic and geological information of the high slope;
step 3; building a fuzzy comprehensive evaluation model by using the monitoring data of the high slope and the topographic and geological information in the step 2, and calculating the grade of landslide hazard of the high slope;
and 4, step 4: establishing a landslide hazard evaluation function related to the position coordinates of the high slope, and training the landslide hazard evaluation function through the judgment factors of the high slope with known landslide hazard level;
the established landslide hazard assessment function is expressed as E ═ F (h, slope, terain, hydrology, location); wherein E represents a landslide disaster grade value, h represents height, slope represents gradient, terrain represents a geological factor, hydrology represents a hydrological factor, and location represents position coordinates of a high slope;
training and learning through sample data of a high slope with known landslide hazard level to obtain a landslide hazard evaluation function F;
and 5: evaluating the risk of the landslide hazard of the high slope without the sensor by using a landslide hazard evaluation function F obtained by training;
step 6: and setting a threshold value of landslide hazard assessment risk according to the requirement of early warning accuracy, and performing landslide early warning when the landslide risk assessment value is larger than the threshold value.
The fuzzy comprehensive evaluation model in the step 3 is expressed as B ═ A · R; wherein, B represents the evaluation result of the landslide hazard grade of the high slope; a represents a weight vector of the evaluation factor; and R is a fuzzy relation matrix and is the membership of each evaluation factor corresponding to each landslide disaster grade.
Compared with the prior art, the invention has the beneficial effects that:
high slope information is extracted by adopting high-precision meter-level digital elevation model DEM data, and on the basis, a fuzzy comprehensive evaluation model is constructed to divide the grade of landslide disasters by analyzing returned monitoring data and topographic and geological information of a high slope provided with a sensor. By training and learning the existing data, innovatively considering the geographical position of the high slope, constructing a landslide disaster evaluation function between the landslide disaster grade and the slope geological topography information, and finally carrying out landslide disaster grade evaluation and early warning on the high slope without a sensor, the method overcomes the limitations of high cost of arranging the slope sensor, missing of sensor monitoring information and the like in the practical implementation process, and has important significance for the research and prediction of the landslide disaster of the high slope of the road.
Drawings
Fig. 1 is a flowchart of a landslide hazard early warning method for a high slope of a road according to the present invention.
Detailed Description
The invention is described in further detail below with reference to the figures and specific examples.
According to the method, high slope information is extracted by adopting digital elevation model DEM data, and on the basis, a fuzzy comprehensive evaluation model is constructed to divide the grade of landslide disasters by analyzing returned monitoring data and topographic and geological information of a high slope provided with a sensor. According to the landslide disaster prediction method, the geographic position of the high slope is innovatively considered through training and learning of the existing data, a landslide disaster evaluation function is constructed between the landslide disaster grade and the slope geological topography information, and finally landslide disaster grade evaluation and early warning are carried out on the high slope without the sensor, so that the limitations of high cost of arranging the slope sensor, sensor monitoring information loss and the like in the practical implementation process are overcome, and the landslide disaster prediction method has important significance for research and prediction of the landslide disaster of the high slope of the road.
As shown in fig. 1, the following describes steps of a road high slope landslide hazard early warning method process based on evaluation function training according to the present invention.
Step 1: and identifying and extracting the road high slope based on DEM data and by analyzing the elevation and the gradient in the road buffer area. Calculating the gradient value S of a certain point (x, y) on the road surface according to the DEM, wherein the gradient value S is a function of the elevation change rate of a terrain surface function z ═ f (x, y) in the east-west direction and the south-north direction, and the expression is as follows:
wherein f isxIs the rate of change of elevation in the east-west direction, fyIs the elevation change rate in the north-south direction. Since the grid DEM stores the surface elevations in the form of discrete points, the terrain surface and surface functions are unknown. Thus, DEM solves for fxAnd fyGenerally in the local range, by numerical differentiation or local surface fitting. The invention adopts the second-order difference 2FD to calculate the gradient value.
Step 2: and for the high slope with the sensor, carrying out data recording and storing on the high slope monitoring data returned by the sensor and the topographic and geological information of the high slope obtained from the known material data.
And step 3: and (3) calculating the grade of the landslide hazard of the high slope by constructing a fuzzy comprehensive evaluation model by utilizing the high slope monitoring data and the topographic and geological information in the step (2).
The sensors arranged on the high slope monitor and transmit back data of gradient, stress, displacement and water quantity of the high slope in real time, data basis is provided for monitoring changes of morphological characteristics, rock stratum structures and the like of the high slope in real time, geological and topographic information of the high slope comprises height, gradient, geological factors, hydrological factors and the like, and grade division of landslide disasters is carried out by constructing a fuzzy comprehensive evaluation model based on multiple factors. The fuzzy comprehensive evaluation model formula is as follows:
B＝A·R (2)
wherein, B represents the evaluation result vector, is the degree representation of the comprehensive condition grading of each evaluated object, and is synthesized by A and R under a proper operator. A represents the weight vector of the evaluation factors, R is a fuzzy relation matrix, and the membership of each value of each evaluation index corresponding to the level domain in the evaluation factor domain is represented. The key point of applying the fuzzy comprehensive evaluation model to carry out geological disaster evaluation is the determination of A and R. In the determination of the evaluation factor weight vector a, the evaluation factor weight vector a can be determined by referring to the weight vector suggested in the detailed rules implemented in the relevant documents such as the prefecture investigation and division basic requirements of geological disasters in counties (cities) (reference 8: zhao loyal, beijing area sudden geological disasters are easy to be caused and the risk degree evaluation [ J ] resource investigation and environment 2009.30 (3): 213) 221.), or by adopting an Analytic Hierarchy Process (AHP) (reference [9-11 ]: 9] high forever, fern, mazajqin, AHP-Fuzzy-based geological disaster risk evaluation research [ J ] coal geology 2009, 21 (AOI): 29-31. [10] liu transmission correction, regional landslide debris flow disaster early warning theory and method research [ J ] hydrological engineering geology, 2004, 31 (3): upa 6 [11] ludow flood and a comprehensive evaluation of urban geological disasters in the geological judgment [ J ] road J ] Fuzzy evaluation risk of roads [ I ] based on AHP, 2009, 29(3): 357-360.) or an expert discriminant method. The fuzzy relation matrix R is determined by an expert scoring method at present, and the scoring is established in a membership degree mode on the basis of rating domain division of the evaluation index, so that the method has higher scientificity and objectivity. The operator between a and R is generally in the form of a multiplication, and represents a weighted sum between the evaluation indexes.
Further, a fuzzy judgment matrix of each index of the high slope is established according to 8 evaluation indexes of height, gradient, geological factor, hydrological factor, gradient, stress, displacement and water quantity of the high slope, which influence the landslide hazard of the high slope. A one-to-one corresponding construction factor set A aiming at the indexesi(i is 1, 2, …, 8), first, qualitative description is expressed quantitatively by the concept of fuzzy mathematics, and taking the slope as an example, generally speaking, the more the slope is trembled, the more the possibility of landslide disaster occurring is increased, and according to the result, R (a) can be determined as the evaluation factor weight vector for the landslide disaster for different slopesi) And expressing that the membership of each value of the corresponding landslide disaster grade domain of each evaluation index can be obtained in the same way. On the basis, the influence contribution degrees of all evaluation index factors of the landslide disaster are accumulated, namely the weighted sum B of all the evaluation indexes is obtained by multiplying the above factors by A and R, and the weighted sum B is the grade of the landslide disaster of the high slope.
And 4, step 4: a landslide disaster evaluation function is constructed by training and learning a large amount of existing data and combining the grade of the landslide disaster and the topographic and geological information of the side slope.
The occurrence of landslide is influenced by a plurality of factors, the traditional training model has a plurality of input parameters, and a relatively accurate evaluation result of whether landslide occurs can be obtained, but the traditional training model cannot be directly applied in considering that a high slope without a sensor lacks slope gradient, stress, displacement and water quantity data. According to the method, the position coordinates of the high slope are innovatively added into the training model, and the possibility of landslide disasters of the high slope near the high-risk slope is increased under the normal condition. By analyzing the corresponding relation between the landslide disaster risk value and the topographic geological information such as the height h, the slope, the geological factor terrain, the hydrological factor hydrology and the position coordinate location of the high slope with the sensor, and training and learning the existing data, the landslide disaster evaluation function E between the landslide disaster risk value (grade) and the topographic geological information considering the regional locality is constructed to be F (h, slope, terrain, hydrology, location).
The landslide disaster grade value B, namely E, is obtained by calculating in step 3 for the high slope provided with the sensor and capable of obtaining monitoring data, and then the function F of the landslide disaster evaluation model is trained and obtained by combining evaluation factors h, slope, terain, hydrology and location which do not need to be monitored to obtain data, so that the obtained evaluation model is irrelevant to the monitoring data, and the method is suitable for disaster grade evaluation of the high slope without monitoring data and provided with no sensor.
And 5: and evaluating the grade of the landslide disaster on the road high slope without the sensor by using a landslide disaster evaluation function.
And for other road high slopes without sensors, evaluating the landslide hazard risk value (grade) of the high slopes without the sensors by using the obtained landslide hazard evaluation function according to the relation between the topographic and geological information of the slopes and the topographic and geological information of the slopes with the sensors and the geographical positions of the slopes. And during evaluation, inputting parameters h, slope, terrain, hydrology and location of the high slope of the road, and outputting to obtain the evaluation grade E of the landslide hazard.
And performing function correction on the trained landslide hazard evaluation function F through the real-time monitoring data and the geological and topographic data of the high slope provided with the sensor so as to enable the evaluation function to be more accurate.
Step 6: and adjusting the threshold value of the landslide hazard assessment risk according to the requirement of the early warning accuracy rate, and carrying out landslide early warning. Generally speaking, the higher the accuracy requirement is, the larger the threshold value is set, and the higher the risk of landslide hazard of the high slope for early warning is.
Example (b):
take landslide hazard level assessment of highways and highways in Hangzhou city, Zhejiang as an example.
The method comprises the steps that firstly, the elevation and the gradient in a road buffer area are analyzed through DEM data, and the high slope of the uphill highway is identified and extracted;
secondly, according to a high slope on the highway on the mountain, which is extracted by high-part image recognition, monitoring a high slope with monitoring sensors such as an inclinometer, a stress meter, a displacement meter and a rain gauge, and integrating and recording monitoring data including slope inclination, a stress value, a displacement value, a water quantity value and the like returned by the sensors in real time, and slope geological and topographic information including height, gradient, geological factors, hydrological factors and the like acquired according to existing materials;
and thirdly, constructing a fuzzy comprehensive evaluation model B which is A.R according to the monitoring data and the topographic and geological information of the high and high slope in the second step, wherein B represents an evaluation result vector, represents the degree of grading of the comprehensive condition of each evaluated object, and is obtained by synthesizing A and R under a proper operator. A represents the weight vector of the evaluation factors, R is a fuzzy relation matrix, and the membership of each value of each evaluation index corresponding to the level domain in the evaluation factor domain is represented. An operator between A and R generally adopts a multiplication form to express the weighted sum of all evaluation indexes;
fourthly, constructing a landslide disaster evaluation function E between the landslide disaster grade giving consideration to the regional position and the topographic geological information, namely F (h, slope, terrain, hydrology, location), by analyzing the height, the gradient, the geological factor, the hydrological factor and other topographic geological information of the high slope with the sensor and the corresponding relation between the position coordinate of the high slope and the landslide disaster grade and training and learning the existing data;
and fifthly, for other road high slopes without sensors, firstly, performing function correction on the landslide hazard evaluation function E by using the relation between the topographic and geological information of the slope and the topographic and geological information of other slopes with sensors and the geographical positions of the slopes, and finally, evaluating the landslide hazard grade of the high slopes without sensors by using the function.
And finally, adjusting the threshold value of the landslide hazard assessment risk according to the requirement of the early warning accuracy rate, and performing landslide early warning. Generally speaking, the higher the accuracy requirement is, the larger the threshold value is set, and the higher the risk of landslide hazard of the high slope for early warning is.
Claims (4)
1. A road high slope landslide hazard early warning method based on evaluation function training is characterized by comprising the following steps:
step 1, extracting a road high slope based on digital elevation model DEM data to obtain a road slope value;
step 2, recording and storing high slope monitoring data returned by the sensor and known topographic and geological information of the high slope for the high slope provided with the sensor;
step 3, constructing a fuzzy comprehensive evaluation model by using the monitoring data of the high slope and the topographic and geological information in the step 2, and calculating the grade of the landslide hazard of the high slope;
the fuzzy comprehensive evaluation model is expressed as B ═ A · R; wherein, B represents the evaluation result of the landslide hazard grade of the high slope; a represents a weight vector of the evaluation factor; r is a fuzzy relation matrix and is a membership relation of each evaluation factor corresponding to each landslide disaster grade;
step 4, establishing a landslide hazard assessment function related to the position coordinates of the high slope, and training the landslide hazard assessment function through the judgment factors of the high slope with known landslide hazard level;
the established landslide hazard assessment function is expressed as E ═ F (h, slope, terain, hydrology, location); e is a landslide disaster grade value, h represents height, slope represents gradient, terrain represents a geological factor, hydrology represents a hydrological factor, and location represents position coordinates of a high slope; training and learning through sample data of a high slope with known landslide hazard level to obtain a landslide hazard evaluation function F;
step 5, evaluating the grade of the landslide disaster on the high slope without the sensor by using a landslide disaster evaluation function F;
and 6, setting a threshold value of the landslide disaster evaluation risk according to the requirement of the early warning accuracy, and performing landslide early warning when the grade value of the landslide disaster is greater than the threshold value.
2. The method according to claim 1, wherein in step 1, the gradient value S of a certain point (x, y) on the road surface is calculated according to DEM, and S is obtained by the elevation change rate of a terrain surface function z ═ f (x, y) in east-west and south-north directions, as follows:
wherein f isxIs the rate of change of elevation in the east-west direction, fyIs the elevation change rate in the north-south direction.
3. The method according to claim 1, wherein in the step 3, the fuzzy comprehensive evaluation model is expressed as B-a-R; wherein, B represents the evaluation result of the landslide hazard grade of the high slope; a represents a weight vector of the evaluation factor; and R is a fuzzy relation matrix and is the membership of each evaluation factor corresponding to each landslide disaster grade.
4. The method according to claim 1 or 3, wherein in the step 3, the fuzzy comprehensive evaluation model comprises 8 evaluation factors of the high slope: altitude, grade, geological factor, hydrological factor, inclination, stress, displacement, and water volume.
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