CN116882731A - Geological disaster risk assessment method and system based on slope unit - Google Patents
Geological disaster risk assessment method and system based on slope unit Download PDFInfo
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Abstract
The invention provides a geological disaster risk assessment method and system based on a slope unit, wherein the method comprises the following steps: according to the soil type and the initial humidity of the underlying surface of each slope unit, calculating the infiltration amount in the rainfall process; combining rainfall and infiltration with geological disaster susceptibility results to obtain geological disaster induction factors influenced by the rainfall; constructing a landslide trigger probability model according to the geological disaster susceptibility result and geological disaster induction factors influenced by water fall; and calculating landslide hazard indexes of landslide occurrence for each slope unit according to the landslide trigger probability model. The invention provides a seamless butt joint management unit based on a slope unit, and gradually changes the current group prevention and group measurement measures into the gridding of a prevention and control unit, so that the understanding of a disaster grid length (disaster informationa) on a geological disaster occurrence discovery mechanism, the effective implementation of measures such as early warning, prevention and control and the like can be enhanced.
Description
Technical Field
The invention relates to the technical field of geological disaster risk assessment, in particular to a geological disaster risk assessment method and system based on a slope unit.
Background
In the rainy season of each year, a large number of precipitation-induced geological disasters are liable to pose a threat. The key to coping with geological disasters is to discover and take governance measures in time. However, current control of geological disaster sites is mainly dependent on limited engineering detection and large-scale group detection and prevention. Compared with disasters such as flood, earthquake and the like, the geological disasters have smaller influence range and belong to disaster events of micro-domain scale.
Therefore, the macro-scale natural disaster evaluation method is difficult to provide effective guidance for controlling geological disasters, and the micro-scale evaluation result leads to remarkable improvement of control and management cost. The precision of the existing special group combined early warning system can not meet the requirements of disaster prevention and reduction. Therefore, how to implement fine management on the hidden danger points of the geological disasters and reduce the cost of cluster detection have become a key problem for geological disaster research.
Disclosure of Invention
The technical problem to be solved by the invention is to provide the geological disaster risk assessment method and the system based on the slope unit, so that the accuracy of rainfall-induced geological disaster risk assessment is improved, and a more reliable decision basis is provided for geological disaster prevention and control.
In order to solve the technical problems, the technical scheme of the invention is as follows:
in a first aspect, a method for evaluating risk of geological disaster based on a ramp unit, the method comprising:
selecting a representative rainfall process according to the historical rainfall data, and calculating rainfall capacity for each slope unit;
according to the soil type and the initial humidity of the underlying surface of each slope unit, calculating the infiltration amount in the rainfall process;
combining rainfall and infiltration with geological disaster susceptibility results to obtain geological disaster induction factors influenced by the rainfall;
constructing a landslide trigger probability model according to the geological disaster susceptibility result and geological disaster induction factors influenced by water fall;
and calculating landslide hazard indexes of landslide occurrence for each slope unit according to the landslide trigger probability model.
Further, according to the soil type and the initial humidity of the underlying surface of each slope unit, the infiltration amount in the rainfall process is calculated, and the method comprises the following steps:
acquiring soil type and initial humidity data of a detection area from a soil geographic database or field actual measurement data;
and calculating the infiltration amount of each slope unit in the rainfall process through an infiltration model according to the soil type and the initial humidity data of the detection area.
Further, the calculation formula of the infiltration model is as follows:
I(t)＝St+A*sqrt(t)
wherein I (t) is the accumulated infiltration amount at time t, S is the initial infiltration speed, A is the suction head in the infiltration process, and t is the time.
Further, combining rainfall and infiltration with a geological disaster susceptibility result to obtain a geological disaster inducing factor affected by the rainfall, including:
determining the relativity of the rainfall and the occurrence of the geological disaster according to the rainfall and the data of the geological disaster;
calculating the infiltration amount of each slope unit by using the infiltration model, and analyzing the relation between the infiltration amount of each slope unit and the occurrence of geological disasters;
combining rainfall, infiltration and geological disaster susceptibility results to construct a rainfall-induced factor model;
and verifying the rainfall-induced factor model through the existing geological disaster event, and adjusting the rainfall-induced factor model.
Further, the calculation formula of the landslide trigger probability model is as follows:
wherein P (LS) is the probability of landslide occurrence, H is the geological disaster susceptibility result, F is the geological disaster induction factor affected by the water fall, a is a constant term, and b1 and b2 are regression coefficients of respective variables.
Further, the determining of the ramp unit includes the steps of:
calculating a topographic feature parameter in the detection area;
normalizing the topographic feature parameters;
weighting and superposing the topographic feature parameters after normalization treatment to generate a comprehensive topographic feature map;
and dividing the slope unit according to the comprehensive topography characteristic diagram, and determining the watershed position and the slope unit boundary according to the extreme points and the boundary lines in the comprehensive topography characteristic diagram.
Further, the terrain characteristic parameters include terrain curvature, slope and slope direction.
In a second aspect, a device for evaluating risk of geological disaster based on a slope unit includes:
the acquisition module is used for selecting a representative rainfall process according to the historical rainfall data and calculating the rainfall capacity of each slope unit; according to the soil type and the initial humidity of the underlying surface of each slope unit, calculating the infiltration amount in the rainfall process;
the processing module is used for combining rainfall and infiltration with geological disaster susceptibility results to obtain geological disaster induction factors influenced by the rainfall; constructing a landslide trigger probability model according to the geological disaster susceptibility result and geological disaster induction factors influenced by water fall; and calculating landslide hazard indexes of landslide occurrence for each slope unit according to the landslide trigger probability model.
In a third aspect, a computer comprises:
one or more processors;
and a storage means for storing one or more programs that, when executed by the one or more processors, cause the one or more processors to implement the method.
In a fourth aspect, a computer readable storage medium has a program stored therein, which when executed by a processor, implements the method.
The scheme of the invention at least comprises the following beneficial effects:
according to the scheme, the infiltration amount in the rainfall process is calculated according to the soil type and the initial humidity of the lower pad surface of each slope unit; combining rainfall and infiltration with geological disaster susceptibility results to obtain geological disaster induction factors influenced by the rainfall; constructing a landslide trigger probability model according to the geological disaster susceptibility result and geological disaster induction factors influenced by water fall; according to the landslide trigger probability model, landslide risk indexes of landslide occurrence are calculated for each slope unit, accuracy of rainfall-induced geological disaster risk assessment is improved, and a more reliable decision basis is provided for geological disaster prevention.
Drawings
Fig. 1 is a flow chart of a method for evaluating the risk of a geological disaster based on a ramp unit according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a device for evaluating the risk of a geological disaster based on a slope unit according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
As shown in fig. 1, an embodiment of the present invention proposes a method for evaluating the risk of a geological disaster based on a ramp unit, the method comprising the steps of:
step 11, selecting a representative rainfall process according to the historical rainfall data, and calculating the rainfall capacity of each slope unit;
step 12, calculating infiltration amount in the rainfall process according to the soil type and the initial humidity of the underlying surface of each slope unit;
step 13, combining rainfall and infiltration with geological disaster susceptibility results to obtain geological disaster induction factors influenced by the rainfall;
Step 14, constructing a landslide trigger probability model according to the geological disaster susceptibility result and geological disaster induction factors influenced by the water fall;
and 15, calculating landslide hazard indexes of landslide occurrence for each slope unit according to the landslide trigger probability model.
In the embodiment of the invention, in step 11, a representative rainfall process is selected according to the historical rainfall data, the rainfall of each slope unit in the rainfall events is calculated, and the influence degree of the rainfall events with different intensities and different time lengths on the geological disaster risk can be known by calculating the rainfall of each slope unit in different rainfall events, so that disaster prevention measures can be formulated in a targeted manner. In step 12, the infiltration amount in the rainfall process is calculated according to the soil type and the initial humidity of the underlying surface of each slope unit, the infiltration amount refers to the process that rainwater enters deep below after passing through the soil surface layer, the infiltration amount has an important influence on the soil moisture change, and the rainwater flow direction and the retention condition under different soil types and humidity conditions can be known through calculating the infiltration amount, so that the geological disaster risk can be known in a targeted manner.
In step 13, the rainfall and the infiltration are combined with the geological disaster susceptibility results to obtain the geological disaster inducing factors affected by the rainfall, wherein the susceptibility refers to the probability of occurrence of geological disasters in an area or a section, the geological disaster inducing factors affected by the rainfall refer to the geological disaster dangers caused by interaction of all the geological factors in the rainfall process, and the geological disaster susceptibility can be more comprehensively evaluated by combining multiple factors such as the rainfall, the infiltration and the geological factors. In step 14, a landslide trigger probability model is constructed according to the geological disaster susceptibility result and the geological disaster inducing factors influenced by the water fall, wherein the landslide trigger probability model is to predict the possibility of landslide according to the historical data and the physical model, and the geological disaster risk can be more accurately estimated by constructing a reliable landslide trigger probability model. In step 15, according to the landslide trigger probability model, a landslide risk index of landslide occurrence is calculated for each slope unit, wherein the landslide risk index refers to a quantitative evaluation index of landslide occurrence probability of each slope unit, and can be used for guiding disaster prevention and control work.
In a preferred embodiment of the present invention, in step 12, it includes:
step 121, acquiring soil type and initial humidity data of a detection area from a soil geographic database or field actual measurement data;
step 122, according to the soil type and the initial humidity data of the detection area, calculating the infiltration amount in the rainfall process for each slope unit through an infiltration model, wherein the calculation formula of the infiltration model is as follows:
I(t)＝St+A*sqrt(t)
wherein I (t) is the accumulated infiltration amount at time t, S is the initial infiltration speed, A is the suction head in the infiltration process, and t is the time.
In the embodiment of the present invention, in step 121, the soil type and the initial humidity data of the detection area need to be obtained from the soil geographic database or the field actual measurement data, where the soil type refers to the characteristics of the soil such as the composition, structure, texture and moisture, and the infiltration characteristics of different soil types are different; the initial humidity is the water content of the soil surface before rainfall starts, the infiltration degree of the rainwater is affected, and the infiltration amount can be calculated more accurately by acquiring the data, so that a reliable basis is provided for subsequent work. In step 122, the infiltration amount in the rainfall process is calculated for each slope unit through an infiltration model according to the soil type and initial humidity data of the detection area, wherein the infiltration model is a mathematical model describing how long the rainwater infiltrates into the soil and gradually deepens. When rainfall starts, the rainwater starts to permeate from the soil surface, the depth gradually deepens after a period of time, and the permeation speed gradually slows down until the permeation stops. By using the formula, the infiltration amount of each slope unit in different time can be calculated, so that the flow direction and the retention condition of rainwater on the slope can be known, and a scientific basis is provided for making disaster prevention measures.
In a preferred embodiment of the present invention, in step 13, it includes:
step 131, determining the relativity of the rainfall and the occurrence of the geological disaster according to the rainfall and the data of the geological disaster;
step 132, calculating the infiltration amount of each slope unit by using the infiltration model, and analyzing the relation between the infiltration amount of each slope unit and the occurrence of geological disasters;
step 133, combining rainfall, infiltration and geological disaster susceptibility results to construct a rainfall-induced factor model;
step 134, verifying the rainfall induction factor model through the existing geological disaster event, and adjusting the rainfall induction factor model, wherein the calculation formula of the landslide trigger probability model is as follows:
wherein P (LS) is the probability of landslide occurrence, H is the geological disaster susceptibility result, F is the geological disaster induction factor affected by the water fall, a is a constant term, and b1 and b2 are regression coefficients of respective variables.
In the embodiment of the present invention, in step 131, the correlation between the rainfall and the occurrence of the geological disaster is determined according to the historical rainfall and the data of the occurrence of the geological disaster, and the occurrence of the geological disaster under different rainfall degrees can be known by analyzing and comparing the data, so as to determine the relationship between the rainfall and the occurrence of the geological disaster. In step 132, the infiltration amount of each slope unit is calculated by using the infiltration model, and the relationship between the infiltration amount of each slope unit and the occurrence of the geological disaster is analyzed, and by analyzing and comparing the infiltration amount of each slope unit, the infiltration amounts of different slope units and the relationship between the different infiltration amounts and the occurrence of the geological disaster can be known under different rainfall levels. In step 133, the rainfall, the infiltration amount and the geological disaster susceptibility result are combined to construct a rainfall-induced factor model, and the rainfall-induced factor model is constructed by comprehensively analyzing the rainfall, the infiltration amount and the geological disaster susceptibility result, so that the rainfall-induced factor model can predict the susceptibility of the geological disaster under the conditions of different rainfall and different infiltration amounts, and the rainfall-induced factor model can help to better know the occurrence mechanism and influence factors of the geological disaster, thereby improving the prediction accuracy and formulating corresponding disaster prevention measures. In step 134, the rainfall-induced factor model is verified by the existing geological disaster event, and adjusted, and by verifying the prediction accuracy of the rainfall-induced factor model, the quality of the model can be evaluated, and the model is adjusted and optimized for the deficiency, so that the prediction capability and reliability of the model are improved.
In another preferred embodiment of the present invention, the determination of the ramp unit includes the steps of:
calculating a topographic feature parameter in the detection area, wherein the topographic feature parameter comprises a topographic curvature, a gradient and a slope direction;
normalizing the topographic feature parameters;
weighting and superposing the topographic feature parameters after normalization treatment to generate a comprehensive topographic feature map;
and dividing the slope unit according to the comprehensive topography characteristic diagram, and determining the watershed position and the slope unit boundary according to the extreme points and the boundary lines in the comprehensive topography characteristic diagram.
In an embodiment of the invention, the topographic feature parameters in the detection area are calculated, including the topographic curvature, the grade and the slope direction. The topography curvature describes the degree of curvature of the ground surface relative to the horizontal, the slope describes how fast the ground level changes, and the slope direction represents the directional characteristics of the ground. And carrying out normalization processing on the topographic feature parameters. The normalization can convert data of different orders into the same scale range, so that subsequent weighted superposition is facilitated. For example, the raw data may be mapped linearly between 0 and 1 using a min-max normalization method. And carrying out weighted superposition on the topographic feature parameters subjected to normalization processing to generate a comprehensive topographic feature map. The purpose of the weighted superposition is to comprehensively consider different topographic feature parameters and give a comprehensive topographic index to better reflect the complexity of the topographic conditions. And dividing the slope unit according to the comprehensive topography characteristic diagram, and determining the watershed position and the slope unit boundary according to the extreme points and the boundary lines in the comprehensive topography characteristic diagram. In this step, the topography index is typically divided into different categories using some mathematical method (e.g., cluster analysis, least squares, thresholding, etc.), and the ramp unit and watershed positions are determined. The watershed is a boundary line between ramp units, which is generally located in a region where connectivity is weak, indicating that from this region, significant turning and change in the direction and travel path of the two-sided flow occurs.
In another preferred embodiment of the present invention, in acquiring historical rainfall data, data such as geological background, topographic data, remote sensing images, hydrographic environments, human activities and the like are acquired from a geological survey website and a related geographic information database. And (5) extracting all environmental factors based on the slope unit. Calculating the contribution rate of each environmental factor in the index system to the development of the hidden danger of the geological disaster, and carrying out correlation analysis on all the environmental factors. And constructing a sample data set by using the environmental factor information of the geological disaster hidden danger points and the non-geological disaster hidden danger point samples in the database.
In the susceptibility evaluation of geological disasters, the random forest model can effectively process complex relations among various environmental factors, so that the accuracy of identifying the hidden dangers of the geological disasters is improved. And constructing a sample data set of the hidden danger points and the non-hidden danger points of the geological disaster through the geological disaster data set. The data set is divided into a training set and a verification set, so that the generalization capability of the model is ensured. The random forest model is trained using the training set data and the relevant parameters (number of decision trees, maximum depth and feature subset size) are adjusted during model training to optimize model performance. The importance of each disaster-tolerant environmental factor is evaluated through a random forest algorithm, and the influence degree of the environmental factors on the susceptibility of the geological disaster is revealed. After model training is completed, model prediction accuracy is evaluated by using verification set data. And calculating evaluation indexes such as accuracy, recall rate, F1 score and the like of the model according to the prediction result so as to quantitatively evaluate the performance of the model.
In order to more comprehensively evaluate the risk of geological disasters, the rainfall is combined with the underlying surface, and the rainfall and infiltration of each slope unit in a specific rainfall event are calculated by adopting a hydrologic calculation model. According to the rainfall of each slope unit and the geological disaster liability obtained by the random forest model, a geological disaster risk assessment model is constructed so as to more accurately predict possible landslide events.
The selection and quantification of the disaster-tolerant environmental factors are the precondition of landslide vulnerability evaluation, the accuracy of the selection of the disaster-tolerant environmental factors directly influences the final evaluation result, and the selection of the evaluation factors suitable for the detection area has a decisive effect on the accuracy of the final evaluation result. And collecting and processing geological disaster influence factor data such as elevation, gradient, NDVI (normalized vegetation index), stratum lithology, distance from water system, distance from fault, distance from road, land utilization type, average rainfall value of the detection area for years and the like.
Elevation refers to the vertical height of a point on the surface of the earth relative to a reference plane (typically sea level). In geological disaster research, the elevation influences the factors such as the topographic features, the climatic conditions, the hydrologic environment and the like, so that landslide generation and development are influenced. Elevation differences may lead to increased terrain complexity and correspondingly increased risk of geological disasters.
Slope is one of the main factors affecting the occurrence of geological disasters, and is directly related to soil layer thickness, hydrologic conditions, vegetation coverage and many other factors. The gradient influences the development and inoculation process of geological disasters by influencing the conditions of slope body stress distribution, surface runoff, groundwater supply, groundwater discharge and the like, and is one of important influencing factors. Along with the increase of the gradient, the sliding force of the slope body is gradually increased, and the occurrence probability of the corresponding geological disaster is also increased.
The normalized vegetation index (Normalized Difference Vegetation Index, NDVI) is an indicator of vegetation coverage monitored by remote sensing techniques. The influence of vegetation on geological disasters is mainly reflected in the consolidation effect of vegetation root systems on soil and the regulation effect of vegetation on precipitation infiltration. The higher the vegetation coverage, the lower the likelihood of geological disasters occurring.
Formation lithology refers to the nature, composition, and structure of rock in a formation. Different formation lithology can lead to different physical properties such as shear strength, permeability and the like, thereby influencing the occurrence condition and stability of geological disasters. For example, differences in lithology of sandstone and claystone result in significant differences in their slip resistance and shear strength.
The distance from the water system means the horizontal distance of the water system (such as river, lake, marsh, etc.) closest to a certain slope unit. The water system distance can influence hydrologic conditions such as groundwater level, infiltration volume, and the like, thereby influencing the occurrence and development of geological disasters. Generally, the ground water level is high in a region closer to the water system, and the risk of geological disasters is relatively high.
The distance from a fault refers to the horizontal distance of a fault closest to a certain ramp unit. The influence of faults on geological disasters is mainly reflected in the aspects of crustal movement and crustal stress distribution. The area close to the fault has larger ground stress, relatively complex geological structure and higher possibility of occurrence of geological disasters.
The road distance refers to a horizontal distance of a road closest to a certain ramp unit. Road construction and use can affect the geological environment and groundwater dynamics in surrounding areas. The geological environment of the area close to the road may be disturbed by artificial activities, and the risk of occurrence of geological disasters is relatively high.
Land use type refers to a classification of land surface use, including farmland, woodland, grassland, construction land, and the like. The influence of different land utilization types on the occurrence of geological disasters is mainly reflected in the aspects of surface coverage characteristics, groundwater dynamics, human activity interference and the like. For example, woodlands often have higher vegetation coverage, which is beneficial to soil consolidation and reduces the risk of geological disasters; the construction land may cause adverse factors such as surface damage and underground water level change, and the possibility of occurrence of geological disasters is increased.
The average rainfall over a plurality of years is an average rainfall over a long period of time in a certain detection area. Rainfall is one of the main causative factors affecting the occurrence of geological disasters, and is closely related to groundwater dynamics, soil shear strength and the like. In areas where the average rainfall is high in the detection area for many years, the probability of occurrence of geological disasters is generally high, because continuous or strong rainfall may cause adverse conditions such as reduced soil shear strength, rising groundwater level and the like, thereby increasing the risk of geological disasters.
In order to improve the accuracy of the dividing result of the slope unit, the curvature watershed method and the gradient-direction method are combined, and various terrain features are comprehensively considered.
The method comprises the following specific steps: step 1, calculating a topographic feature parameter: first, the feature parameters of the terrain such as the curvature, gradient, and slope direction are calculated from Digital Elevation Model (DEM) data, respectively. Step 2, normalization processing: and in order to enable the feature parameters of each terrain to have the same numerical range and weight, carrying out normalization processing on the calculated curvature, gradient and slope direction of the terrain. Step 3, generating a comprehensive topography characteristic diagram: and carrying out weighted superposition on the normalized topographic curvature, gradient and slope direction to generate a comprehensive topographic feature map. The weight can be set according to actual requirements and data characteristics. The comprehensive terrain feature map can reflect three-dimensional morphological features of terrain more comprehensively, and is beneficial to improving accuracy of dividing slope units. Step 4, determining watershed and slope unit boundaries: based on the comprehensive topography characteristic diagram, a curvature watershed method is adopted for dividing the slope units. The watershed position and the slope unit boundary can be determined according to extreme points and boundary lines in the comprehensive topography characteristic map. At this time, the judgment of the boundaries of the watershed and the slope unit not only depends on the curvature information, but also considers the gradient and the slope direction information, thereby being beneficial to improving the accuracy of the dividing result.
When the comprehensive topographic feature map is generated, the invention adjusts the weight of the curvature, the gradient and the slope direction of the topography according to the actual requirements and the data characteristics so as to realize the priority consideration of different topographic features, and when the boundaries of the watershed and the slope unit are determined, a dynamic threshold strategy can be adopted to dynamically adjust the threshold according to the local change of the topographic features, so that more accurate dividing results are obtained in different topographic regions.
It should be noted that, the ramp unit generated by using the curvature watershed method in combination with the gradient-direction method has the following advantages: 1. comprehensively utilizing the topographic information: by combining the curvature watershed method and the gradient-direction method, the terrain curvature, gradient and gradient information in the DEM data can be fully utilized, so that the generated slope unit boundary is more in line with the actual terrain characteristics, and the accuracy of the slope unit division is improved. 2. Improving watershed recognition capability: and gradient-direction information is introduced on the basis of a curvature watershed method, so that the watershed position can be accurately identified. Particularly in areas with more complex terrain variations, the two methods can be combined to better cope with the terrain variations with different scales and complexity. 3. The adaptability is stronger, and compared with a curvature watershed method or a gradient-direction method which are independently used, the adaptability of the slope unit division can be improved by combining the two methods. This combination method can achieve better division effect when processing various different types of terrains. 4. In practical application, the weight and parameter setting of the curvature watershed method and the gradient-direction method can be adjusted according to practical requirements and terrain characteristics, so that greater flexibility is provided for processing terrains with different characteristics.
It should be noted that, by establishing a plurality of decision trees and fusing the Bagging ideas with randomly selected features, a more accurate and stable model is obtained. Then, a decision tree model is constructed by using K samples, and K different classification results are obtained. The model constructs a plurality of decision trees, and when a certain sample needs to be predicted, the prediction result of each tree in the forest on the sample is statistically calculated through a voting method, and a final result is obtained according to the principle that the number of the prediction results is less and the number of the prediction results is more. Its random body now has two aspects: firstly, randomly taking characteristics; secondly, randomly taking samples; each tree in the forest is allowed to have both similarity and variability. The model has the advantages of high accuracy, high operation efficiency, capability of processing high-dimensional characteristics without dimension reduction, capability of balancing errors for category unbalanced data and the like. According to the method, a random forest model is selected to conduct landslide vulnerability analysis.
The sample point extraction specifically comprises the steps of constructing a geological disaster point data set comprising landslide and debris flow, and constructing a sample data set based on landslide point sample data (positive samples) and non-landslide point sample data (negative samples) for model training and model performance verification. The positive sample is a data sample of landslide points by acquiring attribute values of various landslide evaluation indexes of the landslide points through ArcGIS according to the landslide occurrence points; the negative sample is a factor point which is randomly generated by 100m out of the known landslide point, and the attribute values of various landslide evaluation indexes of the non-landslide point are collected as the data sample of the non-landslide point.
It should be noted that, the data preprocessing specifically includes:
missing value processing, in which missing values refer to clustering, grouping, deleting or truncating of data in coarse data due to lack of information, refers to incomplete values of one or some attributes in existing data sets. The missing values are treated here in a mode-filled manner.
Outlier handling, outlier (outlier), refers to a point in a sample where some values deviate significantly from the rest of the values, and is therefore also called an outlier. Outliers are an important link in data cleaning, and may directly cause deviation in subsequent data analysis and modeling. Outliers are handled herein based on the 3σ principle.
The probability of the sample being approximately normal distribution, the numerical distribution in (mu-3 sigma, mu+3 sigma) is 99.73%, the probability of falling outside + -3 sigma is only 0.27% for the random error of normal distribution, and the probability of occurrence in a limited number of measurements is small, so that the maximum or minimum value exceeding this range is regarded as an outlier and rejected.
The dimension reduction and feature extraction are a preprocessing method for high-dimension feature data, wherein the high-dimension data is kept with the most important features, noise and unimportant features are removed, so that the aim of improving the data processing speed is fulfilled. The invention adopts a principal component analysis method to reduce the data to two dimensions. Principal component analysis (Principal Component Analysis) is one of the most widely used data dimension reduction algorithms. PCA maps n-dimensional features onto k-dimensions, which are completely new orthogonal features, also called principal components, and are k-dimensional features reconstructed on the basis of original n-dimensional features. PCA works by sequentially finding a set of mutually orthogonal axes from the original space, the selection of which is closely related to the data itself. The first new coordinate axis is selected to be the direction with the maximum variance in the original data, the second new coordinate axis is selected to be the plane orthogonal to the first coordinate axis so as to make the variance maximum, and the third axis is selected to be the plane orthogonal to the 1 st and 2 nd axes so as to make the variance maximum. By analogy, n such coordinate axes may be obtained. The new axes obtained in this way have a majority of the variances contained in the first k axes, and the latter axes have a variance of almost 0. Thus, we can ignore the remaining axes, leaving only the first k axes with the vast majority of variances. In fact, this amounts to retaining only dimensional features containing a substantial portion of variance, while ignoring feature dimensions containing variances of almost 0, achieving dimension reduction of the data features. The method comprises the steps of reducing dimensions of a plurality of variables based on a principal component analysis method, and converting the plurality of variables into principal components capable of fully reflecting most information in the original variables without repetition.
Data splitting and standardization, namely splitting a data set into a training set and a verification set, randomly extracting 80% of positive samples and negative samples as the training set, and the rest 20% as the verification set. Preprocessing the training set based on the Standard scaler method, storing the preprocessed parameters, and processing the verification set by using the same parameters.
The research area evaluation factor grading and attribute extraction specifically comprises the following steps:
grading the evaluation factors, namely grading the evaluation factors, wherein the grading is the basis for researching the spatial distribution relation between landslide and the evaluation factors, each evaluation factor in a research area can be divided into two major categories, namely continuous type (such as elevation, NDVI, gradient and the like) and discrete type (such as soil type and land utilization type), and reclassifying continuous data; discrete data refers to the thought and method of previous study, and natural numbers of 1-9 are assigned to the discrete data for quantification treatment. A forever-Jia county landslide vulnerability assessment index hierarchy was established (table 1).
TABLE 1 landslide susceptibility evaluation index grading system
The method is based on a GridSearchCV method, parameters are sequentially adjusted according to step length in a designated parameter range, a learner is trained by using the adjusted parameters, the parameter with the highest precision on a verification set is found from all the parameters, and a Scikit-learning library under a Python3.9 environment is adopted to realize the random forest.
As shown in fig. 2, an embodiment of the present invention further provides a geological disaster risk assessment device 20 based on a ramp unit, including:
the obtaining module 21 is configured to select a representative rainfall process according to the historical rainfall data, and calculate a rainfall amount for each slope unit; according to the soil type and the initial humidity of the underlying surface of each slope unit, calculating the infiltration amount in the rainfall process;
a processing module 22, configured to combine rainfall and infiltration with a geological disaster susceptibility result to obtain a geological disaster inducing factor affected by the rainfall; constructing a landslide trigger probability model according to the geological disaster susceptibility result and geological disaster induction factors influenced by water fall; and calculating landslide hazard indexes of landslide occurrence for each slope unit according to the landslide trigger probability model.
Optionally, calculating the infiltration amount in the rainfall process according to the soil type of the underlying surface of each slope unit and the initial humidity, including:
acquiring soil type and initial humidity data of a detection area from a soil geographic database or field actual measurement data;
and calculating the infiltration amount of each slope unit in the rainfall process through an infiltration model according to the soil type and the initial humidity data of the detection area.
Optionally, the calculation formula of the infiltration model is:
I(t)＝St+A*sqrt(t)；
wherein I (t) is the accumulated infiltration amount at time t, S is the initial infiltration speed, A is the suction head in the infiltration process, and t is the time.
Optionally, combining the rainfall and the infiltration with the geological disaster susceptibility results to obtain geological disaster inducing factors affected by the rainfall, including:
determining the relativity of the rainfall and the occurrence of the geological disaster according to the rainfall and the data of the geological disaster;
calculating the infiltration amount of each slope unit by using the infiltration model, and analyzing the relation between the infiltration amount of each slope unit and the occurrence of geological disasters;
combining rainfall, infiltration and geological disaster susceptibility results to construct a rainfall-induced factor model;
and verifying the rainfall-induced factor model through the existing geological disaster event, and adjusting the rainfall-induced factor model.
Optionally, the calculation formula of the landslide trigger probability model is as follows:
wherein P (LS) is the probability of landslide occurrence, H is the geological disaster susceptibility result, F is the geological disaster induction factor affected by the water fall, a is a constant term, and b1 and b2 are regression coefficients of respective variables.
Optionally, the determining of the ramp unit includes the steps of:
Calculating a topographic feature parameter in the detection area;
normalizing the topographic feature parameters;
weighting and superposing the topographic feature parameters after normalization treatment to generate a comprehensive topographic feature map;
and dividing the slope unit according to the comprehensive topography characteristic diagram, and determining the watershed position and the slope unit boundary according to the extreme points and the boundary lines in the comprehensive topography characteristic diagram.
Optionally, the terrain characteristic parameters include terrain curvature, slope and slope direction.
It should be noted that the apparatus is an apparatus corresponding to the above method, and all implementation manners in the above method embodiment are applicable to this embodiment, so that the same technical effects can be achieved.
Embodiments of the present invention also provide a computer including: a processor, a memory storing a computer program which, when executed by the processor, performs the method as described above. All the implementation manners in the method embodiment are applicable to the embodiment, and the same technical effect can be achieved.
Embodiments of the present invention also provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform a method as described above. All the implementation manners in the method embodiment are applicable to the embodiment, and the same technical effect can be achieved.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk, etc.
Furthermore, it should be noted that in the apparatus and method of the present invention, it is apparent that the components or steps may be disassembled and/or assembled. Such decomposition and/or recombination should be considered as equivalent aspects of the present invention. Also, the steps of performing the series of processes described above may naturally be performed in chronological order in the order of description, but are not necessarily performed in chronological order, and some steps may be performed in parallel or independently of each other. It will be appreciated by those of ordinary skill in the art that all or any of the steps or components of the methods and apparatus of the present invention may be implemented in hardware, firmware, software, or a combination thereof in any computing device (including processors, storage media, etc.) or network of computing devices, as would be apparent to one of ordinary skill in the art after reading this description of the invention.
The object of the invention can thus also be achieved by running a program or a set of programs on any computing device. The computing device may be a well-known general purpose device. The object of the invention can thus also be achieved by merely providing a program product containing program code for implementing said method or apparatus. That is, such a program product also constitutes the present invention, and a storage medium storing such a program product also constitutes the present invention. It is apparent that the storage medium may be any known storage medium or any storage medium developed in the future. It should also be noted that in the apparatus and method of the present invention, it is apparent that the components or steps may be disassembled and/or assembled. Such decomposition and/or recombination should be considered as equivalent aspects of the present invention. The steps of executing the series of processes may naturally be executed in chronological order in the order described, but are not necessarily executed in chronological order. Some steps may be performed in parallel or independently of each other.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the present invention.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the present invention.
Claims (10)
1. A method for assessing risk of a geological disaster based on a ramp unit, the method comprising:
selecting a representative rainfall process according to the historical rainfall data, and calculating rainfall capacity for each slope unit;
according to the soil type and the initial humidity of the underlying surface of each slope unit, calculating the infiltration amount in the rainfall process;
combining rainfall and infiltration with geological disaster susceptibility results to obtain geological disaster induction factors influenced by the rainfall;
constructing a landslide trigger probability model according to the geological disaster susceptibility result and geological disaster induction factors influenced by water fall;
And calculating landslide hazard indexes of landslide occurrence for each slope unit according to the landslide trigger probability model.
2. The slope unit-based geological disaster risk assessment method according to claim 1, wherein calculating the infiltration amount during rainfall according to the soil type of the underlying surface of each slope unit and the initial humidity comprises:
acquiring soil type and initial humidity data of a detection area from a soil geographic database or field actual measurement data;
and calculating the infiltration amount of each slope unit in the rainfall process through an infiltration model according to the soil type and the initial humidity data of the detection area.
3. The method for evaluating the risk of geological disasters based on a slope unit according to claim 2, wherein the calculation formula of the infiltration model is:
I(t)＝St+A*sqrt(t)
wherein I (t) is the accumulated infiltration amount at time t, S is the initial infiltration speed, A is the suction head in the infiltration process, and t is the time.
4. The method for evaluating the risk of a geological disaster based on a slope unit according to claim 3, wherein combining the rainfall and the infiltration with the result of geological disaster susceptibility to obtain the geological disaster inducing factor affected by the rainfall comprises:
Determining the relativity of the rainfall and the occurrence of the geological disaster according to the rainfall and the data of the geological disaster;
calculating the infiltration amount of each slope unit by using the infiltration model, and analyzing the relation between the infiltration amount of each slope unit and the occurrence of geological disasters;
combining rainfall, infiltration and geological disaster susceptibility results to construct a rainfall-induced factor model;
and verifying the rainfall-induced factor model through the existing geological disaster event, and adjusting the rainfall-induced factor model.
5. The method for evaluating the risk of geological disasters based on a slope unit according to claim 4, wherein the calculation formula of the landslide trigger probability model is as follows:
wherein P (LS) is the probability of landslide occurrence, H is the geological disaster susceptibility result, F is the geological disaster induction factor affected by the water fall, a is a constant term, and b1 and b2 are regression coefficients of respective variables.
6. The ramp unit-based geological disaster risk assessment method according to claim 1, wherein the determination of the ramp unit comprises the steps of:
calculating a topographic feature parameter in the detection area;
normalizing the topographic feature parameters;
Weighting and superposing the topographic feature parameters after normalization treatment to generate a comprehensive topographic feature map;
and dividing the slope unit according to the comprehensive topography characteristic diagram, and determining the watershed position and the slope unit boundary according to the extreme points and the boundary lines in the comprehensive topography characteristic diagram.
7. The ramp unit-based geological disaster risk assessment method according to claim 6, wherein said topographical feature parameters include topographical curvature, grade and slope direction.
8. A ramp unit-based geological disaster risk assessment device, comprising:
the acquisition module is used for selecting a representative rainfall process according to the historical rainfall data and calculating the rainfall capacity of each slope unit; according to the soil type and the initial humidity of the underlying surface of each slope unit, calculating the infiltration amount in the rainfall process;
the processing module is used for combining rainfall and infiltration with geological disaster susceptibility results to obtain geological disaster induction factors influenced by the rainfall; constructing a landslide trigger probability model according to the geological disaster susceptibility result and geological disaster induction factors influenced by water fall; and calculating landslide hazard indexes of landslide occurrence for each slope unit according to the landslide trigger probability model.
9. A computer, comprising:
one or more processors;
storage means for storing one or more programs which when executed by the one or more processors cause the one or more processors to implement the method of any of claims 1-7.
10. A computer readable storage medium, characterized in that the computer readable storage medium has stored therein a program which, when executed by a processor, implements the method according to any of claims 1-7.
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| CN117764402A (en) * | 2024-02-22 | 2024-03-26 | 山东光合云谷大数据有限公司 | Geological disaster information processing system |
| CN117764402B (en) * | 2024-02-22 | 2024-05-14 | 山东光合云谷大数据有限公司 | Geological disaster information processing system |
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| CN117764402A (en) * | 2024-02-22 | 2024-03-26 | 山东光合云谷大数据有限公司 | Geological disaster information processing system |
| CN117764402B (en) * | 2024-02-22 | 2024-05-14 | 山东光合云谷大数据有限公司 | Geological disaster information processing system |
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