CN112200357B - Landslide prediction method, landslide prediction device, landslide prediction equipment and storage medium - Google Patents
Landslide prediction method, landslide prediction device, landslide prediction equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the invention discloses a landslide prediction method, device, equipment and storage medium, which are used for acquiring landslide information of a target area, determining the target size of each grid of the target area based on the landslide information, determining the target size of each grid according to actual landslide information, further ensuring that landslide influence factors have the best characterization effect under the target size, and carrying out landslide prediction based on the landslide influence factors of each grid within a set time period under the target size, thereby improving the precision of data, predicting in units of days, fully considering the time sequence information of dynamic factors and improving the prediction precision; by setting two preset models, landslide prediction is carried out in two stages, whether the probability of landslide occurrence in the current day is larger than a set value or not is predicted in the first stage, if so, landslide prediction in the second stage is carried out, and the landslide occurrence probability of a specific grid is carried out by taking the data in the current day as a reference, so that the waste of calculation resources is greatly reduced, and the landslide prediction efficiency is improved.
Description
Technical Field
The embodiment of the invention relates to the technical field of landslide monitoring, in particular to a landslide prediction method, a landslide prediction device, landslide prediction equipment and a storage medium.
Background
Landslide is one of the most common disastrous natural disasters, has the characteristics of wide distribution range, high occurrence frequency, multiple occurrence, regional property, severity and the like, and can cause a large amount of casualties and serious environmental and infrastructure losses every year. The method has important significance in evaluating the liability of landslide.
The existing landslide susceptibility prediction can be divided into a deterministic method and a non-deterministic method according to the difference of theoretical basis on which the landslide susceptibility prediction is based. The deterministic method is mainly a directional analysis based on expert experience and knowledge and a landslide process or physical model analysis method, and the prediction accuracy is poor. With the rapid development of computer technology and 3S technology in recent years, non-deterministic methods have been widely used, mainly including fuzzy logic methods, analytic hierarchy processes, decision trees, and the like. However, the time precision of the landslide factor processed by the method is poor, particularly the rainfall factor, and only one year of rainfall is usually considered, so that the prediction precision is also not ideal, meanwhile, due to the fact that the influence factors of landslide are numerous, the prediction efficiency of prediction by only adopting a non-determining method is poor, and since grid size determination is not performed, landslide prediction based on landslide data under an optimal grid cannot be guaranteed, and further prediction precision cannot be guaranteed.
Disclosure of Invention
The invention provides a landslide prediction method, device, equipment and storage medium, which realize the determination of an optimal grid based on the actual condition of a target area, and carry out landslide prediction according to landslide data of each grid every day, so that the prediction precision is improved, and meanwhile, the prediction efficiency is improved by carrying out staged landslide prediction based on two models.
In a first aspect, an embodiment of the present invention provides a method for predicting a landslide, where the method for predicting a landslide includes:
acquiring landslide information of a target area, and determining the target size of each grid of the target area based on the landslide information, wherein the landslide information comprises landslide positions and landslide areas;
under the target size, acquiring a landslide influence factor of each grid of the target area every day in a set time period, and determining the occurrence probability of the landslide every day according to the landslide influence factor based on a first preset model;
and if the occurrence probability of the landslide on the current day is larger than the preset probability threshold value, predicting the occurrence probability of the landslide of each grid based on a second preset model according to the landslide influence factors of each grid on the current day.
In a second aspect, an embodiment of the present invention further provides a device for predicting a landslide, where the device for predicting a landslide includes:
The grid size determining module is used for acquiring landslide information of a target area and determining the target size of each grid of the target area based on the landslide information, wherein the landslide information comprises landslide positions and landslide areas;
the first landslide prediction module is used for acquiring landslide influence factors of each grid of the target area every day in a set time period under the target size, and determining occurrence probability of the landslide every day according to the landslide influence factors based on a first preset model;
and the second landslide prediction module is used for predicting the occurrence probability of landslide of each grid according to the landslide influence factors of each grid on the current day based on a second preset model if the occurrence probability of landslide on the current day is larger than a preset probability threshold value.
In a third aspect, an embodiment of the present invention further provides a landslide prediction apparatus, including:
one or more processors;
a memory for storing one or more programs;
when the one or more programs are executed by the one or more processors, the one or more processors implement the landslide prediction method provided by any embodiment of the present invention.
In a fourth aspect, embodiments of the present invention also provide a storage medium containing computer-executable instructions, which when executed by a computer processor, are used to perform the method of predicting landslide provided by any of the embodiments of the present invention.
According to the technical scheme, landslide information of a target area is obtained, the target size of each grid of the target area is determined based on the landslide information, so that the target size of each grid is determined according to actual landslide information, and further, the landslide influence factors have the best characterization effect under the target size, landslide prediction is carried out based on the landslide influence factors of each grid every day in a set time period, the data precision is improved, prediction is carried out in units of days, the time sequence information of dynamic factors is fully considered, and the prediction precision is improved; by setting two preset models, landslide prediction is carried out in two stages, whether the probability of landslide occurrence in the current day is larger than a set value or not is predicted in the first stage, if so, landslide prediction in the second stage is carried out, and the landslide occurrence probability of a specific grid is carried out by taking the data in the current day as a reference, so that the waste of calculation resources is greatly reduced, and the landslide prediction efficiency is improved.
Drawings
FIG. 1 is a flow chart of a method for predicting landslide in accordance with a first embodiment of the invention;
FIG. 2 is a flow chart of a method for predicting landslide in accordance with a second embodiment of the present invention;
FIG. 3 is a flowchart of a landslide prediction method in accordance with a third embodiment of the present invention;
Fig. 4 is a schematic structural diagram of a landslide prediction device according to a fourth embodiment of the present invention;
fig. 5 is a schematic structural diagram of a landslide prediction apparatus in a fifth embodiment of the invention.
Detailed Description
The invention is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present invention are shown in the drawings.
Example 1
Fig. 1 is a flowchart of a landslide prediction method according to an embodiment of the present invention, where the method may be performed by a landslide prediction device, and the method includes the following steps:
step 110, acquiring landslide information of the target area, and determining the target size of each grid of the target area based on the landslide information.
The target area is usually an area where landslide occurs, and may be any designated area. The target area may be obtained from a catalog of pre-entered prediction areas. The landslide information can be determined by correlating disaster type, landslide occurrence date, longitude and latitude information with coordinate information of each grid. Optionally, the landslide information includes a landslide position and a landslide area. The landslide location may include coordinate information of each grid where the landslide occurs, and the landslide area may be determined according to the coordinate information of each grid where the landslide occurs.
It can be understood that if the grid size of the grid corresponding to the target area is too large, a plurality of landslides which are relatively close fall into one grid, so that the landslide influence factor is too small; if the grid size of the grid corresponding to the target area is too small, the calculation amount of the landslide prediction process is greatly increased. In order to reduce the calculation amount of the landslide prediction process and simultaneously preserve the richness of the landslide influence factors, the embodiment can determine the optimal grid size of the target area according to the landslide information of the target area, namely, determine the target size. Alternatively, the present embodiment may determine the target size of each grid by: determining the initial size and the initial grid number of each grid of the target area, and determining the landslide grid number and the non-landslide grid number according to the landslide information, wherein the landslide grid number is the grid number of landslide occurrence and the non-landslide grid number is the grid number of non-landslide occurrence; if the ratio of the landslide number to the non-landslide number is in a set balance interval, taking the initial size as the target size; if the ratio of the landslide grid number to the non-landslide grid number is not in the set balance interval, adjusting the size of each grid of the target area, and re-determining the landslide grid number, the non-landslide grid number and the current grid number; and if the ratio of the redetermined landslide grid number to the non-landslide grid number is in the set balance interval and the ratio of the current grid number to the initial grid number is smaller than a set threshold value, taking the size of each grid after adjustment as the target size.
The initial dimension may be any dimension, for example 1km by 1km. The initial number of grids is the number of grids corresponding to the initial size. It can be understood that when the target area is rasterized, landslide of the target area falls into the grids, the grids where landslide occurs and the grids where landslide does not occur are determined according to the landslide position and the landslide area, and the number of landslide grids and the number of non-landslide grids are determined. Further, calculating the ratio of the number of landslide grids to the number of non-landslide grids, if the ratio of the number of landslide grids to the number of non-landslide grids is in a set balance interval, indicating that the number of landslide grids in the grids corresponding to the initial size is balanced with the number of non-landslide grids, and taking the initial size as a target size; if the ratio of the number of landslide grids to the number of non-landslide grids is not in a set balance interval, the size of each grid is regulated according to the regulating gradient, the number of landslide grids, the number of non-landslide grids and the current number of grids are redetermined, if the ratio of the redetermined number of landslide grids to the number of non-landslide grids is in the set balance interval, the number of landslide grids and the number of non-landslide grids are in a balance state, if the ratio of the current number of grids to the number of initial grids is smaller than a set threshold, the total number of landslide grids in a target area is in a balance state, and the regulated size of each grid can be used as the target size.
Optionally, the present embodiment may further determine the target size of each grid by: acquiring rainfall data of each rainfall station of the target area, the initial size and the initial grid number of each grid of the target area, and determining the landslide grid number and the non-landslide grid number according to the landslide information, wherein the landslide grid number is the number of grids with landslide, and the non-landslide grid number is the number of grids without landslide; calculating rainfall predicted values of the rainfall stations corresponding to any current grid according to rainfall data of the rainfall stations with a specific number, and calculating rainfall errors based on the rainfall predicted values and actually measured rainfall values of the rainfall stations corresponding to the current grid; if the rainfall error is smaller than a set error threshold, and the ratio of the landslide grid number to the non-landslide grid number is in a set balance interval, taking the initial size as the target size; if the rainfall error is larger than a set error threshold, adjusting the size of each grid of the target area, and recalculating the rainfall error according to the rainfall predicted value and the actually measured rainfall value of the rainfall station corresponding to any grid after adjustment; and if the recalculated rainfall error is smaller than the set error threshold, the ratio of the redetermined landslide grid number to the redetermined non-landslide grid number is in a set balance interval, the ratio of the current grid number to the initial grid number is smaller than the set threshold, and the size of the grid after adjustment is taken as the target size.
Specifically, a plurality of monitoring points can be set in the target area, and each monitoring point is provided with a rainfall station so as to acquire rainfall data of each monitoring point in real time. Specifically, the target area is rasterized, a plurality of set rainfall stations are distributed in different grids, when rainfall of a certain monitoring point is predicted, rainfall of a plurality of other monitoring points can be obtained, the rainfall of the monitoring point is determined based on a collaborative Kriging interpolation method and by taking elevation information as auxiliary information, the rainfall is used as predicted rainfall of the monitoring point, the predicted rainfall and the actually measured rainfall obtained by the rainfall stations of the monitoring point are subjected to rainfall error, and if the rainfall error is smaller than a set error threshold, the predicted rainfall of the rainfall station in the grids under the initial size is more accurate; meanwhile, calculating the ratio of the number of landslide grids to the number of non-landslide grids, judging whether the ratio is in a set balance interval, if so, indicating that the number of landslide grids corresponding to the initial size is balanced with the number of non-landslide grids, and taking the initial size as a target size; if the rainfall error is not less than the set error threshold, the predicted rainfall of the rainfall station in the grids with the initial size is inaccurate, the size of each grid needs to be adjusted, the rainfall error is recalculated according to the rainfall predicted value and the actually measured rainfall value of the rainfall station in the grids with the adjusted grid size until the recalculated rainfall error is less than the set error threshold, if the ratio of the redetermined landslide grid number to the redetermined non-landslide grid number is also in the set balance interval and the ratio of the current grid number to the initial grid number is less than the set threshold, the actually measured rainfall of the rainfall station of the adjusted grid is accurate, the landslide grid number corresponding to the adjusted grid size is balanced with the non-landslide grid number, and the adjusted total grid number is balanced, and the size of the adjusted grid is taken as the target size.
According to the embodiment, the landslide grid number and the non-landslide grid number are determined in the mode, so that the target size of the grids is determined according to actual landslide information and rainfall, and further the landslide influence factor has the best characterization effect under the target size, and the prediction accuracy of the landslide occurrence probability is improved.
S120, under the target size, acquiring landslide influence factors of each grid of the target area every day in a set time period, and determining occurrence probability of landslide every day according to the landslide influence factors based on a first preset model.
Wherein the set time period may be one day, three days, one week, one month, or other time period. Optionally, the landslide impact factor comprises a dynamic factor and a static factor, wherein the dynamic factor comprises at least one of rainfall and soil moisture, and the static factor comprises at least one of elevation, slope direction, planar curvature, profile curvature, terrain moisture index, water current intensity index, sediment transport index, terrain roughness index, distance to fault, distance to river, distance to road, lithology, land utilization, and vegetation coverage. Specifically, a plurality of monitoring points can be set in the target area so as to acquire landslide influence factors of the monitoring points in real time. And further combining data collected by a preset department to form a landslide impact factor of each grid of the target area every day within a set time period.
The first preset model may be a neural network model, or other learning algorithm. By way of example, the first preset model may be a support vector machine algorithm (Support Vector Machine, SVM), a Long Short Term Memory Network (LSTM), a logistic regression model (Logistics Regression, LR), an XGBoost (Extreme Gradient Boosting, extreme gradient boost decision tree) algorithm, a GBDT (Gradient Boosting Decision Tree, gradient boost decision tree) algorithm, a full convolution Network (Fully Convolutional Networks, FCN), a cyclic convolution Network (Recurrent Neural Network, RNN), a Residual Network (ResNet), a gated loop unit (Gate Recurrent Unit, GRU), and the like.
Specifically, the training process of the first preset model is as follows:
basic information of landslide of a target area or all areas is extracted from files such as landslide field investigation reports, typical landslide monitoring reports and the like, the basic information comprises landslide information such as landslide occurrence time, longitude and latitude, disaster scale and the like, landslide influence factors of historical time periods of various landslide occurrence lands are determined according to the landslide information, and a training set and a verification set are formed according to set proportion, for example, 8:2 or 7:3; and carrying out data correction and registration on each landslide influence factor, carrying out coordinate unified processing and grid unified processing, thus obtaining landslide influence factors with consistent grid sizes, and training a first preset model by using the landslide influence factors processed by the steps. The landslide influence factors are characterized by extracting characteristics of the landslide influence factors through characteristic engineering to form an input characteristic matrix of a first preset model; initializing parameters of a first preset model, inputting the input feature matrix into the first preset model, performing model training to obtain historical landslide information of a historical time period, adjusting the parameters of the first preset model according to an evaluation result based on an F1-value (F1-Score) and an ROC (Receiver Operating Characteristic) as evaluation indexes, performing model verification through a verification set when the parameters are met, and obtaining a trained first preset model after verification. Wherein the historical landslide information includes a probability of each grid landslide occurrence.
Further, after obtaining the landslide impact factor, the method further comprises: and determining a first feature matrix of a first preset model according to the landslide influence factor. Specifically, a feature set of a first preset model can be constructed according to landslide influence factors, feature selection is performed on the feature set according to a random forest algorithm, and therefore an input feature matrix of the first preset model is obtained, and a first feature matrix is obtained.
It can be understood that the landslide data amount in the landslide influence factors is far smaller than the non-landslide data amount, when the occurrence probability prediction of landslide is performed, the landslide influence factors at different time points may be included in the same grid, and if the landslide influence factors at different time points in the same grid are simultaneously input into the first prediction model, the utilization rate of the landslide influence factors is low. In order to improve the utilization rate of the landslide influence factor, after the occurrence probability of the landslide every day is determined according to the landslide influence factor based on the first preset model, the method further comprises the following steps: if the same grid comprises landslide influence factors of a plurality of time points, at least one landslide influence factor is reserved in the same grid, and the unreserved landslide influence factors are input into the first preset model again to determine the occurrence probability of the daily landslide. In this way, a plurality of landslide impact factors in the same grid can be utilized to the maximum extent, and all landslide impact factors of the landslide grid can be ensured not to be missed.
S130, if the occurrence probability of the landslide of the current day is larger than a preset probability threshold, predicting the occurrence probability of the landslide of each grid based on a second preset model according to the landslide influence factors of each grid of the current day.
The preset probability threshold may be 0.5, 0.6 or other values, and of course, the preset probability threshold may also be represented by a fraction or a percentage. When the occurrence probability of landslide is larger than the preset probability threshold value, the landslide is high in probability.
The landslide influence factors are screened by setting the preset probability threshold value, and the data of the current day is transmitted to the second preset model for further prediction only when the occurrence probability of the landslide of the current day is larger than the preset probability threshold value, so that the data quantity input by the model is greatly reduced, the processing efficiency is improved, and meanwhile, the prediction accuracy is improved.
The second preset model may be a convolutional neural network, such as a one-dimensional convolutional neural network, a one-dimensional residual neural network, a deep neural network (Deep Neural Networks, DNN), a full convolutional network, a distributed gradient lifting framework (Light Gradient Boosting Machine, lightGBM) based on a decision tree algorithm, an adaptive iterative algorithm (Adaptive Boosting, adaboost), an iterative algorithm (SMOTEboost) based on SMOTE (Synthetic Minority Oversampling Technique, a minority-class oversampling technique), a balancecam algorithm, and the like.
Specifically, the number of the current days may be 1 or more, and needs to be determined according to the determination result of S130. And when the landslide occurrence probability of a certain day is greater than a preset probability threshold value, discretization prediction data or an input feature matrix of landslide preset data corresponding to the certain day is sent to a second preset model so as to predict landslide transmission probability of each grid of the certain day.
Specifically, the training process of the second preset model is as follows:
the training set and the verification set are obtained in the same way as the first preset model, and the training mode of the second preset model is the same as the training mode of the first preset model. In order to improve the accuracy of the model, the time period selected by the training data is the time period in which no landslide occurs and rainfall events exist, so that the defect that landslide prediction is performed only according to rainfall is effectively avoided, and the accuracy of the model is enhanced. And respectively training and verifying the second preset model according to the training set and the verification set, thereby obtaining a trained second preset model. Specifically, the second preset model may be evaluated by using the grid classification accuracy and ROC (Receiver operating Characteristic) as evaluation indexes, where the grid classification accuracy is used to represent the probability that the grid classification is correct, and specifically, the ratio of the number of grids with correct classification to the total number of grids may be used to represent the probability.
Further, the parameters of the first preset model and the second preset model can be optimized based on a Bayesian optimization algorithm.
According to the technical scheme, the landslide range and the landslide quantity in the landslide range are determined according to the longitude and latitude information of the target area, the target size of each grid of the target area is determined based on the landslide quantity, the target size of the grids is determined according to the actual landslide information, the landslide influence factor is enabled to have the best characterization effect under the target size, and the redundancy of data is reduced; landslide prediction is performed based on landslide influence factors, so that the accuracy of data is improved, prediction is performed by taking a day as a unit, time sequence information of dynamic factors is fully considered, and the prediction accuracy is improved; by setting two preset models, landslide prediction is carried out in two stages, whether the probability of landslide occurrence in the current day is larger than a set value or not is predicted in the first stage, if so, landslide prediction in the second stage is carried out, and the landslide occurrence probability of a specific grid is carried out by taking the data in the current day as a reference, so that the waste of calculation resources is greatly reduced, and the landslide prediction efficiency is improved.
Example two
Fig. 2 is a flowchart of a landslide prediction method according to a second embodiment of the present invention, where the previous embodiment is further refined. Optionally, the determining, based on the first preset model, the occurrence probability of the landslide every day according to the landslide influence factor includes: calculating construction characteristics of the dynamic factors of each grid every day, wherein the construction characteristics comprise the sum, average value, maximum value, minimum value, range, quartile and rainfall time of each dynamic factor of each grid, and obtaining a first characteristic matrix of the first preset model according to the construction characteristics and the static factors every day; and inputting the first feature matrix into the first preset model to determine the occurrence probability of landslide every day according to the first preset model. For parts which are not described in detail in this method embodiment, reference is made to the above-described embodiments. Referring specifically to fig. 2, the method may include the steps of:
S210, acquiring landslide information of the target area, and determining the target size of each grid of the target area based on the landslide information.
S220, under the target size, acquiring landslide influence factors of each grid of the target area every day in a set time period, calculating the construction characteristics of the dynamic factors of each grid every day, and obtaining a first characteristic matrix of a first preset model according to the construction characteristics and the static factors every day.
Wherein the construction features include a sum, an average, a maximum, a minimum, a range, a quartile, and a rain time for each dynamic factor of the respective grid. The quartiles include an upper quartile and a lower quartile, and the architectural features may also include median or other feature values. The rainfall time may be a daily rainfall time, or may be a rainfall time of a finer period.
Similar to the previous embodiments, the landslide impact factors include dynamic factors including rainfall (precipitation) and soil humidity, and static factors including elevation, slope direction, planar curvature, profile curvature, terrain humidity index, water current intensity index, sediment transport index, terrain roughness index, distance to fault, distance to river, distance to road, lithology, land utilization, vegetation coverage, and the like.
Illustratively, the landslide impact factor X of each grid acquired daily is: x= { X (1) ,X (2) ,…,X (d) And, wherein the superscript indicates the number of days, X (c) C=1, 2, …, d, representing the landslide impact factor of each grid in the target area on day c, where matrix X (c) The rows of (1) represent a grid, the columns represent landslide factors, and the matrix X (c) Is m x n, i.e. comprises m grids, n landslide factors, where i=1, 2, … n 1 Represents a dynamic factor (rainfall, soil humidity, etc.), i=n 1 +1,n 1 +2, … n, represent the statics factor. Constructing feature set X in days 1 (first feature matrix) constructed as follows: for each X (c) Calculating n by grid 1 The sum, average, maximum, minimum, range, upper quartile, lower quartile and other structural features of each dynamic factor are determined by the structural features and static factorsSub-obtaining feature set X 1 . For training data, a tag vector Y may also be constructed 1 To indicate whether or not a landslide has occurred every day, wherein 1 indicates that a landslide has occurred and 0 indicates that no landslide has occurred.
S230, inputting the first feature matrix into the first preset model to determine the occurrence probability of landslide every day according to the first preset model.
Optionally, before the first feature matrix is input to the first preset model, normalization processing may be further performed on the first feature matrix, and feature screening may be performed on the first feature matrix based on a random forest algorithm.
Specifically, the first preset model is a support vector machine (Support Vector Machine, SVM) model, wherein the parameters of the SVM include a kernel function type, a penalty coefficient and a kernel function coefficient γ, wherein the kernel function type includes RBF (Radial Basis Function, gaussian kernel function), linear (Linear kernel function), sigmoid kernel function, polynominal (Polynomial kernel function), and the value range of the Polynomial kernel function is 2 -8 ～2 8 The value range of gamma is 2 -8 ～2 8 。
S240, if the occurrence probability of the landslide on the current day is larger than a preset probability threshold, feature extraction is performed on landslide influence factors of grids corresponding to the current day, so that a second feature matrix of a second preset model is generated.
Specifically, assuming that the landslide occurrence probability on the c-th day is greater than the prediction probability threshold, the landslide influence factor X on the c-th day (c) The feature set is sent to a second preset model, and the feature set of the second preset model is constructed in the specific construction mode that: and calculating the construction characteristics of the dynamic factors of each grid on the day, such as the corresponding sum, the mean, the variance, the median, the mean and the variance of the difference, the skewness, the kurtosis and the like, wherein the total sum, the mean, the variance, the median, the mean and the variance of the difference are respectively calculated on 3 days, 7 days, 15 days and 30 days, and the kurtosis is the early rainfall index calculated based on the rainfall attenuation index. Meanwhile, consider the case of each grid within a set range centered on the current grid, such as 3*3 area, including whether the current grid is the maximum or minimum within the set range, whether the average value corresponding to the set range is exceeded, and setting Whether the range of grids have landslide or not, the number of times of landslide occurrence of the current grids and the like, and finally obtaining a second feature matrix X of a second preset model 2 fea 。
S250, carrying out normalization processing on the second feature matrix.
Specifically, the second feature matrix X can be normalized based on max-min 2 fea Is normalized. Of course, other normalization algorithms can be selected for normalization processing. The embodiment of the invention does not limit the normalization algorithm of the first feature matrix and the second feature matrix.
Further, after the normalization process, the method further includes:
feature selection is carried out on the second feature matrix based on a multiple collinearity method so as to carry out feature selection according to the collinearity degree, and a screened second feature matrix is obtained
S260, inputting the normalized second feature matrix into a second preset model to determine the occurrence probability of each grid landslide in the current day according to the second preset model.
Specifically, if the feature selection is performed, the above steps are replaced by: the second feature matrix after screeningAnd inputting the second preset model to determine the occurrence probability of each grid landslide on the current day according to the second preset model.
Specifically, the second preset model can be a one-dimensional convolutional neural network model, and comprises a convolutional layer, a batch standardization layer, an activation function and an optimization layer, wherein the value range of the number of convolutional kernels of the convolutional layer is 32-512, and the step length is 16; the activation function may include any one of a ReLU function (Rectified Linear Unit, linear rectification function), a Linear function (Linear function), a Sigmoid function, and a Tanh function (hyperbolic function); the optimization method comprises any one of SGD (Stochastic Gradient Descent, gradient update rule), adam (Adaptive Moment Estimation ), nadam (Nesterov Adaptive Moment Estimation, nesterov acceleration adaptive moment estimation), adagard (Adaptive Gradient Algorithm ), RMSprop (Root Mean Square Prop, forward root mean square gradient descent algorithm) and other optimization algorithms; the initial learning rate may take a value of 0.0001, 0.001, 0.01, or 0.1; the value range of the number of the hidden layer neurons is 4-256, the step length is 4, the value range of the number of the hidden layer neurons is 3-8, and the step length is 1; the random discarding rate is 0-0.8, and the step length is 0.05.
According to the technical scheme, the landslide range and the landslide quantity in the landslide range are determined according to longitude and latitude information of the target area, the target size of each grid of the target area is determined based on the landslide quantity, the target size of the grids is determined according to actual landslide information, and therefore the landslide influence factor has the best characterization effect under the target size; further acquiring landslide influence factors, particularly predicting dynamic factors in landslide by taking a day as a unit, fully considering time sequence information of the dynamic factors, and improving prediction accuracy; by setting two preset models, carrying out landslide prediction in two stages, wherein the probability of landslide occurrence in the first stage is larger than a set value, if so, carrying out landslide prediction in the second stage, and carrying out landslide occurrence probability of a specific grid by taking data in the current day as a reference, thereby greatly reducing the waste of calculation resources and improving the landslide prediction efficiency; by constructing the dynamic factor characteristics, the function of the dynamic factor in model prediction is increased, and the accuracy of model prediction is improved; and through feature screening and normalization processing, the efficiency of model prediction is improved. Meanwhile, parameter optimization is respectively carried out on the two models, so that the quality of the models is improved, and the accuracy of prediction is ensured.
Example III
Fig. 3 is a flowchart of a landslide prediction method according to a third embodiment of the present invention. The embodiment is used for analyzing the whole landslide prediction flow. For parts which are not described in detail in this method embodiment, reference is made to the above-described embodiments. Referring specifically to fig. 3, the method may include the steps of:
s310, acquiring landslide information of the target area, and determining the target size of each grid of the target area based on the landslide information.
Optionally, before determining the target size, remote sensing image information and terrain information of the target area can be obtained, each functional area of the target area is determined based on the functional area classification model according to the remote sensing image information and the terrain information, and the target functional area is screened according to the labels corresponding to each functional area.
The remote sensing image information refers to satellite images and can be obtained through land reflectivity products of Landsat (terrestrial satellite) series, and can reflect the land feature types of grids of a target area, such as grassland types, woodland types, rice types and building types; the terrain information may be obtained from a digital elevation model (Digital Elevation Model, DEM) of the target area, reflecting the maximum degree of variation of the grid information of a certain grid and surrounding grids of the target area. The terrain information may include elevation information, gradient information, curvature information, etc., which may reflect a terrain feature Gao Chengdian of a certain grid of the target area, elevation points near an important geographic target, or key elevation points on a distribution range, the gradient information may reflect a degree of steepness of a certain grid of the target area, and the curvature information may reflect a concave-convex condition of a certain grid of the target area.
Optionally, the method for determining each functional area of the target area includes: preprocessing the remote sensing image information, wherein the preprocessing comprises at least one of atmospheric correction, radiation correction and edge detection processing; and respectively extracting features of the preprocessed remote sensing image information and the preprocessed topographic information, inputting the features of the remote sensing image and the features of the topographic information into the functional area classification model, and determining each functional area of the target area, wherein the features of the remote sensing image comprise the mean value, the maximum value and the minimum value of the remote sensing image information, the topographic information comprises the elevation information, the gradient information and the curvature information, and the features of the topographic information comprise the mean value, the maximum value and the minimum value of the elevation information, the gradient information and the curvature information.
The functional area classification model is a random forest model, and the training method of the functional area classification model comprises the following steps: acquiring an initial forest model; sample remote sensing image information and sample terrain information of each functional area in a historical time period are input into the initial forest model, and a prediction label and the probability of the prediction label of each functional area are determined; and adjusting parameters of the initial forest model based on the prediction label, the probability of the prediction label and the sample label of each functional area until the prediction label is consistent with the sample label and the probability of the prediction label reaches a set threshold value, so as to obtain the functional area classification model. The probability refers to the probability of a predicted label of the output of the initial forest model, parameters of the initial forest model are adjusted through iteration, if the predicted label is consistent with the sample label and the predicted label probability reaches a set threshold, the probability that the predicted label is the sample label is high, and the initial forest model under the iteration number is used as a functional class classification model. The set threshold may be a larger value, for example, the set threshold takes a value of 0.9. Optionally, before the sample remote sensing image information is input into the initial forest model, the sample remote sensing image information may be preprocessed, for example, atmospheric correction, radiation correction, edge detection, false color synthesis processing, etc. are performed on the sample remote sensing image information, so as to improve the training accuracy of the functional area classification model of the sample remote sensing information.
It can be understood that each functional area output by the functional area classification model comprises all functional areas of a target area, including functional areas of town buildings, urban green lands, water bodies, farmlands, bare soil, mountain forests and the like, and landslide occurrence probabilities of the functional areas are different. For example, urban buildings and urban green lands have extremely low probability of landslide, and water bodies and mountain forests have relatively high probability of landslide. By comparing the historical prediction probability corresponding to each functional area with the historical probability threshold, the target functional area is screened out, and the target functional area is subjected to targeted landslide prediction, so that the calculated amount can be reduced, and the landslide prediction efficiency of the target area is improved.
S320, under the target size, acquiring landslide influence factors of each grid of the target area every day in a set time period, calculating the construction characteristics of the dynamic factors of each grid every day, and obtaining a first characteristic matrix of a first preset model according to the construction characteristics and the static factors every day.
If each functional area of the target area is determined in S310 and the target functional area is screened according to the label corresponding to each functional area, S320 may be replaced by: and under the target size, acquiring landslide influence factors of each grid of the target functional area of the target area every day in a set time period, calculating the construction characteristics of the dynamic factors of each grid every day, and obtaining a first characteristic matrix of a first preset model according to the construction characteristics and the static factors every day.
Wherein the dynamic factor includes at least one of rainfall and soil humidity. The rainfall can be determined by inputting the obtained geographical environment data of the target area into a rainfall interpolation model. The geographic environment data includes geographic location data, atmospheric data, terrain data, and underlying data. The rainfall interpolation model can be a back propagation model (BP, back propagation neural network), and is a multi-layer feedforward network consisting of nonlinear transformation units based on an error back propagation algorithm, wherein the BP generally consists of an input layer, an implicit layer and an output layer, each layer further comprises N neurons, the neurons at the same layer are mutually independent, and the output of the neurons between each layer only affects the input of the neurons at the lower layer after a specific excitation function.
Specifically, the training method of the rainfall interpolation model comprises the following steps: acquiring an initial model, determining an initial weight matrix and an initial threshold value of the initial model, and calculating the fitness of the initial model; sample geographic environment information in a historical time period is input into the initial model, predicted rainfall is determined, and according to the predicted rainfall and the actually measured rainfall in the historical time period, an initial weight matrix and an initial threshold value of the initial model are iteratively adjusted based on a genetic algorithm; and adjusting the initial model and calculating the fitness of the adjusted model based on the iteratively adjusted weight matrix threshold value until the recalculated fitness reaches an expected value, and taking the model corresponding to the fitness reaching the expected value as the rainfall interpolation model.
Specifically, the initial weight matrix may include a connection weight between an input layer and an implicit layer and a connection weight between the implicit layer and an output layer; the initial threshold may include a threshold of an hidden layer and a threshold of an output layer. The fitness of the initial model is calculated by the following formula:where n is the number of samples, y (i) is the actual output of the ith sample of the initial model, and t (i) is the expected output of the ith sample of the initial model. Specifically, sample geographic environment information in a historical time period is input into an initial model to obtain a predicted rainfall, a root mean square error between the predicted rainfall and an actually measured rainfall is calculated, if the root mean square error is larger than a preset error threshold, weight coding and threshold coding of the initial model are iteratively adjusted, an adjusted weight matrix and a threshold are determined based on the adjusted weight coding and threshold coding until the root mean square error is not smaller than the preset error threshold, the initial model is adjusted to be in a stable state, the fitness of the adjusted model is calculated by adopting the fitness calculation formula, and if the recalculated fitness reaches a desired value, a model corresponding to the fitness reaching the desired value is used as the rainfall interpolation model.
Optionally, the weight and the threshold are coded by adopting a binary coding, real number coding or Gray code coding mode, and the adjusted weight matrix and the adjusted threshold are determined according to the weight coding and the threshold coding. Optionally, the initial model may be adjusted to a stable state by calculating an average error, an average absolute error or a linear correlation coefficient between the predicted rainfall and the actually measured rainfall, and iteratively adjusting the weight code and the threshold code of the initial model according to the average error, the average absolute error or the linear correlation coefficient until the average error, the average absolute error or the linear correlation coefficient is not less than a preset error threshold, and further calculating the fitness of the adjusted model and determining the rainfall interpolation model.
It should be noted that, the sample geographic environment data in the historical time period is multidimensional, and when the rainfall interpolation model is trained, the precision and the robustness of the rainfall interpolation model can be improved by the multidimensional sample geographic environment data, so that after the daily geographic environment data is obtained, the prediction precision of the rainfall probability can be improved based on the rainfall interpolation model prediction, and the prediction precision of landslide prediction can be improved.
In this embodiment, the target size may be obtained by adjusting the initial size multiple times, and the target size may be used as the first grid size. In order to improve accuracy of landslide prediction, resampling can be performed on the first grid size, and landslide prediction is performed by combining characteristic information of the resampled grid size. The specific method comprises the following steps: acquiring a first grid size of a target area, determining a second grid size after resampling the first grid size, dividing an initial grid corresponding to the second grid size into a plurality of grids according to the first grid size, extracting characteristic information of landslide influence factors under the grids of the second grid size based on the first grid size, and inputting the characteristic information and the landslide influence factors into a first preset model and a second preset model to conduct landslide prediction. The feature information extraction method comprises the following steps: acquiring characteristic values of landslide impact factors in eight neighbor grids of the current grid; and determining the characteristic information of the current grid according to the characteristic values in the eight-neighborhood grid.
According to the embodiment, the occurrence probability of each grid landslide is determined by combining the characteristic information and the landslide influence factors, the data size of the landslide influence factors can be increased, the occurrence probability of each grid landslide can be determined more accurately according to the characteristic information, and the accuracy of landslide prediction is improved.
S330, inputting the first feature matrix into the first preset model to determine the occurrence probability of landslide every day according to the first preset model.
Unlike the previous embodiments, the first preset model may be a self-classifying learning model that iteratively adjusts a self-step factor and a downsampling ratio determination based on historical landslide data, historical non-landslide data. The self-step factors are determined according to the number of sub-boxes and the iteration times of the historical non-landslide data, and the downsampling proportion is determined according to the self-step factors of each sub-box and the self-step factors of all sub-boxes. Optionally, the method for determining the historical landslide data and the historical non-landslide data includes: determining labels corresponding to grids of a history area, and determining historical rainfall and specific correlation factors corresponding to landslide grids and non-landslide grids at each history time point respectively; and generating a landslide data set comprising the historical rainfall and the specific correlation factor respectively corresponding to the landslide grids and the non-landslide grids based on the labels, and determining the historical landslide data and the historical non-landslide data according to the labels corresponding to the grids in the landslide data set.
Optionally, the method for determining the historical landslide data and the historical non-landslide data includes: determining labels corresponding to grids of a history area, and determining historical rainfall and specific correlation factors corresponding to landslide grids and non-landslide grids at each history time point respectively; and generating a landslide data set comprising the historical rainfall and the specific correlation factor respectively corresponding to the landslide grids and the non-landslide grids based on the labels, and determining the historical landslide data and the historical non-landslide data according to the labels corresponding to the grids in the landslide data set.
Optionally, the training method of the first preset model includes: acquiring an initial prediction model, and acquiring the rainfall of a landslide grid and the rainfall of a non-landslide grid at each historical landslide time point, and specific correlation factors under each label; and inputting the rainfall of the landslide grids at the historical landslide time points, the rainfall of the non-landslide grids and the specific correlation factors under the labels into the initial prediction model according to the days, and adjusting a loss function of the initial prediction model based on the landslide probability output by the initial prediction model and the landslide probability corresponding to the historical landslide time points until the loss function reaches a set threshold value to obtain the first preset model.
The step of adjusting the loss function of the initial prediction model based on the landslide probability output by the initial prediction model and the landslide probability corresponding to the historical landslide time point until the loss function reaches a set threshold value, and obtaining the first preset model comprises the following steps: determining an initial classification hardness of the initial prediction model based on a rainfall of the landslide grid at the historical landslide time point, a rainfall of the non-landslide grid, each of the labels and a specific correlation factor under each of the labels; determining the rainfall capacity of the non-landslide grids and the number of the sub-boxes of the specific correlation factors of the non-landslide grids according to the initial classification hardness, and determining the self-step factors of the sub-boxes of the initial prediction model based on the number of the sub-boxes; determining a downsampling proportion of each sub-box based on the self-step factors, determining the rainfall capacity of downsampled non-landslide grids in each sub-box based on the downsampling proportion, and determining specific correlation factors of the downsampled non-landslide grids; inputting the rainfall of the downsampled non-landslide grids, the specific correlation factors of the downsampled non-landslide grids and the rainfall of the landslide grids into the initial prediction model, determining the loss function based on the landslide probability output by the initial prediction model and the landslide probability corresponding to the historical landslide time point, and iteratively adjusting the self-walking factors and the downsampling proportion of each sub-bin based on the training classification hardness of the loss function on a single sample; and adjusting the initial prediction model according to the self-step factors adjusted in an iteration mode and the downsampling proportion of each sub-bin until the loss function reaches a set threshold value, and obtaining the first preset model.
The downsampling proportion of each sub-box is the ratio of the self-step factor of each sub-box to the self-step factors of all sub-boxes, and the self-step factors are obtained by adding 1 to the sum of the number of sub-boxes, the opposite number of sub-box labels and the iteration number.
The landslide prediction method based on the self-step classification learning model has the advantages that: the self-step factors of the self-step classification learning model are determined according to the number of sub-boxes of the historical non-landslide data and the iteration times, and the down-sampling proportion is determined according to the self-step factors of each sub-box and the self-step factors of all sub-boxes, so that when the self-step factors and the down-sampling proportion are adjusted based on the historical landslide data and the historical non-landslide data in an iteration way, the sampling proportion of each sub-box gradually changes uniformly from the lower down-sampling number of the low hardness to the last lower down-sampling number of each sub-box along with the increase of the iteration times, and the lower down-sampling proportion of each box is uniformly changed, and meanwhile, the lower down-sampling number of the sub-boxes with small hardness can be always higher than the lower down-sampling number of the sub-boxes with large hardness, so that the first preset model with high diversity, robustness and strong inclusion is obtained.
And S340, if the occurrence probability of the landslide on the current day is larger than a preset probability threshold, extracting features of landslide influence factors of grids corresponding to the current day to generate a second feature matrix of a second preset model.
S350, carrying out normalization processing on the second feature matrix.
S360, inputting the normalized second feature matrix into a second preset model to determine the occurrence probability of each grid landslide in the current day according to the second preset model.
Alternatively, the second preset model may be a self-organizing learning model. The determination manner of the historical landslide data for training the second preset model and the training method of the second preset model can refer to S330, and this step will not be described in detail.
Optionally, after determining the occurrence probability of each grid landslide in the current day, the occurrence probability of each grid in the current day may be compared with an early warning threshold of the target area, and the occurrence level of the landslide in the target area is determined based on the obtained early warning level corresponding to the occurrence probability of each grid, where the early warning threshold is determined according to the first coefficient and the second coefficient of the grid landslide in each area.
The method for determining the first coefficient and the second coefficient comprises the following steps: acquiring historical probability of landslide occurrence in a set time period of grid landslide of each area; determining a first determined probability interval and a second determined probability interval based on the distribution characteristics of the historical probabilities; respectively calculating the sum of products of probability threshold values and corresponding coefficients of the same risk level in the first determined probability interval and the second determined probability interval to obtain intermediate determined probabilities; and if the intermediate determined probability does not reach the set evaluation index, iteratively adjusting the corresponding coefficient of the probability threshold value of each risk level until the intermediate determined probability reaches the set evaluation index, and determining a first coefficient and a second coefficient according to the corresponding coefficient reaching the set evaluation index.
The method for determining the early warning threshold value comprises the following steps: determining a first prediction probability interval and a second prediction probability interval based on the distribution characteristics of occurrence probability of each grid landslide; calculating a first product of each risk level in the first prediction probability interval and the first coefficient, calculating a second product of each risk level in the second prediction probability interval and the second coefficient, and taking the sum of the first product and the second product as an early warning threshold value of each risk level.
Wherein the determining the first determined probability interval and the second determined probability interval based on the distribution characteristics of the historical probabilities comprises: determining the historical probability and the times corresponding to the historical probability; determining the density distribution characteristics and breakpoint distribution characteristics of the historical probabilities according to the historical probabilities and the landslide occurrence times corresponding to the historical probabilities; the first determined probability interval is determined based on the intensity distribution characteristics and the second determined probability interval is determined based on the breakpoint distribution characteristics.
It can be understood that the above manner is based on the historical probability of the landslide occurring in the set time period of the grid landslide of each region, the first determined probability interval and the second determined probability interval are determined according to the distribution characteristics of the historical probability, the first coefficient and the second coefficient of each region are determined according to the probability threshold value of each risk level in the first determined probability interval and the second determined probability interval and the set evaluation index, and after the occurrence probability of different regions is obtained, the early warning threshold value of the different regions can be flexibly determined according to the first coefficient, the second coefficient and the occurrence probability corresponding to the different regions, so that the risk level of the region can be accurately determined according to the early warning threshold value corresponding to each region.
The determining the occurrence level of the landslide of the target area based on the obtained early warning level corresponding to the occurrence probability of each grid comprises the following steps: screening target grades larger than a first grade in the early warning threshold; calculating the average grade of the grids corresponding to the target grade; and determining the occurrence level of the landslide of the target area based on the average level, the set coefficient and the ratio of the grid number corresponding to the target level to all the grid numbers in the target area.
The calculation formula of the occurrence level of the landslide of the target area is as follows:
wherein alpha is a set coefficient, and is obtained by performing Bayesian calculation on the historical occurrence level of the landslide of the target area,and p is the ratio of the number of grids corresponding to the target grade to the number of all grids in the target zone.
Example IV
Fig. 4 is a schematic diagram of the result of a landslide prediction device according to a fourth embodiment of the present invention, where, as shown in fig. 4, the landslide prediction device includes: a grid sizing module 410, a first landslide prediction module 420, and a second landslide prediction module 430.
The grid size determining module 410 is configured to obtain landslide information of a target area, and determine a target size of each grid of the target area based on the landslide information, where the landslide information includes a landslide position and a landslide area;
A first landslide prediction module 420, configured to obtain, at the target size, a landslide impact factor of each grid of the target area during a set period of time, and determine, based on a first preset model, an occurrence probability of a landslide of each day according to the landslide impact factor;
and a second landslide prediction module 430, configured to predict, based on a second preset model, the occurrence probability of each grid landslide according to the landslide influence factor of each grid on the current day if the occurrence probability of the landslide on the current day is greater than the preset probability threshold.
According to the technical scheme, landslide information of the target area is obtained, the target size of each grid of the target area is determined based on the landslide information, so that the target size of each grid is determined according to actual landslide information, and further, the landslide influence factors have the best characterization effect under the target size, landslide prediction is carried out based on the landslide influence factors of each grid every day in a set time period, the accuracy of data is improved, prediction is carried out by taking days as a unit, the time sequence information of dynamic factors is fully considered, and the prediction accuracy is improved; by setting two preset models, landslide prediction is carried out in two stages, whether the probability of landslide occurrence in the current day is larger than a set value or not is predicted in the first stage, if so, landslide prediction in the second stage is carried out, and the landslide occurrence probability of a specific grid is carried out by taking the data in the current day as a reference, so that the waste of calculation resources is greatly reduced, and the landslide prediction efficiency is improved.
Optionally, the grid size determining module 410 is further configured to determine an initial size and an initial grid number of each grid of the target area, and determine a landslide grid number and a non-landslide grid number according to the landslide information, where the landslide grid number is a grid number where landslide occurs, and the non-landslide grid number is a grid number where landslide does not occur;
if the ratio of the landslide grid number to the non-landslide grid number is in a set balance interval, taking the initial size as the target size;
if the ratio of the landslide grid number to the non-landslide grid number is not in the set balance interval, adjusting the size of each grid of the target area, and re-determining the landslide grid number, the non-landslide grid number and the current grid number;
and if the ratio of the redetermined landslide grid number to the non-landslide grid number is in the set balance interval and the ratio of the current grid number to the initial grid number is smaller than a set threshold value, taking the size of each grid after adjustment as the target size.
Optionally, the grid size determining module 410 is further configured to obtain rainfall data of each rainfall station in the target area, an initial size and an initial grid number of each grid in the target area, and determine a landslide grid number and a non-landslide grid number according to the landslide information, where the landslide grid number is a grid number where landslide occurs, and the non-landslide grid number is a grid number where no landslide occurs;
Calculating rainfall predicted values of the rainfall stations corresponding to any current grid according to rainfall data of the rainfall stations with a specific number, and calculating rainfall errors based on the rainfall predicted values and actually measured rainfall values of the rainfall stations corresponding to the current grid;
if the rainfall error is smaller than a set error threshold, and the ratio of the landslide grid number to the non-landslide grid number is in a set balance interval, taking the initial size as the target size;
if the rainfall error is larger than a set error threshold, adjusting the size of each grid of the target area, and recalculating the rainfall error according to the rainfall predicted value and the actually measured rainfall value of the rainfall station corresponding to any grid after adjustment;
and if the recalculated rainfall error is smaller than the set error threshold value, and the ratio of the redetermined landslide grid number to the redetermined non-landslide grid number is in a set balance interval, taking the size of the grid after adjustment as the target size.
Optionally, the apparatus further comprises: a landslide impact factor selection module; the landslide influence factor selection module is used for reserving at least one landslide influence factor in the same grid if the landslide influence factors at a plurality of time points are included in the same grid, and re-inputting the landslide influence factors which are not reserved into the first preset model so as to determine the occurrence probability of the daily landslide.
Optionally, the landslide impact factor comprises a dynamic factor and a static factor, wherein the dynamic factor comprises at least one of rainfall and soil humidity, and the static factor comprises at least one of elevation, slope direction, planar curvature, profile curvature, terrain humidity index, water current intensity index, sediment transport index, terrain roughness index, distance to fault, distance to river, distance to road, lithology, land utilization, and vegetation coverage.
Optionally, the first landslide prediction module 420 is further configured to calculate a construction feature of the dynamic factors of each grid of each day, where the construction feature includes a sum, an average, a maximum, a minimum, a range, a quartile, and a rainfall time of each dynamic factor of each grid, and obtain a first feature matrix of the first preset model according to the construction feature and the static factors of each day;
and inputting the first feature matrix into the first preset model to determine the occurrence probability of landslide every day according to the first preset model.
Optionally, the second landslide prediction module 430 is further configured to perform feature extraction on landslide impact factors of each grid corresponding to the current day, so as to generate a second feature matrix of the second preset model;
Normalizing the second feature matrix;
and inputting the normalized second feature matrix into the second preset model to determine the occurrence probability of each grid landslide in the current day according to the second preset model.
The landslide prediction device provided by the embodiment of the invention can execute the landslide prediction method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example five
Fig. 5 is a schematic structural diagram of a landslide prediction apparatus according to a fifth embodiment of the present invention, where, as shown in fig. 5, the apparatus includes a processor 510, a memory 520, an input device 530, and an output device 540; the number of device processors 510 may be one or more, one processor 510 being illustrated in fig. 5; the processor 510, memory 520, input means 530 and output means 540 in the device may be connected by a bus or other means, for example in fig. 5.
The memory 520 is a computer readable storage medium, and may be used to store a software program, a computer executable program, and modules, such as program instructions/modules corresponding to the landslide prediction method in the embodiment of the present invention (for example, the grid size determining module 410, the first landslide prediction module 420, and the second landslide prediction module 430 in the landslide prediction device). The processor 510 executes various functional applications of the apparatus and data processing, i.e., implements the landslide prediction method described above, by running software programs, instructions, and modules stored in the memory 520.
Memory 520 may include primarily a program storage area and a data storage area, wherein the program storage area may store an operating system, at least one application program required for functionality; the storage data area may store data created according to the use of the terminal, etc. In addition, memory 520 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. In some examples, memory 520 may further include memory located remotely from processor 510, which may be connected to the device/terminal/server via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input means 530 may be used to receive input numeric or character information and to generate key signal inputs related to user settings and function control of the device. The output 540 may include a display device such as a display screen.
Example six
A sixth embodiment of the invention also provides a storage medium containing computer-executable instructions, which when executed by a computer processor, are for performing a method of predicting a landslide, the method comprising:
Acquiring landslide information of a target area, and determining the target size of each grid of the target area based on the landslide information, wherein the landslide information comprises landslide positions and landslide areas;
under the target size, acquiring a landslide influence factor of each grid of the target area every day in a set time period, and determining the occurrence probability of the landslide every day according to the landslide influence factor based on a first preset model;
and if the occurrence probability of the landslide on the current day is larger than the preset probability threshold value, predicting the occurrence probability of the landslide of each grid based on a second preset model according to the landslide influence factors of each grid on the current day.
Of course, the storage medium containing the computer executable instructions provided in the embodiments of the present invention is not limited to the method operations described above, and may also perform the related operations in the landslide prediction method provided in any embodiment of the present invention.
From the above description of embodiments, it will be clear to a person skilled in the art that the present invention may be implemented by means of software and necessary general purpose hardware, but of course also by means of hardware, although in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, etc., and include several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments of the present invention.
It should be noted that, in the embodiment of the landslide prediction apparatus described above, each unit and module included are only divided according to the functional logic, but not limited to the above-described division, so long as the corresponding functions can be implemented; in addition, the specific names of the functional units are also only for distinguishing from each other, and are not used to limit the protection scope of the present invention.
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (9)
1. A method of predicting landslide, comprising:
acquiring landslide information of a target area, and determining the target size of each grid of the target area based on the landslide information, wherein the landslide information comprises landslide positions and landslide areas;
Under the target size, acquiring a landslide influence factor of each grid of the target area every day in a set time period, and determining the occurrence probability of the landslide every day according to the landslide influence factor based on a first preset model;
if the occurrence probability of the landslide on the current day is larger than a preset probability threshold value, predicting the occurrence probability of the landslide of each grid based on a second preset model according to the landslide influence factors of each grid on the current day;
the determining the target size of each grid of the target area based on the landslide information includes:
determining the initial size and the initial grid number of each grid of the target area, and determining the landslide grid number and the non-landslide grid number according to the landslide information, wherein the landslide grid number is the grid number of landslide occurrence and the non-landslide grid number is the grid number of non-landslide occurrence;
if the ratio of the landslide grid number to the non-landslide grid number is in a set balance interval, taking the initial size as the target size;
if the ratio of the landslide grid number to the non-landslide grid number is not in the set balance interval, adjusting the size of each grid of the target area, and re-determining the landslide grid number, the non-landslide grid number and the current grid number;
And if the ratio of the redetermined landslide grid number to the non-landslide grid number is in the set balance interval and the ratio of the current grid number to the initial grid number is smaller than a set threshold value, taking the size of each grid after adjustment as the target size.
2. The prediction method according to claim 1, wherein the determining the target size of each grid of the target area based on the landslide information includes:
acquiring rainfall data of each rainfall station of the target area, the initial size and the initial grid number of each grid of the target area, and determining the landslide grid number and the non-landslide grid number according to the landslide information, wherein the landslide grid number is the number of grids with landslide, and the non-landslide grid number is the number of grids without landslide;
calculating rainfall predicted values of the rainfall stations corresponding to any current grid according to rainfall data of the rainfall stations with a specific number, and calculating rainfall errors based on the rainfall predicted values and actually measured rainfall values of the rainfall stations corresponding to the current grid;
if the rainfall error is smaller than a set error threshold, and the ratio of the landslide grid number to the non-landslide grid number is in a set balance interval, taking the initial size as the target size;
If the rainfall error is larger than a set error threshold, adjusting the size of each grid of the target area, and recalculating the rainfall error according to the rainfall predicted value and the actually measured rainfall value of the rainfall station corresponding to any grid after adjustment;
and if the recalculated rainfall error is smaller than the set error threshold, the ratio of the redetermined landslide grid number to the redetermined non-landslide grid number is in a set balance interval, the ratio of the current grid number to the initial grid number is smaller than the set threshold, and the size of the grid after adjustment is taken as the target size.
3. The prediction method according to claim 1, further comprising, after the determining the occurrence probability of each day of landslide according to the landslide influence factor based on the first preset model:
if the same grid comprises landslide influence factors of a plurality of time points, at least one landslide influence factor is reserved in the same grid, and the unreserved landslide influence factors are input into the first preset model again to determine the occurrence probability of the daily landslide.
4. The prediction method according to claim 1, wherein the landslide impact factor comprises a dynamic factor and a static factor, wherein the dynamic factor comprises at least one of rainfall and soil humidity, and the static factor comprises at least one of elevation, slope direction, planar curvature, profile curvature, terrain humidity index, water current intensity index, sediment transport index, terrain roughness index, fault distance, river distance, road distance, lithology, land utilization, and vegetation coverage.
5. The prediction method according to claim 4, wherein determining the occurrence probability of each landslide according to the landslide influence factor based on the first preset model includes:
calculating construction characteristics of the dynamic factors of each grid every day, wherein the construction characteristics comprise the sum, average value, maximum value, minimum value, range, quartile and rainfall time of each dynamic factor of each grid, and obtaining a first characteristic matrix of the first preset model according to the construction characteristics and the static factors every day;
and inputting the first feature matrix into the first preset model to determine the occurrence probability of landslide every day according to the first preset model.
6. The prediction method according to claim 1, wherein predicting the occurrence probability of each grid landslide according to the landslide influence factor of each grid on the current day based on the second preset model comprises:
feature extraction is carried out on landslide influence factors of grids corresponding to the current day so as to generate a second feature matrix of the second preset model;
normalizing the second feature matrix;
and inputting the normalized second feature matrix into the second preset model to determine the occurrence probability of each grid landslide in the current day according to the second preset model.
7. A landslide prediction apparatus comprising:
the grid size determining module is used for acquiring landslide information of a target area and determining the target size of each grid of the target area based on the landslide information, wherein the landslide information comprises landslide positions and landslide areas;
the grid size determining module is specifically configured to determine an initial size and an initial grid number of each grid of the target area, and determine a landslide grid number and a non-landslide grid number according to the landslide information, where the landslide grid number is a number of grids on which landslide occurs, and the non-landslide grid number is a number of grids on which landslide does not occur;
if the ratio of the landslide grid number to the non-landslide grid number is in a set balance interval, taking the initial size as the target size;
if the ratio of the landslide grid number to the non-landslide grid number is not in the set balance interval, adjusting the size of each grid of the target area, and re-determining the landslide grid number, the non-landslide grid number and the current grid number;
if the ratio of the redetermined landslide grid number to the non-landslide grid number is in the set balance interval and the ratio of the current grid number to the initial grid number is smaller than a set threshold value, taking the size of each grid after adjustment as the target size;
The first landslide prediction module is used for acquiring landslide influence factors of each grid of the target area every day in a set time period under the target size, and determining occurrence probability of the landslide every day according to the landslide influence factors based on a first preset model;
and the second landslide prediction module is used for predicting the occurrence probability of landslide of each grid according to the landslide influence factors of each grid on the current day based on a second preset model if the occurrence probability of landslide on the current day is larger than a preset probability threshold value.
8. A prediction apparatus for landslide, characterized in that the apparatus comprises:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, causes the one or more processors to implement the landslide prediction method of any one of claims 1-6.
9. A storage medium containing computer executable instructions which, when executed by a computer processor, are for performing a method of landslide prediction as claimed in any one of claims 1 to 6.
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| US10262373B2 (en) * | 2013-06-07 | 2019-04-16 | State Farm Mutual Automobile Insurance Company | Systems and methods for grid-based insurance rating |
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| CN109743743A (en) * | 2018-11-28 | 2019-05-10 | 中通服建设有限公司 | Rasterizing monitoring method, electronic equipment, storage medium and the system of network performance |
Non-Patent Citations (1)
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|---|
| The influence of DEM spatial resolution on landslide susceptibility mapping in the Baxie River basin, NW China;Zhuo Chen et al.;Natural Hazards;853-877 * |
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