CN112200362A - Landslide prediction method, device, equipment and storage medium - Google Patents
Landslide prediction method, device, equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the invention discloses a landslide prediction method, a landslide prediction device, equipment and a storage medium, wherein remote sensing image information and topographic information of a target area are obtained, and each functional area of the target area is determined according to the remote sensing image information and the topographic information based on a functional area classification model, so that the target area is accurately partitioned; and further screening target function areas according to the labels corresponding to the function areas, inputting the obtained landslide influence factors of the target function areas in the set time period of each grid to a landslide prediction model, determining the landslide occurrence probability of each grid, performing targeted landslide prediction on the target function areas, reducing the calculated amount and improving the landslide prediction efficiency of the target areas.
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, equipment and a storage medium.
Background
Landslide is one of the most common disasters, and has the characteristics of wide distribution range, high occurrence frequency, high multiplicity, regionality, severity and the like, and the landslide can cause a great amount of casualties and great environmental and infrastructure loss every year. The method has important significance in evaluating the easiness of landslide.
The existing landslide liability prediction can be divided into a deterministic method and a non-deterministic method according to the difference of theoretical bases on which the landslide liability prediction is based. The deterministic method is mainly a directional analysis based on expert experience and knowledge and an analysis method based on a landslide process or a physical model, and the prediction accuracy is poor. With the rapid development of computer technology and 3S technology in recent years, non-deterministic methods are widely applied, mainly including fuzzy logic methods, analytic hierarchy process, decision trees, and the like. The method generally and directly analyzes the landslide influence factor of a certain area, but the area range related to the landslide prediction area is often large, the landslide influence factor comprises an area where landslide frequently occurs and an area where landslide is not easy to occur, and the landslide influence factor is directly utilized to predict the landslide of the area so as to influence the landslide prediction efficiency.
Disclosure of Invention
The invention provides a landslide prediction method, a landslide prediction device, equipment and a storage medium, and achieves the effect of improving landslide prediction efficiency of a target area.
In a first aspect, an embodiment of the present invention provides a landslide prediction method, including:
obtaining remote sensing image information and topographic information of a target area;
determining each functional area of the target area according to the remote sensing image information and the terrain information based on a functional area classification model;
screening target function areas according to the labels corresponding to the function areas, inputting the landslide influence factors acquired in advance in the set time period of each grid of the target function areas into a landslide prediction model, and determining the occurrence probability of each grid landslide.
In a second aspect, an embodiment of the present invention further provides a landslide prediction apparatus, including:
the information acquisition module is used for acquiring remote sensing image information and topographic information of the target area;
the functional area determining module is used for determining each functional area of the target area according to the remote sensing image information and the terrain information based on a functional area classification model;
and the landslide prediction module is used for screening the target functional areas according to the labels corresponding to the functional areas, inputting the obtained landslide influence factors of each grid of the target functional areas in the set time period to the landslide prediction model, and determining the occurrence probability of each grid landslide.
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 of the embodiments of the present invention.
In a fourth aspect, embodiments of the present invention further provide a storage medium containing computer-executable instructions, which when executed by a computer processor, are configured to perform the method for predicting landslide provided by any of the embodiments of the present invention.
According to the technical scheme, remote sensing image information and topographic information of a target area are obtained, and each functional area of the target area is determined according to the remote sensing image information and the topographic information based on a functional area classification model, so that the target area is accurately partitioned; and further screening target function areas according to the labels corresponding to the function areas, inputting the obtained landslide influence factors of each grid of the target function areas in a preset time period to a landslide prediction model, determining the occurrence probability of each grid landslide, performing targeted landslide prediction on the target function areas, reducing the calculated amount and improving the landslide prediction efficiency of the target area.
Drawings
Fig. 1 is a flowchart of a landslide prediction method according to a first embodiment of the present invention;
fig. 2 is a flowchart of a landslide prediction method according to a second embodiment of the present invention;
fig. 3 is a flowchart of a landslide prediction method according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of a landslide prediction apparatus according to a fourth embodiment of the present invention;
fig. 5 is a schematic structural diagram of a landslide prediction apparatus according to a fifth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1 is a flowchart of a landslide prediction method according to an embodiment of the present invention, where the embodiment is applicable to a case where an evaluation is performed on a landslide susceptibility, and the method may be performed by a landslide prediction device, as shown in fig. 1, where the method includes the following steps:
and S110, obtaining remote sensing image information and topographic information of the target area.
The target area is usually an area where a landslide occurs, and may be any designated area. The remote sensing image information refers to a satellite image and can be obtained through land satellite series earth surface reflectivity products, and the remote sensing image information can reflect the land feature types of each grid of a target area, such as grassland type, forest land type, rice type and building type; the topographic information may be obtained from a Digital Elevation Model (DEM) of the target area, and the topographic information reflects a 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, and the like, the elevation information may reflect a terrain feature elevation point of a certain grid of the target area, an elevation point near the important geographic target, or a key elevation point on the distribution range, the gradient information may reflect a gradient degree of the certain grid of the target area, and the curvature information may reflect a concave-convex condition of the certain grid of the target area.
And S120, determining each functional area of the target area according to the remote sensing image information and the terrain information based on the functional area classification model.
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; respectively carrying out feature extraction on the preprocessed remote sensing image information and the preprocessed topographic information, inputting the remote sensing image features and the topographic features into the functional area classification model, and determining each functional area of the target area, wherein the remote sensing image features comprise the mean value, the maximum value and the minimum value of the remote sensing image information, the topographic information comprises elevation information, gradient information and curvature information, and the topographic features respectively comprise the mean value, the maximum value and the minimum value of the elevation information, the gradient information and the curvature information.
When imaging a sensor of a land satellite (Landsat) series ground surface reflectivity product, due to absorption and scattering of atmospheric moisture and aerosol particles, a radiation signal received by the sensor usually cannot truly reflect the reflection and emission spectral characteristics of ground objects, and meanwhile, for an area with obvious terrain fluctuation, the spectral characteristics of the same ground object type under different slopes show larger difference. Therefore, in order to eliminate interference of the atmosphere and the topographic relief on the remote sensing image, the remote sensing image needs to be subjected to atmosphere correction and topographic radiation correction. Optionally, the remote sensing image information may be subjected to atmospheric correction by any one of a radiation transmission model method, a dark target subtraction method, a invariant target method and a histogram matching method, and on the basis, a terrain radiation C correction algorithm is used for performing efficient and automatic radiation correction to eliminate a terrain radiation difference caused by terrain relief.
The edge detection processing is to detect pixel points with obvious brightness change in the remote sensing image information, take the pixels with obvious brightness change as boundary pixel points of the remote sensing image, and determine the spatial distribution of the boundaries of different ground objects of the remote sensing image based on the boundary pixel points, for example, to obtain the boundaries of the types of the ground objects such as water bodies, roads and the like.
Optionally, after the remote sensing image information is processed, a false color synthesis method or a google map is used for further preprocessing the remote sensing image information, so that each functional area can be visually interpreted conveniently, and a label corresponding to each functional area is constructed.
In order to reduce the calculation amount of the functional area classification model, the embodiment performs feature extraction on the preprocessed remote sensing image information and the preprocessed topographic information of each band, obtains remote sensing image features including a mean value, a maximum value and a minimum value of the remote sensing image information, obtains topographic features including a mean value, a maximum value and a minimum value of the topographic information, inputs the remote sensing image features and the topographic features into the functional area classification model, and outputs labels corresponding to the functional areas. The target area is accurately partitioned according to the remote sensing image information and the terrain information of the target area based on the functional area classification model.
Optionally, the feature classification model is a random forest model, and the method for training the feature classification model includes: acquiring an initial forest model; inputting sample remote sensing image information and sample terrain information of each functional area in a historical time period into the initial forest model, and determining a prediction label and the probability of the prediction label of each functional area; and adjusting parameters of the initial forest model based on the prediction label, the probability of the prediction label and the sample labels of the functional areas until the prediction label is consistent with the sample labels and the probability of the prediction label reaches a set threshold value, so as to obtain the functional area classification model. And if the prediction label is consistent with the sample label and the probability of the prediction label reaches a set threshold value, the probability that the prediction label is the sample label is high, and the initial forest model under the iteration times is used as a functional area classification model. The set threshold may be a large value, for example, the set threshold is 0.9. Optionally, before the sample remote sensing image information is input into the initial forest model, preprocessing may be performed on the sample remote sensing image information, for example, atmospheric correction, radiation correction, edge detection, false color synthesis processing, and the like are performed on the sample remote sensing image information, so as to improve the training precision of the functional region classification model of the sample remote sensing information.
S130, screening target function areas according to the labels corresponding to the function areas, inputting the landslide influence factors acquired in advance in the set time period of each grid of the target function areas into a landslide prediction model, and determining the occurrence probability of each grid landslide.
Optionally, the label corresponding to each functional area corresponds to a historical prediction probability of the landslide. The method for determining the target functional area comprises the following steps: deleting the functional area corresponding to the label with the historical prediction probability smaller than or equal to the probability threshold, and taking the functional area corresponding to the label with the historical prediction probability larger than the probability threshold as the target functional area. The historical prediction probability is obtained by analyzing the landslide detection results of the functional areas of the monitoring areas in advance, and the probability threshold is obtained by analyzing the landslide detection results of the functional areas of the monitoring areas in advance, and may be a minimum value, for example, the value of the probability threshold is 0.2.
It can be understood that each functional area output by the functional area classification model includes all functional areas of the target area, including functional areas such as town buildings, urban green lands, water bodies, farmlands, bare soil, mountain forests and the like, and the landslide occurrence probability of each functional area is different. For example, urban buildings and urban greenbelts have a very low probability of landslide and water and mountain forests have a high probability of landslide. It should be noted that, 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 targeted landslide prediction is performed on the target functional area, so that the calculation amount can be reduced, and the landslide prediction efficiency of the target area can be improved.
The raster data is a data form which divides the space into regular grids, each grid becomes a raster or a unit, and corresponding attribute values are given to each unit to represent the entity. The landslide impact factor is raster data. The landslide influence factors mainly comprise various landslide influence factors of landslide, such as elevation factors and environmental factors. The set period of time may be one day, three days, one week, one month, or other period of time. Optionally, the landslide impact factor includes a dynamic factor and a static factor, wherein the dynamic factor includes at least one of rainfall and soil moisture, and the static factor includes at least one of elevation, gradient, slope, plane curvature, section curvature, terrain moisture index, current intensity index, sediment transport index, terrain roughness index, distance from fault, distance from river, distance from road, lithology, land use, and vegetation coverage.
The landslide prediction model can be a single neural network model or a complex neural network model formed by sequentially connecting an input end and an output end. Specifically, the training process of the landslide prediction model is as follows: extracting basic information of landslide of a target area or all areas from files such as a landslide field survey report, a typical landslide monitoring report and the like, wherein the basic information comprises landslide information such as landslide occurrence time, longitude and latitude, disaster scale and the like and measured rainfall of the landslide area, determining landslide influence factors of historical time periods of various landslide occurrence places according to the landslide information, and forming a training set and a verification set by the landslide influence factors of the historical time periods in a set proportion, such as 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 to obtain landslide influence factors with consistent grid sizes, and training a landslide prediction model by the landslide influence factors processed in the steps. The landslide influence factors are subjected to characteristic engineering to extract the characteristics of each landslide influence factor so as to form an input characteristic matrix of the landslide prediction model; initializing parameters of a landslide prediction model, inputting the input feature matrix into the landslide prediction model, performing model training to obtain historical landslide information of a historical time period, adjusting parameters of the landslide prediction model according to an evaluation result based on an F1-value (F1-Score) and an ROC (Receiver Operating Characteristic) as evaluation indexes, and when the evaluation indexes are met, primarily finishing the training, performing model verification through a verification set, and obtaining the trained landslide prediction model after the verification passes. The historical landslide information comprises the probability of landslide of each grid.
Optionally, before inputting the obtained landslide influence factors of each grid of the target function area to the landslide prediction model within the set time period, the method further includes: and preprocessing the landslide influence factor, wherein the preprocessing comprises at least one of coordinate unified processing, correction processing, data discretization processing and grid unified processing.
Due to different sources of the landslide influence factors, the sizes of the coordinates or grids of the landslide influence factors are not uniform, and therefore after the landslide influence factors are obtained, the coordinate uniform processing and the grid uniform processing need to be performed on the landslide influence factors. Of course, other data preprocessing, such as correction processing, outlier removal, non-raster data rasterization, etc., may also be performed according to the specifics of the landslide impact factor. Specifically, the coordinate unification process is mainly used for unifying the coordinate system of the landslide impact factor, for example, the coordinate system of the landslide impact factor is taken as the sienna 80 coordinate system, and of course, other coordinate systems may also be taken as the coordinate system of the landslide impact factor.
According to the technical scheme, remote sensing image information and topographic information of a target area are obtained, and each functional area of the target area is determined according to the remote sensing image information and the topographic information based on a functional area classification model, so that the target area is accurately partitioned; and further screening target function areas according to the labels corresponding to the function areas, inputting the obtained landslide influence factors of each grid of the target function areas in a preset time period to a landslide prediction model, determining the occurrence probability of each grid landslide, and performing targeted landslide prediction on the target areas, so that the calculated amount is reduced, and the landslide prediction efficiency of the target areas is improved.
Example two
Fig. 2 is a flowchart of a landslide prediction method according to a second embodiment of the present invention, where this embodiment is a further refinement of the previous embodiment, where the landslide influence factors obtained in advance in a set time period of the target function region are input into a landslide prediction model to determine occurrence probabilities of grid landslides, where the method includes: determining the occurrence probability of 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 greater than a preset probability threshold value, determining the occurrence probability of each grid landslide of the target functional area according to the landslide influence factor on the current day based on a second preset model. In the method, reference is made to the above-described embodiments for those parts which are not described in detail. Referring specifically to fig. 2, the method may include the steps of:
s210, obtaining remote sensing image information and topographic information of the target area.
And S220, determining each functional area of the target area according to the remote sensing image information and the terrain information based on the functional area classification model.
S230, screening target function areas according to the labels corresponding to the function areas, and determining the occurrence probability of landslide every day according to landslide influence factors of the target function areas based on a first preset model.
As described in the previous embodiment, the landslide influence factor includes data corresponding to each landslide factor, wherein the landslide factor includes a dynamic factor including rainfall (precipitation) and soil humidity, and a static factor including elevation, gradient, slope, plane curvature, section curvature, terrain humidity index, current intensity index, sedimentary transport index, terrain roughness index, distance from fault, distance from river, distance from road, lithology, land utilization, vegetation coverage, and the like.
The first preset model may be a neural network model or other learning algorithm. For example, the first predetermined model may be a Support Vector Machine algorithm (SVM), a Long Short-Term Memory Network (LSTM), a logistic Regression model (LR), an XGBoost (Extreme Gradient boost Decision Tree) algorithm, a GBDT (Gradient boost Decision Tree) algorithm, a Full Convolution Network (FCN), a cyclic convolution Network (RNN), a Residual Network (Residual Network, net), a gated cyclic Unit (Gate recovery, GRU), and so on. The training method of the first preset model is the same as the training method of the landslide prediction model in the previous embodiment, and is not described in detail here.
Optionally, the method for determining the occurrence probability of daily landslide is: calculating the structural characteristics of landslide influence factors of each grid every day, wherein the structural characteristics comprise the sum, the average value, the maximum value, the minimum value, the range, the quartile and the rainfall time of each landslide influence factor of each grid, and a first characteristic matrix of the first preset model is obtained according to the structural characteristics every day; and inputting the first feature matrix into the first preset model so as to determine the occurrence probability of landslide every day according to the first preset model.
As described in the foregoing embodiment, the landslide influence factor includes a dynamic factor and a static factor, a feature is constructed for the dynamic factor, a first feature matrix is determined by combining the static factor, and the occurrence probability of daily landslide is determined based on the first feature matrix and a first preset model.
Illustratively, the acquired landslide impact factor X for each grid per day is: x ═ X(1),X(2),…,X(d)In which the upper corner indicates days, X(c)And c is 1,2, …, d, which indicates each grid in the target area of the day cThe landslide influence factor of, wherein matrix X(c)The rows of (1) represent a grid, the columns represent a landslide factor, matrix X(c)Is m × n, i.e. comprises m grids and n landslide factors, wherein i is 1,2, … n1Denotes a dynamic factor (rainfall, soil humidity, etc.), i ═ n1+1,n1+2, … n, representing a static factor. Constructing feature set X in units of days1(first feature matrix) constructed as follows: for each X(c)Calculating n by grid1The sum, the average value, the maximum value, the minimum value, the range, the upper quartile, the lower quartile and other structural characteristics of the dynamic factors are obtained, and the characteristic set X is obtained from the structural characteristics and the static factors1. For training data, a label vector Y may also be constructed1To indicate whether or not a landslide occurs every day, wherein 1 indicates that a landslide occurs and 0 indicates that a landslide does not occur.
S240, if the occurrence probability of the landslide of the current day is larger than a preset probability threshold value, based on a second preset model, determining the occurrence probability of each grid landslide of the target function area according to the landslide influence factor of the current day of the target function area.
Optionally, the determining, based on the second preset model, the landslide occurrence probability of each grid of the target functional area according to the landslide influence factor of the current day of the target functional area includes: performing feature extraction on the landslide influence factors of each grid corresponding to the current day 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 so as to determine the landslide occurrence probability of each grid of the target function area on the current day according to the second preset model.
Specifically, assuming that the landslide occurrence probability on the c-th day is greater than the prediction probability threshold, the landslide impact factor X on the c-th day is determined(c)And sending the feature set to a second preset model, and constructing the feature set of the second preset model in a specific construction mode: calculating the dynamic factors of each grid of the day according to the corresponding sum, mean, variance, median and difference mean and power of 3 days, 7 days, 15 days and 30 daysTaking structural characteristics such as difference, skewness, kurtosis and the like into consideration, and considering the conditions of each grid in a set range taking the current grid as the center, such as a 3X 3 area range, including whether the current grid is the maximum value or the minimum value in the set range, whether the current grid exceeds the mean value corresponding to the set range, whether the grid in the set range generates a landslide or not, the number of times of the current grid generating the landslide and the like, and finally obtaining a second characteristic matrix X of a second preset model2 fea。
In particular, the second feature matrix X may be normalized based on max-min2 feaThe respective features of (a) are normalized. Of course, other normalization algorithms can be selected for normalization. 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 processing, the method further includes: selecting the characteristics of the second characteristic matrix based on a multiple collinearity method, and screening the characteristics according to the collinearity degree to obtain a screened second characteristic matrix
Further, after the feature selection, the steps are replaced by: the second feature matrix after screening
And inputting the data into a second preset model so as to determine the occurrence probability of each grid landslide of 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 normalization layer, an activation function and an optimization layer, wherein the value range of the number of convolutional cores of the convolutional layer is 32-512, and the step length is 16; the activation function may include any one of a ReLU function (Linear rectification function), a Linear function (Linear function), a Sigmoid function, and a Tanh function (hyperbolic function); the optimization method comprises any one of optimization algorithms such as SGD (Gradient update rule), Adam (Adaptive Moment Estimation), Nadam (neov Adaptive Moment Estimation), Adaptive Gradient Algorithm (Adaptive Gradient Algorithm) and RMSprop (Root Mean Square Gradient Descent Algorithm); the value of the initial learning rate may be 0.0001, 0.001, 0.01 or 0.1; the value range of the number of neurons in the hidden layer is 4-256, the step length is 4, the value range of the number of the neurons in the hidden layer is 3-8, and the step length is 1; the random discarding rate is 0-0.8, and the step length is 0.05. The training method of the first preset model is the same as the training method of the landslide prediction model in the previous embodiment, and is not described in detail here.
According to the technical scheme provided by the embodiment, the target area is accurately partitioned according to the remote sensing image information and the terrain information based on the function area classification model, the target function area is further screened according to the label corresponding to each function area, prediction is carried out by taking days as a unit, the time sequence information of dynamic factors is fully considered, and the prediction precision is improved; by setting two preset models, targeted landslide prediction is carried out on a target function area in two stages, the calculated amount is reduced, whether the probability of landslide occurring on the current day is predicted in the first stage is larger than a set value or not is judged, if yes, 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 of the current day as a reference, so that the waste of computing resources is greatly reduced, and the landslide prediction efficiency is improved; by constructing the characteristics of the dynamic factors, the functions of the dynamic factors in model prediction are increased, and the precision of the model prediction is improved; through feature screening and normalization processing, the efficiency of model prediction is improved. Meanwhile, the two models are respectively optimized in parameters, 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 present embodiment is used to analyze the entire flow of landslide prediction. In the method, reference is made to the above-described embodiments for those parts which are not described in detail. Referring specifically to fig. 3, the method may include the steps of:
and S310, obtaining remote sensing image information and topographic information of the target area.
And S320, determining each functional area of the target area according to the remote sensing image information and the terrain information based on the functional area classification model.
S330, screening target functional areas according to the labels corresponding to the functional areas, and determining the occurrence probability of landslide every day according to landslide influence factors of the target functional areas based on a first preset model.
In order to improve the representation effect of the landslide influence factor in the target function area, the target size of the grid corresponding to the target function area can be determined before the occurrence probability of each day of landslide is determined according to the landslide influence factor based on the first preset model. Alternatively, the target size may be determined by two methods. Optionally, a first method for determining the grid of the target size is to divide the grid corresponding to the target functional area uniformly. The uniform dividing method of the target size comprises the following steps: acquiring landslide information of a target functional area; determining a target size for each grid of the target area based on the landslide information, wherein the landslide information comprises a landslide location and a landslide area; wherein the determining a target size for each grid of the target area based on the landslide information comprises: 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 landslides, and the non-landslide grid number is the grid number of non-landslides; if the ratio of the landslide grid number to the non-landslide grid number is in the 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 number of the newly determined landslide grids to the number of the non-landslide grids is within the set balance interval and the ratio of the current grid number to the initial grid number is smaller than a set threshold, taking the adjusted size of each grid as the target size.
Wherein determining a target size for each grid of the target area based on the landslide information comprises: 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 grid number of landslides, and the non-landslide grid number is the grid number of non-landslides; calculating a rainfall prediction value of a rainfall station corresponding to any current grid according to rainfall data of a specific number of rainfall stations, and calculating a rainfall error based on the rainfall prediction value and an actual rainfall value of the rainfall station corresponding to the current grid; if the rainfall error is smaller than a set error threshold value and the ratio of the landslide grid number to the non-landslide grid number is within a set balance interval, taking the initial size as the target size; if the rainfall error is larger than a set error threshold value, adjusting the size of each grid of the target area, and recalculating the rainfall error according to the adjusted rainfall predicted value and the actually measured rainfall value of the rainfall station corresponding to any grid; and if the recalculated rainfall error is smaller than the set error threshold value, the ratio of the number of the redetermined landslide grids to the number of the redetermined non-landslide grids is in a set balance interval, and the ratio of the number of the current grids to the number of the initial grids is smaller than a set threshold value, taking the size of the adjusted grid as the target size.
In the embodiment, the number of landslide grids and the number of non-landslide grids are determined in the above manner, so that the target size of the grid 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 landslide occurrence probability prediction accuracy is improved.
Optionally, a second method for determining the target size is to divide the grid corresponding to the target functional area non-uniformly. The uneven dividing method of the grid with the target size comprises the following steps: acquiring a first grid size and a second grid size of a target area, and acquiring a landslide influence factor of the target area under the first grid size and the second grid size respectively, wherein the second grid size is larger than the first grid size; determining whether the second grid size is a desired grid size based on distribution characteristics of the landslide influence factor at the first grid size and the landslide influence factor at the second grid size, respectively, wherein the desired grid size is smaller than the second grid size and greater than or equal to the first grid size; if not, iteratively adjusting the second grid size based on a particular scale factor; and if the current grid size corresponding to the current iteration times is the expected grid size, inputting the daily landslide influence factors of each grid corresponding to the expected grid size into a landslide prediction model, and determining the occurrence probability of each grid landslide based on the landslide prediction model.
Wherein the determining whether the second grid size is the desired grid size based on the distribution characteristics of the landslide impact factor under the first grid size and the landslide impact factor under the second grid size, respectively, comprises: performing significance test on the distribution characteristics of the landslide influence factors under the second grid size based on the distribution characteristics of the landslide influence factors under the first grid size; determining the second grid size to be the desired grid size if the landslide image factor at the second grid size passes a saliency test, otherwise, the second grid size is not the desired grid size. Determining whether the second grid size is the desired grid size based on the distribution characteristics of the landslide influence factor under the first grid size and the landslide influence factor under the second grid size, respectively, includes: calculating first characteristic data based on the distribution characteristics of the landslide influence factors under the first grid size, and calculating second characteristic data based on the distribution characteristics of the landslide influence factors under the second grid size; wherein the first characteristic data and the second characteristic data comprise at least one of variance and mean; comparing a difference between the first characteristic data and the second characteristic data to a set threshold; determining the second grid size to be the desired grid size if the difference is less than the set threshold, otherwise, the second grid size is not the desired grid size.
Iteratively adjusting the second grid size based on a specific scale factor to obtain a current grid size under a current iteration number, including: and iteratively reducing the second grid size according to the specific scale factor to obtain the current grid size under the current iteration times.
By carrying out uneven division on the grid with the target size, the landslide influence factor has the advantages of simultaneously keeping rich characteristic information and simplifying the redundancy of data under the divided grid size; when the landslide probability is predicted subsequently, the landslide influence factors are input into the landslide prediction model by taking days as units according to the landslide influence factors in grids corresponding to different grid sizes, the landslide occurrence probability is predicted, and the landslide prediction accuracy is improved.
In this embodiment, the target size may be obtained by adjusting the initial size for a plurality of times, and the target size may be used as the first grid size. In order to improve the accuracy of landslide prediction, the first grid size can be resampled, and landslide prediction can be performed by combining the characteristic information of the resampled grid size. The specific method comprises the following steps: the method comprises the steps of obtaining a first grid size of a target area, determining a second grid size after resampling of the first grid size, wherein the second grid size is larger than 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 feature information of a landslide influence factor under the grid of the second grid size based on the first grid size, and inputting the feature information and the landslide influence factor into a first preset model and a second preset model to predict landslide. The extraction method of the characteristic information comprises the following steps: acquiring a characteristic value of a landslide influence factor in an eight-neighborhood grid of a current grid; and determining the characteristic information of the current grid according to the characteristic values in the eight-neighborhood grid.
In the embodiment, the occurrence probability of each grid landslide is determined by combining the characteristic information and the landslide influence factor, so that the data volume of the landslide influence factor can be increased, the occurrence probability of each grid landslide can be more accurately determined according to the characteristic information, and the accuracy of landslide prediction is improved.
As in the previous embodiment, the landslide impact factor includes a dynamic factor and a static factor. Wherein the dynamic factor comprises at least one of rainfall and soil moisture. The rainfall amount can be determined by inputting the acquired geographic environment data of the target area into a rainfall interpolation model. The geographic environmental data includes geographic location data, atmospheric data, terrain data, and underlying surface data. The rainfall interpolation model can be a back propagation neural network (BP), a multilayer feedforward network based on an error back propagation algorithm and composed of nonlinear transformation units is adopted, the BP generally consists of an input layer, a hidden layer and an output layer, each layer also comprises N neurons, the neurons in the same layer are independent, and the output of the neurons in each layer only influences the input of the neurons in the lower layer after passing through 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 of the initial model, and calculating the fitness of the initial model; inputting sample geographic environment information in a historical time period into the initial model, determining a predicted rainfall, iteratively adjusting an initial weight matrix and an initial threshold of the initial model based on a genetic algorithm according to the predicted rainfall and an actual rainfall of the historical time period; and based on the weight matrix threshold value after iterative adjustment, adjusting the initial model and calculating the fitness of the adjusted model 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 the input layer and the hidden layer and a connection weight between the hidden layer and the output layer; the initial threshold may include a threshold of the hidden layer and a threshold of the output layer. The calculation formula of the fitness of the initial model is as follows:
wherein n isIs 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, after sample geographic environment information in a historical time period is input into an initial model, a predicted rainfall is obtained, 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 adjusted in an iterative mode, an adjusted weight matrix and an adjusted 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 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 encoded by binary encoding, real number encoding or gray code encoding, and the adjusted weight matrix and threshold are determined according to the weight encoding and the threshold encoding. 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 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 accuracy and 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 accuracy of the rainfall probability can be improved based on the rainfall interpolation model prediction, and the landslide prediction accuracy can be improved.
In this embodiment, the target size may be obtained by adjusting the initial size for a plurality of times, and the target size may be used as the first grid size. In order to improve the accuracy of landslide prediction, the first grid size can be resampled, and landslide prediction can be performed by combining the characteristic information of the resampled grid size. The specific method comprises the following steps: the method comprises the steps of obtaining a first grid size of a target area, determining a second grid size after resampling of the first grid size, wherein the second grid size is larger than 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 feature information of a landslide influence factor under the grid of the second grid size based on the first grid size, and inputting the feature information and the landslide influence factor into a first preset model and a second preset model to predict landslide. The extraction method of the characteristic information comprises the following steps: acquiring a characteristic value of a landslide influence factor in an eight-neighborhood grid of a current grid; and determining the characteristic information of the current grid according to the characteristic values in the eight-neighborhood grid.
In the embodiment, the occurrence probability of each grid landslide is determined by combining the characteristic information and the landslide influence factor, so that the data volume of the landslide influence factor can be increased, the occurrence probability of each grid landslide can be more accurately determined according to the characteristic information, and the accuracy of landslide prediction is improved.
Unlike the foregoing embodiment, the first preset model may be a self-step classification learning model, and the first preset model is determined by iteratively adjusting a self-step factor and a down-sampling ratio according to historical landslide data and historical non-landslide data. The self-step factor is determined according to the number of the sub-boxes of the historical non-landslide data and the iteration times, and the down-sampling proportion is determined according to the self-step factor of each sub-box and the self-step factors of all sub-boxes. Optionally, the method for determining historical landslide data and historical non-landslide data includes: determining labels corresponding to grids of a history area, and determining historical rainfall and specific correlation factors corresponding to a landslide grid and a non-landslide grid at each history time point; and generating a landslide data set comprising historical rainfall and specific correlation factors respectively corresponding to landslide grids and 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 historical landslide data and historical non-landslide data includes: determining labels corresponding to grids of a history area, and determining historical rainfall and specific correlation factors corresponding to a landslide grid and a non-landslide grid at each history time point; and generating a landslide data set comprising historical rainfall and specific correlation factors respectively corresponding to landslide grids and 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 rainfall of a landslide grid and rainfall of a non-landslide grid at each historical landslide time point, and specific correlation factors under each label; and inputting the rainfall capacity of the landslide grid at the historical landslide time point, the rainfall capacity of the non-landslide grid and specific correlation factors under each label into the initial prediction model according to the day, 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 point until the loss function reaches a set threshold value to obtain the first preset model.
The 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 to obtain the first preset model comprises: determining an initial classification hardness of the initial prediction model based on the rainfall capacity of the landslide grid at the historical landslide time point, the rainfall capacity of the non-landslide grid, each of the labels and a specific correlation factor under each of the labels; determining the rainfall of the non-landslide grid and the number of the bins of the specific correlation factors of the non-landslide grid according to the initial classification hardness, and determining the self-stepping factors of each bin of the initial prediction model based on the number of the bins; determining a down-sampling proportion of each bin based on the self-stepping factor, determining the rainfall of a down-sampled non-landslide grid in each bin based on the down-sampling proportion, and determining a specific correlation factor of the down-sampled non-landslide grid; inputting the rainfall of the down-sampled non-landslide grid, the specific correlation factor of the down-sampled non-landslide grid and the rainfall of the landslide grid 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-stepping factor and the down-sampling proportion of each sub-box based on the training classification hardness of the loss function on a single sample; and adjusting the initial prediction model according to the self-stepping factor of the iterative adjustment and the down-sampling proportion of each sub-box until the loss function reaches a set threshold value, thereby obtaining the first preset model.
The down-sampling 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 the number of sub-box labels and the number of iterations.
The benefit of selecting the self-classification learning model for landslide prediction is as follows: the self-step factors of the self-step classification learning model are determined according to the box number and the iteration number of historical non-landslide data, and the down-sampling proportion is determined according to the self-step factor of each box and the self-step factors of all the boxes.
S340, if the occurrence probability of the landslide of the current day is larger than a preset probability threshold value, based on a second preset model, determining the occurrence probability of each grid landslide of the target function area according to the landslide influence factor of the current day of the target function area.
Optionally, the second preset model may also be a self-walking ensemble learning model. The determination method for training the historical landslide data of the second preset model and the training method for the second preset model can refer to S330, and the step is not described in detail.
Optionally, after determining the occurrence probability of each grid landslide of the current day, the occurrence probability of each grid of the current day may be compared with the early warning threshold of the target area, and the occurrence level of the landslide of the target area is determined based on the early warning level corresponding to the obtained 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 of each area.
The method for determining the first coefficient and the second coefficient comprises the following steps: acquiring historical probability of landslide occurring in a set time period of grid landslide of each region; 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 probability; 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 the occurrence probability of each grid landslide; and calculating a first product of each risk level and the first coefficient in the first prediction probability interval, calculating a second product of each risk level and the second coefficient in the second prediction probability interval, 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 feature of the historical probability comprises: determining the historical probability and the corresponding times of the historical probability; according to the historical probabilities and the landslide occurrence times corresponding to the historical probabilities, determining the density distribution characteristics and the breakpoint distribution characteristics of the historical probabilities; determining the first determined probability interval based on the intensity distribution characteristic and determining the second determined probability interval based on the breakpoint distribution characteristic.
It can be understood that, in the above manner, based on the historical probability of landslide occurring in the set time period of 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 values of each risk level in the first determined probability interval and the second determined probability interval and the set evaluation index, after the occurrence probabilities of different regions are obtained, the early warning threshold values of different regions can be flexibly determined according to the first coefficient, the second coefficient and the occurrence probability corresponding to different regions, which is beneficial to accurately determining the risk level of the region according to the early warning threshold values corresponding to the regions subsequently.
Determining the occurrence level of the landslide of the target area based on the early warning levels corresponding to the obtained occurrence probabilities of the grids, wherein the determining of the occurrence level of the landslide of the target area comprises the following steps of: screening a target grade greater than a first grade in the early warning threshold value; calculating the average grade of the grid corresponding to the target grade; and determining the occurrence grade of the landslide of the target area based on the average grade, the setting coefficient and the ratio of the number of grids corresponding to the target grade to the number of all grids in the target area.
The formula for calculating the occurrence grade of the landslide of the target area is as follows:
wherein alpha is a set coefficient and is obtained by carrying out Bayesian calculation on the historical occurrence level of the landslide of the target area,
to the eyesAnd p is the ratio of the number of grids corresponding to the target level to the number of all grids in the target area.
Example four
Fig. 4 is a schematic diagram illustrating a result of a landslide prediction apparatus according to a fourth embodiment of the present invention, where as shown in fig. 4, the landslide prediction apparatus includes: an information acquisition module 410, a functional zone determination module 420, and a landslide prediction module 430.
The information acquisition module 410 is configured to acquire remote sensing image information and topographic information of a target area;
a functional area determining module 420, configured to determine, based on a functional area classification model, each functional area of the target area according to the remote sensing image information and the terrain information;
and the landslide prediction module 430 is configured to screen the target functional areas according to the labels corresponding to the functional areas, input the obtained landslide influence factors of each grid of the target functional areas in the set time period to the landslide prediction model, and determine the occurrence probability of landslide of each grid.
According to the technical scheme, remote sensing image information and topographic information of a target area are obtained, and each functional area of the target area is determined according to the remote sensing image information and the topographic information based on a functional area classification model, so that the target area is accurately partitioned; and further screening target function areas according to the labels corresponding to the function areas, inputting the obtained landslide influence factors of the target function areas in the set time period of each grid to a landslide prediction model, determining the landslide occurrence probability of each grid, performing targeted landslide prediction on the target function areas, reducing the calculated amount and improving the landslide prediction efficiency of the target areas.
Optionally, the functional area determining module 420 is further configured to determine, based on the functional area classification model, each functional area of the target area according to the remote sensing image information and the terrain information, including:
preprocessing the remote sensing image information, wherein the preprocessing comprises atmospheric correction, radiation correction and edge detection processing;
respectively carrying out feature extraction on the preprocessed remote sensing image information and the preprocessed topographic information, inputting the remote sensing image features and the topographic features into the functional area classification model, and determining each functional area of the target area, wherein the remote sensing image features comprise the mean value, the maximum value and the minimum value of the remote sensing image information, the topographic information comprises elevation information, gradient information and curvature information, and the topographic features respectively comprise the mean value, the maximum value and the minimum value of the elevation information, the gradient information and the curvature information.
Optionally, the apparatus further comprises: a functional area classification model training module; the functional area classification model is a random forest model;
the functional area classification model training module is specifically used for acquiring an initial forest model;
inputting sample remote sensing image information and sample terrain information of each functional area in a historical time period into the initial forest model, and determining a prediction label and the probability of the prediction label of each functional area;
and adjusting parameters of the initial forest model based on the prediction label, the probability of the prediction label and the sample labels of the functional areas until the prediction label is consistent with the sample labels and the probability of the prediction label reaches a set threshold value, so as to obtain the functional area classification model.
Optionally, the landslide prediction module 430 is further configured to delete the functional area corresponding to the label with the historical prediction probability being less than or equal to a set threshold, and use the functional area corresponding to the label with the historical prediction probability being greater than or equal to the set threshold as the target functional area.
Optionally, the landslide prediction module 430 is further configured to determine an occurrence probability of daily landslide 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 greater than a preset probability threshold value, determining the occurrence probability of each grid landslide of the target functional area according to the landslide influence factor on the current day based on a second preset model.
Optionally, the landslide prediction module 430 is further configured to calculate a configuration feature of a dynamic factor in the landslide influence factors of each grid every day, where the configuration feature includes a sum, an average value, a maximum value, a minimum value, 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 configuration feature and a static factor in the landslide influence factors every day;
and inputting the first feature matrix into the first preset model so as to determine the occurrence probability of landslide every day according to the first preset model.
Optionally, the landslide prediction module 430 is further configured to perform feature extraction on the landslide influence factors of each grid corresponding to the current day 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 so as to determine the landslide occurrence probability of each grid of the target function area on 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, 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 the device processors 510 may be one or more, and one processor 510 is taken as an example in fig. 5; the processor 510, the memory 520, the input device 530 and the output device 540 of the apparatus may be connected by a bus or other means, as exemplified by the bus connection in fig. 5.
The memory 520 may be used as a computer-readable storage medium for storing software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the landslide prediction method in the embodiment of the present invention (for example, the information acquisition module 410, the functional region determination module 420, and the landslide prediction module 430 in the landslide prediction apparatus). The processor 510 executes various functional applications of the device and data processing by executing software programs, instructions and modules stored in the memory 520, that is, implements the landslide prediction method described above.
The memory 520 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. Further, the 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, the memory 520 may further include memory located remotely from the 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 device 530 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of the apparatus. The output device 540 may include a display device such as a display screen.
EXAMPLE six
Sixth embodiment of the present invention also provides a storage medium containing computer-executable instructions which, when executed by a computer processor, perform a method of predicting landslide, the method comprising:
obtaining remote sensing image information and topographic information of a target area;
determining each functional area of the target area according to the remote sensing image information and the terrain information based on a functional area classification model;
screening target function areas according to the labels corresponding to the function areas, inputting the landslide influence factors acquired in advance in the set time period of each grid of the target function areas into a landslide prediction model, and determining the landslide occurrence probability of each grid.
Of course, the storage medium containing the computer-executable instructions provided by the embodiments of the present invention is not limited to the method operations described above, and may also perform related operations in the landslide prediction method provided by any embodiment of the present invention.
From the above description of the embodiments, it is obvious for those skilled in the art that the present invention can be implemented by software and necessary general hardware, and certainly, can also be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute the methods according to the embodiments of the present invention.
It should be noted that, in the embodiment of the prediction apparatus using landslide, the included units and modules are only divided according to the functional logic, but are not limited to the above division as long as the corresponding functions can be realized; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. 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, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A method of predicting landslide, comprising:
obtaining remote sensing image information and topographic information of a target area;
determining each functional area of the target area according to the remote sensing image information and the terrain information based on a functional area classification model;
screening target function areas according to the labels corresponding to the function areas, inputting the landslide influence factors acquired in advance in the set time period of each grid of the target function areas into a landslide prediction model, and determining the occurrence probability of each grid landslide.
2. The prediction method according to claim 1, wherein the determining each functional zone of the target zone according to the remote sensing image information and the terrain information based on the functional zone classification model comprises:
preprocessing the remote sensing image information, wherein the preprocessing comprises at least one of atmospheric correction, radiation correction and edge detection processing;
respectively carrying out feature extraction on the preprocessed remote sensing image information and the preprocessed topographic information, inputting the remote sensing image features and the topographic features into the functional area classification model, and determining each functional area of the target area, wherein the remote sensing image features comprise the mean value, the maximum value and the minimum value of the remote sensing image information, the topographic information comprises elevation information, gradient information and curvature information, and the topographic features respectively comprise the mean value, the maximum value and the minimum value of the elevation information, the gradient information and the curvature information.
3. The prediction method of claim 1, wherein the feature classification model is a random forest model, and the training method of the feature classification model comprises:
acquiring an initial forest model;
inputting sample remote sensing image information and sample terrain information of each functional area in a historical time period into the initial forest model, and determining a prediction label and the probability of the prediction label of each functional area;
and adjusting parameters of the initial forest model based on the prediction label, the probability of the prediction label and the sample labels of the functional areas until the prediction label is consistent with the sample labels and the probability of the prediction label reaches a set threshold value, so as to obtain the functional area classification model.
4. The prediction method according to claim 1, wherein the label corresponding to each functional area corresponds to a historical prediction probability of a landslide;
correspondingly, the screening the target functional regions according to the labels corresponding to the functional regions includes:
deleting the functional area corresponding to the label with the historical prediction probability smaller than or equal to the probability threshold, and taking the functional area corresponding to the label with the historical prediction probability larger than the probability threshold as the target functional area.
5. The prediction method according to claim 1, wherein the step of inputting a landslide influence factor acquired in advance for each grid of the target function area in a set time period each day into the landslide prediction model to determine the occurrence probability of each grid landslide comprises:
determining the occurrence probability of landslide every day according to the landslide influence factors of the target functional zone based on a first preset model;
and if the occurrence probability of the landslide of the current day is greater than a preset probability threshold value, based on a second preset model, determining the occurrence probability of each grid landslide of the target function area according to the landslide influence factor of the current day of the target function area.
6. The prediction method according to claim 5, wherein the determining the occurrence probability of daily landslide according to the landslide influence factor based on the first preset model comprises:
calculating the construction characteristics of dynamic factors in the landslide influence factors of each grid every day, wherein the construction characteristics comprise the sum, the average value, the maximum value, the minimum value, the range, the quartile and the 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 static factors in the landslide influence factors every day;
and inputting the first feature matrix into the first preset model so as to determine the occurrence probability of landslide every day according to the first preset model.
7. The prediction method according to claim 5, wherein the determining the occurrence probability of each grid landslide of the target functional area according to the landslide influence factor of the current day based on the second preset model comprises:
performing feature extraction on the landslide influence factors of each grid corresponding to the current day 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 so as to determine the occurrence probability of each grid landslide of the target function area of the current day according to the second preset model.
8. A landslide prediction apparatus comprising:
the information acquisition module is used for acquiring remote sensing image information and topographic information of the target area;
the functional area determining module is used for determining each functional area of the target area according to the remote sensing image information and the terrain information based on a functional area classification model;
and the landslide prediction module is used for screening the target functional areas according to the labels corresponding to the functional areas, inputting the obtained landslide influence factors of each grid of the target functional areas in the set time period to the landslide prediction model, and determining the occurrence probability of each grid landslide.
9. An apparatus for predicting landslide, the apparatus comprising:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a method of predicting landslide of any of claims 1-7.
10. A storage medium containing computer-executable instructions for performing the method of predicting landslide of any one of claims 1-7 when executed by a computer processor.
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