CN112200361A - Landslide prediction method, device, equipment and storage medium - Google Patents

Landslide prediction method, device, equipment and storage medium Download PDF

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CN112200361A
CN112200361A CN202011066222.5A CN202011066222A CN112200361A CN 112200361 A CN112200361 A CN 112200361A CN 202011066222 A CN202011066222 A CN 202011066222A CN 112200361 A CN112200361 A CN 112200361A
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landslide
grid size
grid
size
prediction
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沈小珍
商琪
郑增荣
吴展开
江子君
宋杰
胡辉
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Hangzhou Ruhr Technology Co Ltd
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Hangzhou Ruhr Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent

Abstract

The embodiment of the invention discloses a landslide prediction method, a landslide prediction device, equipment and a storage medium, wherein whether a second grid size is an expected grid size or not is determined based on the distribution characteristics of landslide influence factors under a first grid size and a second grid size; if not, iteratively adjusting the size of the second grid based on the specific scale factor, inputting the obtained daily landslide influence factor 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. The grid size division can be carried out according to the distribution characteristics of the landslide influence factors under different grid sizes, so that the landslide influence factors have the advantages of reserving rich characteristic information and simplifying the redundancy of data under the divided grid sizes; and the landslide influence factors are input into the landslide prediction model by taking the day as a unit, so that the landslide occurrence probability is predicted, and the landslide prediction accuracy is improved.

Description

Landslide prediction method, device, equipment and storage medium
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. However, the time accuracy of the landslide factor processed by the method is poor, and particularly, the rainfall factor is generally only considered in a month or a year, so that the prediction accuracy is not ideal.
Disclosure of Invention
The invention provides a landslide prediction method, a landslide prediction device, equipment and a storage medium, which are used for determining an optimal grid based on the actual condition of a target area, performing landslide prediction according to landslide data of each grid every day, improving prediction precision, and performing landslide prediction based on a landslide prediction model, and improving prediction efficiency.
In a first aspect, an embodiment of the present invention provides a landslide prediction method, where the landslide prediction method includes:
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.
In a second aspect, an embodiment of the present invention further provides a landslide prediction apparatus, including:
the data acquisition module is used for 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;
a second grid size determination module, configured to determine 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, where the desired grid size is smaller than the second grid size and greater than or equal to the first grid size;
a second grid size adjustment module to iteratively adjust the second grid size based on a particular scale factor if the second grid size is not the desired grid size;
and the landslide prediction module is used for inputting the landslide influence factors of each grid corresponding to the expected grid size into a landslide prediction model if the current grid size corresponding to the current iteration number is the expected grid size, and determining the occurrence probability of each grid landslide based on the landslide prediction model.
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 of the embodiment of the invention, a first grid size and a second grid size of a target area are obtained, and a landslide influence factor of the target area is obtained under the first grid size and the second grid size respectively, wherein the second grid size is larger than the first grid size, and whether the second grid size is an expected grid size or not is determined respectively 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; 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. The grid size division can be carried out according to the distribution characteristics of the landslide influence factors under different grid sizes, so that the grid sizes of different grids are different, and the landslide influence factors have the advantages of reserving rich characteristic information and simplifying the redundancy of data under the divided grid sizes; 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.
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:
s110, acquiring a first grid size and a second grid size of the target area, and acquiring landslide influence factors 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. The target area is generally an area where a landslide occurs, and may be any designated area. A grid is a form of data that divides a space into regular grids, each grid being a grid or cell and each cell being assigned a corresponding attribute value to represent an entity. The landslide impact factor is raster data. The landslide impact factor may be data for each day of the target area for each time period. The set period of time may be one day, three days, one week, one month, or other period of time. Specifically, a plurality of monitoring points can be set in the target area to obtain landslide influence factors of each monitoring point in real time. And further combining the data collected by a preset department to form a landslide influence factor of each grid of the target area every day in a set time period.
The landslide influence 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, gradient, slope direction, plane curvature, section curvature, terrain humidity index, water flow intensity index, sediment transport index, terrain roughness index, distance from fault, distance from river, distance from road, lithology, land utilization, and vegetation coverage. The first grid size can be the minimum size of the target area, and the landslide grid under the first grid size can well reserve the richness of the landslide influence factors. The second grid size may be a maximum size of the target area, and dividing the grid of the target area into the second grid size may reduce data redundancy and reduce the amount of computation. For example, the first grid size is 30m x 30m and the second grid size is 1km x 1 km.
And S120, determining whether the second grid size is the expected grid size or not based on the distribution characteristics of the landslide influence factors under the first grid size and the landslide influence factors under the second grid size respectively.
It can be understood that the landslide influence factors have different degrees of characterization of grids of different sizes, the grids with smaller sizes can keep the richness of the landslide influence factors, and the grids with larger sizes can reduce the redundancy of data. The target area includes different terrains, longitude and latitude information and topography information of each subdivision area (for example, elevation and gradient are distributed in block shape, circle-shaped distribution with lake distance and line distribution with railway distance), the grids of the target area are divided into uniform sizes, and the advantages of reserving rich characteristic information and simplifying redundancy of data cannot be considered at the same time. In order to reduce the amount of calculation in the landslide prediction process and also consider the richness of the landslide influence factor, in this embodiment, the second grid size may be adjusted based on the first grid size according to the distribution characteristics of the landslide influence factor in the first grid size and the second grid size of the target area, so as to determine the optimal grid size of the target area.
Wherein the desired grid size is less than the second grid size and greater than or equal to the first grid size. Optionally, the first method for determining whether the second grid size is the desired grid size comprises the steps of: 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.
Specifically, based on the one-factor non-parametric analysis of variance method and the two-factor rank analysis of variance method, the distribution characteristics of the landslide influence factors at the first grid size, non-parametric significance tests were performed on the distribution characteristics of the landslide impact factors at the second grid size, to determine whether the distribution characteristics of the landslide influence factors under the second grid size and the distribution characteristics of the landslide influence factors under the first grid size have significant differences, if the distribution characteristic of the landslide impact factor at the second grid size is significantly different from the distribution characteristic of the landslide impact factor at the first grid size, determining that the second grid size is not the desired grid size, determining that the second grid size is the desired grid size if the distribution characteristic of the landslide impact factor at the second grid size is not significantly different from the distribution characteristic of the landslide impact factor at the first grid size.
Optionally, the second method for determining whether the second grid size is the desired grid size comprises the steps of: 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.
The threshold value may be a very small percentage, for example 1% or 2%. If the difference is smaller than the set threshold, the distribution characteristic of the landslide influence factor under the second grid size is close to the distribution characteristic of the landslide influence factor under the first grid size, the second grid size is directly used as the expected grid size without adjusting the second grid size; if the difference is not smaller than the set threshold, the difference between the distribution characteristic of the landslide impact factor in the second grid size and the distribution characteristic of the landslide impact factor in the first grid size is large, and the second grid size needs to be adjusted to the desired grid size.
And S130, if the second grid size is not the expected grid size, iteratively adjusting the second grid size based on a specific scale factor.
And S140, if the current grid size corresponding to the current iteration number is the expected grid size, inputting the landslide influence factors of each grid corresponding to the expected grid size into the landslide prediction model, and determining the occurrence probability of each grid landslide based on the landslide prediction model.
Wherein the particular scale factor may be a decreasing gradient of the grid size, e.g., the particular scale factor is 2; optionally, iteratively adjusting the second grid size based on a specific scale factor to obtain a current grid size under a current iteration number includes: and iteratively reducing the second grid size according to the scale factor to obtain the current grid size under the current iteration times.
Specifically, the second grid size is iteratively reduced according to the scale factors, the distribution characteristics of the landslide influence factors under the first grid size and the landslide influence factors under the second grid size in the current iteration number are determined, whether the second grid size in the current iteration number is an expected grid size is determined according to the distribution characteristics under the two sizes until the current grid size corresponding to the current iteration number is the expected grid size, the daily landslide influence factors of each grid corresponding to the expected grid size are input into a landslide prediction model, and the occurrence probability of each grid landslide is determined based on the landslide prediction model.
Illustratively, the first grid size is 1km by 1km, the second grid size is 30m by 30m, and the particular scale factor is 2. Carrying out nonparametric significance test on the distribution characteristics of the landslide influence factors under 1km by 1km on the basis of the distribution characteristics of the landslide influence factors under 30m by 30m, if the distribution characteristics of the landslide influence factors under 1km by 1km and the distribution characteristics of the landslide influence factors under 30m by 30m have significance differences, reducing the grids of 1km by 1km on the basis of a specific scale factor, and reducing the size of the current grids obtained for the first time to be 500m by 500 m; and then, on the basis of the distribution characteristics of the landslide influence factors under 30m by 30m, carrying out non-parameter significance test on the distribution characteristics of the landslide influence factors under 500m by 500m, if the distribution characteristics of the landslide influence factors under 500m by 500m and the distribution characteristics of the landslide influence factors under 30m by 30m have significance differences, continuing to reduce the grids of 500m by 500m on the basis of a specific scale factor until the obtained current grid size corresponding to the current iteration number is the expected grid size, and ending the process of adjusting the second grid size. Wherein the desired grid size may be 30m x 30m or a size smaller than 500m x 500m and larger than 30m x 30 m.
By the mode, grid size division is carried out according to the distribution characteristics of the landslide influence factors under different grid sizes, so that the grid sizes of different grids are different, the characteristic effect of the landslide influence factors under the divided grid sizes is the best, the advantages of reserving rich characteristic information and simplifying the redundancy of data are considered, and the accuracy and the efficiency of landslide prediction are improved.
The landslide prediction model can be a single neural network model or a complex neural network model formed by 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, 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 according to 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 of each day processed through 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.
According to the technical scheme of the embodiment of the invention, a first grid size and a second grid size of a target area are obtained, and a landslide influence factor of the target area is obtained under the first grid size and the second grid size respectively, wherein the second grid size is larger than the first grid size, and whether the second grid size is an expected grid size or not is determined respectively 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; 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. The grid size division can be carried out according to the distribution characteristics of the landslide influence factors under different grid sizes, so that the landslide influence factors have the advantages of reserving rich characteristic information and simplifying the redundancy of data under the divided grid sizes; 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 accuracy of landslide prediction 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 daily landslide influence factor of each grid corresponding to the expected grid size is input into a landslide prediction model, and the determining of the occurrence probability of each grid landslide based on the landslide prediction model includes: based on a first preset model, determining the occurrence probability of daily landslide according to daily landslide influence factors of each grid corresponding to the current grid size; and if the occurrence probability of the landslide of the current day is greater than the preset probability threshold value, determining the occurrence probability of each grid landslide according to the landslide influence factor of each grid corresponding to the size of the current grid on the basis of the 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, acquiring a first grid size and a second grid size of the target area, and acquiring landslide influence factors of the target area under the first grid size and the second grid size respectively.
S220, determining whether the second grid size is the expected grid size or not based on the distribution characteristics of the landslide influence factors under the first grid size and the landslide influence factors under the second grid size respectively.
And S230, if the second grid size is not the expected grid size, iteratively adjusting the second grid size based on a specific scale factor.
S240, if the current grid size corresponding to the current iteration number is the expected grid size, based on the first preset model, determining the occurrence probability of daily landslide according to the daily landslide influence factors of the grids corresponding to the current grid size.
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. When the first preset model is trained, obtaining historical landslide data of historical time periods of landslide occurrence places, wherein the historical landslide data comprises landslide prediction factors, extracting features of the landslide prediction factors and forming an input feature matrix of the first preset model; initializing parameters of a first preset model, inputting the input feature matrix into the first preset model, carrying out model training to obtain daily landslide prediction probability corresponding to a historical time period, and adjusting parameters of the first preset model according to an evaluation result until the first preset model is trained completely on the basis of taking an F1-value (F1-score) and an ROC (receiver operating characteristic) as evaluation indexes. 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 determining, based on the first preset model, the occurrence probability of daily landslide according to the daily landslide influence factor of each grid corresponding to the current grid size includes: calculating the construction characteristics of the dynamic factors of the grids corresponding to the current grid size, 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 the static factors of each 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.
Specifically, the landslide influence factor includes: at least one of soil moisture, elevation, slope, plane curvature, profile curvature, terrain moisture index, current intensity index, sediment transport index, terrain roughness index, distance to fault, distance to river, distance to road, lithology, land use, and vegetation coverage. The rainfall and the soil humidity can be used as dynamic factors for predicting the occurrence probability of landslide, the elevation, the gradient, the slope direction, the plane curvature, the section curvature, the terrain humidity index, the water flow intensity index, the deposition and transportation index, the terrain roughness index, the distance from a fault, the distance from a river, the distance from a road, the lithology, the land utilization and the vegetation coverage can be used as static factors for predicting the occurrence probability of landslide, characteristics of the dynamic factors are constructed, a first characteristic matrix is determined by combining the static factors, and the occurrence probability of daily landslide is determined based on the first characteristic 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)C is 1, 2, …, d, representing the landslide impact factor for each grid in the target area of day c, where the matrix X(c)The rows of (1) represent a grid, the columns represent a landslide factor, matrix X(c)Is of size m × n, i.e. comprises mGrid, n landslide factors, where i ═ 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.
And S250, if the occurrence probability of the landslide of the current day is greater than a preset probability threshold value, determining the occurrence probability of each grid landslide according to the landslide influence factor of each grid corresponding to the size of the current grid based on a second preset model.
The preset probability threshold may be 0.5, 0.6 or other values, and may also be expressed by a fraction or a percentage. When the occurrence probability of the landslide is greater than the preset probability threshold value, the occurrence probability of the landslide on the current day is high. The landslide influence factors are screened by setting the preset probability threshold value, and only when the occurrence probability of landslide on the current day is greater than the preset probability threshold value, the data on the current day are transmitted to the second preset model for further prediction, so that the data volume input by the model is greatly reduced, the processing efficiency is improved, and meanwhile, the prediction precision is improved. The number of the current day may be 1 or more. The landslide occurrence probability of each day in a set time period is predicted through the first preset model, and when the landslide occurrence probability of a certain day is larger than a preset probability threshold value, landslide preset data or a first characteristic matrix corresponding to the certain day is sent to the second preset model so as to predict the landslide transmission probability of each grid of the certain day.
Optionally, the determining, based on the second preset model, the occurrence probability of each grid landslide according to the daily landslide influence factor of each grid corresponding to the current grid size includes: performing feature extraction on the landslide influence factors of each grid corresponding to the current grid size every 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 in 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: and calculating the structural characteristics of the sum, the mean, the variance, the median, the mean and the variance of the difference, the skewness, the kurtosis and the like corresponding to the dynamic factors of each grid of the day for 3 days, 7 days, 15 days and 30 days respectively, and considering the characteristics of each grid in a set range taking the current grid as the center, such as a 3 x 3 area range, 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 the excessive landslide, the number of times of the current grid generating the landslide and the like, so as to finally obtain a second characteristic matrix of a second preset model.
Further, each feature of the second feature matrix may be normalized based on a max-min normalization algorithm. 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. After the normalization processing, feature selection may be performed on the second feature matrix based on a multiple collinearity method, so as to perform feature screening according to the degree of collinearity, and obtain a screened second feature matrix.
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 of the embodiment of the invention, the landslide prediction is carried out in two stages by setting two preset models, whether the probability of landslide occurring on the current day is predicted in the first stage is greater than a set value or not is predicted in the first stage, if yes, the 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 on 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, 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 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:
s310, acquiring a first grid size and a second grid size of the target area, and acquiring landslide influence factors of the target area under the first grid size and the second grid size respectively.
Optionally, before obtaining the first grid size and the second grid size of the target area, remote sensing image information and terrain information of the target area may also be obtained, based on a functional area classification model, each functional area of the target area is determined according to the remote sensing image information and the terrain information, and the target functional area is screened according to a label corresponding to each functional 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 a grassland type, a forest land type, a rice type and a 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.
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.
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; 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.
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.
If the functional areas of the target area are determined through S310, and the target functional areas are screened according to the labels corresponding to the functional areas, S320 may be replaced with: and acquiring a first grid size and a second grid size of a target functional area of the target area, and acquiring a landslide influence factor of the target area under the first grid size and the second grid size respectively.
And S320, determining whether the second grid size is the expected grid size or not based on the distribution characteristics of the landslide influence factors under the first grid size and the landslide influence factors under the second grid size respectively.
And S330, if the second grid size is not the expected grid size, iteratively adjusting the second grid size based on a specific scale factor.
S340, if the current grid size corresponding to the current iteration number is the expected grid size, based on the first preset model, determining the occurrence probability of daily landslide according to the daily landslide influence factors of the grids corresponding to the current grid size.
And S350, if the occurrence probability of the landslide of the current day is larger than a preset probability threshold, determining the occurrence probability of the landslide of each grid according to the landslide influence factor of each grid corresponding to the size of the current grid based on a second preset model.
As in the previous embodiment, the landslide impact factor includes a dynamic factor and a static factor. The dynamic factor includes 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:
Figure BDA0002713825880000181
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, 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 order to improve the accuracy of landslide prediction, the current 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: taking the current grid size as a first size, and determining a second size after resampling of the first size, wherein the second size is larger than the first size; and dividing the grid corresponding to the second size into a plurality of grids according to the first size, extracting the characteristic information of the landslide influence factor under the grid of the second size based on the first size, and inputting the characteristic 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.
Different from the foregoing embodiment, the first preset model and the second preset model may be self-step classification learning models, and the first preset model and the second preset model may be determined by iteratively adjusting self-step factors and downsampling ratios 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.
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.
Wherein the landslide of the target area occursThe calculation formula of the birth grade is as follows:
Figure BDA0002713825880000241
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,
Figure BDA0002713825880000242
and 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: a data acquisition module 410, a second grid size determination module 420, a second grid size adjustment module 430, and a landslide prediction module 440.
The data obtaining module 410 is configured to obtain a first grid size and a second grid size of a target area, and obtain a landslide influence factor of the target area under the first grid size and the second grid size, respectively, where the second grid size is larger than the first grid size;
a grid size determination module 420, configured to determine whether the second grid size is a desired grid size based on distribution characteristics of the landslide impact factor at the first grid size and the landslide impact 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;
a second grid size adjustment module 430 to iteratively adjust the second grid size based on a particular scale factor if the second grid size is not the desired grid size;
and the landslide prediction module 440 is configured to, if the current grid size corresponding to the current iteration number is the expected grid size, input a daily landslide influence factor of each grid corresponding to the expected grid size to a landslide prediction model, and determine an occurrence probability of each grid landslide based on the landslide prediction model.
According to the technical scheme of the embodiment of the invention, a first grid size and a second grid size of a target area are obtained, and a landslide influence factor of the target area is obtained under the first grid size and the second grid size respectively, wherein the second grid size is larger than the first grid size, and whether the second grid size is an expected grid size or not is determined respectively 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; 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. The grid size division can be carried out according to the distribution characteristics of the landslide influence factors under different grid sizes, so that the grid sizes of different grids are different, and the landslide influence factors have the advantages of reserving rich characteristic information and simplifying the redundancy of data under the divided grid sizes; 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.
Optionally, the grid size determining module 420 is further configured to perform a significance check on the distribution characteristic of the landslide influence factor at the second grid size based on the distribution characteristic of the landslide influence factor at 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.
Optionally, the grid size determining module 420 is further configured to calculate first feature data based on the distribution feature of the landslide influence factor at the first grid size, and calculate second feature data based on the distribution feature of the landslide influence factor at 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.
Optionally, the second grid size adjusting module 430 is further configured to iteratively reduce the second grid size according to the scale factor to obtain the current grid size at the current iteration time.
Optionally, the landslide prediction module 440 is further configured to determine, based on the first preset model, a daily landslide influence factor of each grid corresponding to the current grid size, and determine a daily occurrence probability of landslide;
and if the occurrence probability of the landslide of the current day is greater than the preset probability threshold value, determining the occurrence probability of each grid landslide according to the landslide influence factor of each grid corresponding to the size of the current grid on the basis of the second preset model.
Optionally, the second grid size adjusting module 430 is further configured to calculate a configuration feature of a dynamic factor of the grid corresponding to the current grid size, 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 the static factor of each 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 second grid size adjusting module 430 is further configured to perform feature extraction on the daily landslide impact factor of each grid corresponding to the current grid size 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 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, 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 data acquisition module 410, the second grid size determination module 420, the second grid size adjustment module 430, and the landslide prediction module 440 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:
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.
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:
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.
2. The prediction method of claim 1, wherein the determining whether the second grid size is a desired grid size based on the distribution characteristics of the landslide impact factors at the first grid size and the landslide impact factors at 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.
3. The prediction method of claim 1, wherein the determining whether the second grid size is a desired grid size based on the distribution characteristics of the landslide impact factors at the first grid size and the landslide impact factors at the second grid size, respectively, comprises:
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.
4. The prediction method of claim 1, wherein iteratively adjusting the second grid size based on a particular scale factor to obtain a current grid size for a current iteration number comprises:
and iteratively reducing the second grid size according to the specific scale factor to obtain the current grid size under the current iteration times.
5. The prediction method according to claim 1, wherein the inputting the daily landslide impact factor of each grid corresponding to the desired grid size into a landslide prediction model, and determining the occurrence probability of each grid landslide based on the landslide prediction model comprises:
based on a first preset model, determining the occurrence probability of daily landslide according to daily landslide influence factors of each grid corresponding to the current grid size;
and if the occurrence probability of the landslide of the current day is greater than the preset probability threshold value, determining the occurrence probability of each grid landslide according to the landslide influence factor of each grid corresponding to the size of the current grid on the basis of the second preset model.
6. The prediction method according to claim 5, wherein the determining the occurrence probability of daily landslide according to the daily landslide influence factor of each grid corresponding to the current grid size based on the first preset model comprises:
calculating the construction characteristics of the dynamic factors of the grids corresponding to the current grid size, 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 the static factors of each 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 according to the daily landslide influence factor of each grid corresponding to the current grid size based on the second preset model comprises:
performing feature extraction on the landslide influence factors of each grid corresponding to the current grid size every 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 in the current day according to the second preset model.
8. A landslide prediction apparatus comprising:
the data acquisition module is used for 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;
a grid size determination module, configured to determine 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, where the desired grid size is smaller than the second grid size and greater than or equal to the first grid size;
a second grid size adjustment module to iteratively adjust the second grid size based on a particular scale factor if the second grid size is not the desired grid size;
and the landslide prediction module is used for inputting the landslide influence factors of each grid corresponding to the expected grid size into a landslide prediction model if the current grid size corresponding to the current iteration number is the expected grid size, and determining the occurrence probability of each grid landslide based on the landslide prediction model.
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.
CN202011066222.5A 2020-09-30 2020-09-30 Landslide prediction method, device, equipment and storage medium Pending CN112200361A (en)

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