CN112529315B - Landslide prediction method, landslide prediction device, landslide prediction equipment and storage medium - Google Patents
Landslide prediction method, landslide prediction device, landslide prediction equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the invention discloses a landslide prediction method, a landslide prediction device, landslide prediction equipment and a storage medium. The method comprises the steps of obtaining landslide influence factors of each slope unit in a target area every day within a set time period, wherein the landslide influence factors comprise dynamic factors and static factors, determining dynamic probability values of occurrence of landslide of each slope unit based on the dynamic factors, determining static probability values of occurrence of landslide of each slope unit based on the static factors, and carrying out landslide prediction by separating the dynamic factors and the static factors, so that the leakage rate and the false alarm rate in the landslide prediction process can be reduced; and obtaining the landslide occurrence probability of the target area according to the dynamic probability value and the static probability value, and carrying out joint prediction by combining the corresponding probability values of the two types of landslide influence factors to carry out landslide prediction by combining the characteristics of the two types of landslide influence factors, so that the landslide prediction precision can be further improved.
Description
Technical Field
The embodiment of the invention relates to the technical field of landslide monitoring, in particular to a landslide prediction method, a landslide prediction device, landslide prediction equipment and a storage medium.
Background
Landslide is one of the most common disastrous natural disasters, has the characteristics of wide distribution range, high occurrence frequency, multiple occurrence, regional property, severity and the like, and can cause a large amount of casualties and serious environmental and infrastructure losses every year. The method has important significance in evaluating the liability of landslide.
The existing landslide susceptibility prediction can be divided into a deterministic method and a non-deterministic method according to the difference of theoretical basis on which the landslide susceptibility prediction is based. The deterministic method is mainly a directional analysis based on expert experience and knowledge and a landslide process or physical model analysis method, and the prediction accuracy is poor. With the rapid development of computer technology and 3S technology in recent years, non-deterministic methods have been widely used, mainly including fuzzy logic methods, analytic hierarchy processes, decision trees, and the like. However, landslide has a large number of influencing factors, and prediction efficiency is poor by only adopting one uncertain method for prediction.
Disclosure of Invention
The invention provides a landslide prediction method, a landslide prediction device, landslide prediction equipment and a storage medium, which realize the effect of improving landslide prediction precision.
In a first aspect, an embodiment of the present invention provides a method for predicting a landslide, including:
acquiring landslide influence factors of each slope unit in a target area every day within a set time period, wherein the landslide influence factors comprise dynamic factors and static factors;
Determining a dynamic probability value of each slope unit for landslide based on the dynamic factors, and determining a static probability value of each slope unit for landslide based on the static factors;
And obtaining the landslide occurrence probability of the target area according to the dynamic probability value and the static probability value.
In a second aspect, an embodiment of the present invention further provides a landslide prediction apparatus, including:
The landslide influence factor acquisition module is used for acquiring the landslide influence factors of each slope unit in the target area every day in a set time period, wherein the landslide influence factors comprise dynamic factors and static factors;
The dynamic probability value determining module is used for determining the dynamic probability value of each slope unit for generating landslide based on the dynamic factors;
the static probability value determining module is used for determining the static probability value of each slope unit for generating landslide based on the static factors;
and the landslide occurrence probability determining module is used for obtaining the landslide occurrence probability of the target area according to the dynamic probability value and the static probability value.
In a third aspect, an embodiment of the present invention further provides a landslide prediction apparatus, including:
one or more processors;
a memory for storing one or more programs;
When the one or more programs are executed by the one or more processors, the one or more processors implement the landslide prediction method provided by any embodiment of the present invention.
In a fourth aspect, embodiments of the present invention also provide a storage medium containing computer-executable instructions, which when executed by a computer processor, are used to perform the method of predicting landslide provided by any of the embodiments of the present invention.
According to the technical scheme, the landslide influence factors of each slope unit in the target area are obtained, wherein the landslide influence factors comprise dynamic factors and static factors, the dynamic probability value of landslide occurrence of each slope unit is determined based on the dynamic factors, the static probability value of landslide occurrence of each slope unit is determined based on the static factors, and the dynamic factors and the static factors are separated for landslide prediction, so that the missing report rate and the false report rate in the landslide prediction process can be reduced; and obtaining the landslide occurrence probability of the target area according to the dynamic probability value and the static probability value, and carrying out joint prediction by combining the corresponding probability values of the two types of landslide influence factors to carry out landslide prediction by combining the characteristics of the two types of landslide influence factors, so that the landslide prediction precision can be further improved.
Drawings
FIG. 1 is a flow chart of a method for predicting landslide in accordance with a first embodiment of the invention;
FIG. 2 is a flow chart of a method for predicting landslide in accordance with a second embodiment of the present invention;
FIG. 3 is a logic diagram of a landslide prediction method according to a second embodiment of the present invention;
Fig. 4 is a schematic structural diagram of a landslide prediction device in accordance with a third embodiment of the present invention;
Fig. 5 is a schematic structural diagram of a landslide prediction apparatus in accordance with a fourth embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present invention are shown in the drawings.
Example 1
Fig. 1 is a flowchart of a landslide prediction method according to an embodiment of the present invention, where the method may be performed by a landslide prediction device, and the method includes the following steps:
s110, acquiring landslide influence factors of each slope unit in the target area every day in a set time period.
The target area is usually an area where landslide occurs, and may be any designated area. The slope unit is a basic unit for the development of geologic hazards such as landslide, collapse and the like, and corresponding attribute values are assigned to each unit to represent a data form of the entity. The set period of time may be one day, three days, one week, one month, or other period of time prior to the day of landslide occurrence.
Wherein the landslide impact factor comprises a dynamic factor comprising at least one of rainfall, vegetation coefficient, and soil moisture, and a static factor comprising at least one of elevation, slope direction, planar curvature, profile curvature, terrain moisture index, water current intensity index, sediment transport index, terrain roughness index, distance to fault, distance to river, distance to road, lithology, land utilization, and vegetation coverage. Specifically, a plurality of monitoring points can be set in the target area to acquire landslide prediction data of each monitoring point in real time. And further combining the data collected by the preset departments to form landslide prediction data of each slope unit of the target area every day in a set time period.
Optionally, after acquiring the landslide impact factor of each ramp unit in the target area for each day in the set period of time, the method further includes: and preprocessing the landslide impact factors, wherein the preprocessing comprises at least one of coordinate unification processing and correction processing.
Since the landslide impact factors originate from different slope units, the coordinates of the landslide impact factors are not uniform and abnormal values are avoided. Therefore, after the landslide impact factor is acquired, it is necessary to perform coordinate unification processing and correction processing for the landslide impact factor. Naturally, the abnormal value removal, the data discretization processing, and the like may be performed according to the specific situation of the landslide impact factor.
S120, determining dynamic probability values of landslide occurrence of each slope unit based on the dynamic factors, and determining static probability values of landslide occurrence of each slope unit based on the static factors.
Because many factors influence landslide occurrence, only according to static factors to predict whether the landslide occurs in a target area, landslide prediction information cannot be changed for a long time, so that falling rain type landslide, falling snow type landslide and the like are easy to report; the geological conditions of some slope units are very stable, and whether landslide occurs is predicted only according to rainfall information, so that large-area false alarm is easily caused. Therefore, the embodiment determines the dynamic probability value of each slope unit based on the dynamic factors, and determines the static probability value of each slope unit based on the static factors, so that the false alarm rate and the false alarm rate in the landslide prediction process can be reduced.
Optionally, the determining the dynamic probability value of each slope unit for landslide based on the dynamic factor, and determining the static probability value of each slope unit for landslide based on the static factor include: inputting the dynamic factors into a pre-trained first prediction model, and determining dynamic probability values of landslide occurrence of each slope unit; and inputting the static factors into a pre-trained second prediction model, and determining the static probability value of landslide of each slope unit.
Wherein the first and second predictive models may be neural network models, or other learning algorithms. Illustratively, the first predictive model may be a support vector machine algorithm (Support Vector Machine, SVM), a logistic regression model (Logistics Regression, LR), a gradient boosting decision tree algorithm, or the like; the second predictive model may be a Long Short-Term Memory Network (LSTM), a support vector machine algorithm (Support Vector Machine, SVM), a gated loop unit (Gate Recurrent Unit, GRU), XGBoost (Extreme Gradient Boosting, extreme gradient boost decision tree) algorithm, GBDT (Gradient Boosting Decision Tree, gradient boost decision tree) algorithm, a full convolution Network (Fully Convolutional Networks, FCN), a cyclic convolution Network (Recurrent Neural Network, RNN), a Residual Network (ResNet), and the like.
Specifically, the training process of the first prediction model and the second prediction model is as follows: basic information of landslide of a target area or all areas is extracted from files such as landslide field investigation reports, typical landslide monitoring reports and the like, including landslide occurrence time, longitude and latitude, disaster-affected scale, dynamic factors of each slope unit, static factors and the like, a training set and a verification set are formed according to the landslide information set proportion, and characteristics of each landslide influence factor are extracted through characteristic engineering to form an input characteristic matrix of a first prediction model and a second prediction model, wherein the input characteristic matrix is a set of the training set and the verification set, such as 8:2 or 7:3; initializing parameters of a first prediction model and a second prediction model respectively, inputting a feature matrix into the first prediction model and the second prediction model respectively, carrying out model training, predicting dynamic probability values and static probability values of each day, adjusting model parameters of the first prediction model based on the predicted dynamic probability values and dynamic probability values in a training set, and adjusting model parameters of the second prediction model based on the predicted static probability values and static probability values in the training set until the first prediction model and the second prediction model reach a stable state, and obtaining a trained first prediction model and second prediction model; and further determining evaluation indexes of the first prediction model and the second prediction model which are subjected to training based on the training set, and evaluating the first prediction model and the second prediction model based on the evaluation indexes. Wherein the evaluation index may be an F1-value (F1-Score), an ROC (Receiver Operating Characteristic, receiver operating characteristics), or other evaluation index.
S130, obtaining the landslide occurrence probability of the target area according to the dynamic probability value and the static probability value.
Optionally, the obtaining the landslide occurrence probability of the target area according to the dynamic probability value and the static probability value includes: and inputting the dynamic probability value and the static probability value into a third prediction model to obtain the landslide occurrence probability of the target area.
The third prediction model may be a logistic regression model (Logistics Regression, LR), a support vector machine algorithm (Support Vector Machine, SVM), or the like. The third prediction model can be obtained by training based on landslide information of each slope unit of at least one area in the historical time period, wherein the landslide information comprises landslide occurrence time, landslide points, non-landslide points, disaster degree and the like.
According to the technical scheme, the landslide influence factors of each slope unit in the target area are obtained, wherein the landslide influence factors comprise dynamic factors and static factors, the dynamic probability value of landslide occurrence of each slope unit is determined based on the dynamic factors, the static probability value of landslide occurrence of each slope unit is determined based on the static factors, and the dynamic factors and the static factors are separated for landslide prediction, so that the missing report rate and the false report rate in the landslide prediction process can be reduced; and obtaining the landslide occurrence probability of the target area according to the dynamic probability value and the static probability value, and carrying out joint prediction by integrating the corresponding probability values of the two types of landslide influence factors so as to carry out landslide prediction by combining the characteristics of the two types of landslide influence factors, thereby further improving the prediction precision of landslide.
Example two
Fig. 2 is a flowchart of a landslide prediction method according to a second embodiment of the present invention, where the embodiment is further elaborated on the previous embodiment, and the landslide prediction method provided by the present embodiment further includes: determining whether the change value of the landslide impact factor in a set time period exceeds a preset change threshold value; and if so, determining the landslide impact factor as the dynamic factor, otherwise, determining the landslide impact factor as the static factor. For parts which are not described in detail in this method embodiment, reference is made to the above-described embodiments. Referring specifically to fig. 2, the method may include the steps of:
s210, acquiring landslide influence factors of each slope unit in the target area every day in a set time period.
Wherein the landslide impact factor includes a dynamic factor and a static factor.
S220, determining whether a change value of the landslide impact factor in a set time period exceeds a preset change threshold value, if so, determining the landslide impact factor as a dynamic factor, otherwise, determining the landslide impact factor as a static factor.
Wherein the set period of time may be half a day, one day, three days, etc. The prediction change threshold may be a minimum value. Since the static factor does not change over a longer period of time, the dynamic factor changes over time. For example, factors such as soil category, slope direction, gradient and the like are stable, cannot change for a long time, and rainfall and vegetation coefficient stability are poor and can change with time. Therefore, whether the change value of the landslide influence factor in the set time period exceeds a preset change threshold value is determined, if yes, the landslide influence factor is determined to be a dynamic factor, otherwise, the landslide influence factor is determined to be a static factor, and landslide prediction is further performed by respectively predicting the landslide static factor and the dynamic factor.
S230, inputting dynamic factors into a first pre-trained prediction model, determining dynamic probability values of landslide occurrence of each slope unit, inputting static factors into a second pre-trained prediction model, and determining static probability values of landslide occurrence of each slope unit.
Wherein the first prediction model and the second prediction model may be a single model or may include a plurality of sub-networks.
S240, taking the dynamic probability value and the static probability value as input variables of a maximum expected algorithm in the third prediction model, determining a maximum likelihood estimation amount based on the maximum expected algorithm, and determining the landslide occurrence probability of the target area according to the maximum likelihood estimation amount.
Specifically, the dynamic probability value and the static probability value of each ramp unit are used as input variables of a third prediction model, likelihood functions of each ramp unit are calculated based on a maximum expected algorithm in the third prediction model, and the likelihood estimator when the likelihood functions are maximum is used as the maximum likelihood estimator. Wherein, the calculation formula of the likelihood function is as follows:
where p (x i,Zj) is the probability value for each ramp unit, including the probability value for the landslide point and the probability value for the non-landslide point, x i is the data set for the ramp unit, i is the encoding of the ramp unit, Z j is an implicit variable, including landslide occurrence and non-landslide occurrence, j is 0 or 1, and θ is the likelihood estimator. Specifically, the maximum expectation algorithm is based on the dynamic probability value and the static probability value of each slope unit, and iteratively solves the likelihood function according to the implicit variable Z j until the likelihood function reaches the maximum, determines the likelihood estimation quantity theta when the likelihood function is maximum, and takes the likelihood estimation quantity theta when the likelihood function is maximum as the maximum likelihood estimation quantity.
Optionally, the determining a maximum likelihood estimator based on the maximum expectation algorithm, determining a landslide occurrence probability of the target area according to the maximum likelihood estimator includes: the dynamic probability value and the static probability value are input into a fourth prediction model to obtain a probability prediction value of the target area; and determining the landslide occurrence probability of the target area based on the weights respectively corresponding to the probability predicted value, the maximum likelihood estimated value and the maximum likelihood estimated value.
The fourth prediction model may be a logistic regression model, a support vector machine algorithm, and the like. The weights may be determined during a training phase of the third predictive model and the fourth predictive model. Specifically, fig. 3 is a logic schematic diagram of a landslide prediction method. Fig. 3 includes a first layer model and a second layer model, the first layer model includes a first prediction model and a second prediction model, the second layer model includes a third prediction model and a fourth prediction model, weighting calculation is performed on a maximum likelihood estimator output by the third prediction model, a probability value output by the fourth prediction model, and weights corresponding to the probability predictor and the maximum likelihood estimator, respectively, and a result of the weighting calculation is taken as a landslide occurrence probability of the target area. Of course, the second layer model in fig. 3 may further include a fifth prediction model, where the fifth prediction model may be a logistic regression model, a support vector machine algorithm, and the like, and the maximum likelihood estimator output by the third prediction model, the probability value output by the fourth prediction model, the probability value output by the fifth prediction model, and the maximum likelihood estimator and weights corresponding to the maximum likelihood estimator and the maximum likelihood estimator respectively perform weighted calculation, and the weighted calculation result is taken as the landslide occurrence probability of the target area. Of course, the present embodiment may also use the maximum likelihood estimator directly as the probability of landslide occurrence of the target area, and the second layer model may also include a plurality of prediction models.
According to the technical scheme, the dynamic probability value and the static probability value are used as input variables of the maximum expected algorithm in the third prediction model, the dynamic probability value and the static probability value are input into the fourth prediction model, the probability prediction value of each slope unit of the target area is obtained, the landslide occurrence probability of the target area is determined based on the probability prediction value, the maximum likelihood estimation value and weights corresponding to the probability prediction value and the maximum likelihood estimation value respectively, the landslide occurrence probability is determined by combining the prediction results of the multiple models, and the prediction accuracy of the landslide occurrence probability of the target area can be improved.
Example III
Fig. 4 is a schematic diagram of the result of a landslide prediction device according to a third embodiment of the present invention, where, as shown in fig. 4, the landslide prediction device includes: a landslide impact factor acquisition module 310, a dynamic probability value determination module 320, a static probability value determination module 330, and a landslide occurrence probability determination module 340.
The landslide influence factor obtaining module 310 is configured to obtain a landslide influence factor of each slope unit in a target area every day in a set period of time, where the landslide influence factor includes a dynamic factor and a static factor;
A dynamic probability value determining module 320, configured to determine a dynamic probability value of each ramp unit generating a landslide based on the dynamic factor;
A static probability value determining module 330, configured to determine a static probability value of each ramp unit for landslide based on the static factor;
and a landslide occurrence probability determining module 340, configured to obtain a landslide occurrence probability of the target area according to the dynamic probability value and the static probability value.
According to the technical scheme, the landslide influence factors of each slope unit in the target area are obtained, wherein the landslide influence factors comprise dynamic factors and static factors, the dynamic probability value of landslide occurrence of each slope unit is determined based on the dynamic factors, the static probability value of landslide occurrence of each slope unit is determined based on the static factors, and the dynamic factors and the static factors are separated for landslide prediction, so that the missing report rate and the false report rate in the landslide prediction process can be reduced; and obtaining the landslide occurrence probability of the target area according to the dynamic probability value and the static probability value, and carrying out joint prediction by combining the corresponding probability values of the two types of landslide influence factors to carry out landslide prediction by combining the characteristics of the two types of landslide influence factors, so that the landslide prediction precision can be further improved.
Optionally, the dynamic probability value determining module 320 is further configured to input the dynamic factor into a pre-trained first prediction model, and determine a dynamic probability value of each ramp unit for landslide.
Optionally, the static probability value determining module 330 is further configured to input the static factor into a second pre-trained prediction model, and determine a static probability value of each ramp unit for landslide.
Optionally, the landslide occurrence probability determining module 340 is further configured to input the dynamic probability value and the static probability value into a third prediction model, so as to obtain the landslide occurrence probability of the target area.
Optionally, the landslide occurrence probability determining module 340 is further configured to use the dynamic probability value and the static probability value as input variables of a maximum expectation algorithm in the third prediction model, determine a maximum likelihood estimator based on the maximum expectation algorithm, and determine the landslide occurrence probability of the target area according to the maximum likelihood estimator.
Optionally, the landslide occurrence probability determining module 340 is further configured to input the dynamic probability value and the static probability value into a fourth prediction model to obtain a probability prediction value of the target area;
And determining landslide occurrence probability of the target area based on the probability prediction value, the maximum likelihood estimator and weights respectively corresponding to the probability prediction value and the maximum likelihood estimator.
Optionally, the apparatus further comprises: a judging module; the judging module is used for determining whether the change value of the landslide impact factor in the set time period exceeds a preset change threshold value;
and if so, determining the landslide impact factor as the dynamic factor, otherwise, determining the landslide impact factor as the static factor.
Optionally, the dynamic factor includes at least one of rainfall, vegetation coefficient and soil humidity, and the static factor includes at least one of elevation, slope direction, planar curvature, profile curvature, terrain humidity index, water current intensity index, sediment transport index, terrain roughness index, distance to fault, distance to river, distance to road, lithology, land utilization and vegetation coverage.
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 IV
Fig. 5 is a schematic structural diagram of a landslide prediction apparatus according to a fourth embodiment of the present invention. Fig. 5 shows a block diagram of an exemplary landslide prediction apparatus 12 suitable for use in implementing embodiments of the invention. The landslide prediction apparatus 12 shown in fig. 5 is merely an example, and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.
As shown in fig. 5, the prediction device 12 of the landslide is in the form of a general purpose computing device. Components of the landslide prediction apparatus 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, a bus 18 that connects the various system components, including the system memory 28 and the processing units 16.
Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, micro channel architecture (MAC) bus, enhanced ISA bus, video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Landslide prediction device 12 typically comprises a variety of computer system readable media. Such media can be any available media that can be accessed by the landslide prediction device 12 and includes both volatile and nonvolatile media, removable and non-removable media.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 30 and/or cache memory 32. Landslide prediction device 12 may further comprise other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from or write to non-removable, nonvolatile magnetic media (not shown in FIG. 5, commonly referred to as a "hard disk drive"). Although not shown in fig. 5, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In such cases, each drive may be coupled to bus 18 through one or more data medium interfaces. The system memory 28 may include at least one program product having a set of program modules (e.g., a landslide impact factor acquisition module 310, a dynamic probability value determination module 320, a static probability value determination module 330, and a landslide occurrence probability determination module 340 of a landslide prediction device) configured to perform the functions of the various embodiments of the invention.
The program/utility 44 having a set of program modules 46 (e.g., landslide impact factor acquisition module 310, dynamic probability value determination module 320, static probability value determination module 330, and landslide occurrence probability determination module 340) of predictive devices for landslide may be stored in, for example, system memory 28, such program modules 46 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules 46 generally perform the functions and/or methods of the embodiments described herein.
The landslide prediction device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), with one or more devices that enable a user to interact with the landslide prediction device 12, and/or with any device (e.g., network card, modem, etc.) that enables the landslide prediction device 12 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 22. Also, the landslide prediction device 12 may also communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the internet via the network adapter 20. As shown, the network adapter 20 communicates with other modules of the landslide prediction device 12 via the bus 18. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in connection with the landslide prediction apparatus 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, for example, to implement a landslide prediction method provided by an embodiment of the present invention, the method including:
acquiring landslide influence factors of each slope unit in a target area every day within a set time period, wherein the landslide influence factors comprise dynamic factors and static factors;
Determining a dynamic probability value of each slope unit for landslide based on the dynamic factors, and determining a static probability value of each slope unit for landslide based on the static factors;
And obtaining the landslide occurrence probability of the target area according to the dynamic probability value and the static probability value.
The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, for example, to implement a landslide prediction method provided by an embodiment of the present invention.
Of course, those skilled in the art will understand that the processor may also implement the technical solution of the landslide prediction method provided in any embodiment of the present invention.
Example five
The fifth embodiment of the present invention further provides a computer readable storage medium having a computer program stored thereon, the program when executed by a processor implementing a landslide prediction method as provided by the embodiment of the present invention, the method comprising:
acquiring landslide influence factors of each slope unit in a target area every day within a set time period, wherein the landslide influence factors comprise dynamic factors and static factors;
Determining a dynamic probability value of each slope unit for landslide based on the dynamic factors, and determining a static probability value of each slope unit for landslide based on the static factors;
And obtaining the landslide occurrence probability of the target area according to the dynamic probability value and the static probability value.
Of course, the computer readable storage medium provided by the embodiments of the present invention, on which the computer program stored is not limited to the above method operations, may also perform the related operations in the landslide prediction method provided by any of the embodiments of the present invention.
The computer storage media of embodiments of the invention may take the form of any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, or device.
The computer readable signal medium may include dynamic probability values, static probability values, landslide occurrence probabilities, and the like, in which computer readable program code is carried. Such propagated dynamic probability values, static probability values, landslide occurrence probabilities, and the like. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
It should be noted that, in the embodiment of the landslide prediction apparatus described above, each included module is only divided according to the functional logic, but not limited to the above-mentioned division, as long as the corresponding function can be implemented; in addition, the specific names of the functional units are also only for distinguishing from each other, and are not used to limit the protection scope of the present invention.
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (7)
1. A method of predicting landslide, comprising:
acquiring landslide influence factors of each slope unit in a target area every day within a set time period, wherein the landslide influence factors comprise dynamic factors and static factors;
Determining a dynamic probability value of each slope unit for landslide based on the dynamic factors, and determining a static probability value of each slope unit for landslide based on the static factors;
Obtaining landslide occurrence probability of the target area according to the dynamic probability value and the static probability value;
The obtaining the landslide occurrence probability of the target area according to the dynamic probability value and the static probability value comprises the following steps:
Inputting the dynamic probability value and the static probability value into a third prediction model to obtain landslide occurrence probability of the target area;
inputting the dynamic probability value and the static probability value into a third prediction model to obtain the landslide occurrence probability of the target area, wherein the method comprises the following steps:
The dynamic probability value and the static probability value are used as input variables of a maximum expected algorithm in a third prediction model, a maximum likelihood estimator is determined based on the maximum expected algorithm, and the landslide occurrence probability of the target area is determined according to the maximum likelihood estimator;
the determining a maximum likelihood estimator based on the maximum expectation algorithm, determining a landslide occurrence probability of the target area according to the maximum likelihood estimator, includes:
inputting the dynamic probability value and the static probability value into a fourth prediction model to obtain a probability prediction value of a target area;
And determining landslide occurrence probability of the target area based on the probability prediction value, the maximum likelihood estimator and weights respectively corresponding to the probability prediction value and the maximum likelihood estimator.
2. The prediction method according to claim 1, wherein the determining a dynamic probability value of occurrence of a landslide for each of the ramp units based on the dynamic factor, determining a static probability value of occurrence of a landslide for each of the ramp units based on the static factor, comprises:
Inputting the dynamic factors into a pre-trained first prediction model, and determining dynamic probability values of landslide occurrence of each slope unit;
And inputting the static factors into a pre-trained second prediction model, and determining the static probability value of landslide of each slope unit.
3. The prediction method according to claim 1, further comprising:
Determining whether the change value of the landslide impact factor in a set time period exceeds a preset change threshold value;
and if so, determining the landslide impact factor as the dynamic factor, otherwise, determining the landslide impact factor as the static factor.
4. The prediction method according to claim 1, wherein the dynamic factor comprises at least one of rainfall, vegetation coefficient and soil moisture, and the static factor comprises at least one of elevation, slope, planar curvature, profile curvature, terrain moisture index, water current strength index, sediment transport index, terrain roughness index, distance to fault, distance to river, distance to road, lithology, land utilization and vegetation coverage.
5. A landslide prediction apparatus comprising:
The landslide influence factor acquisition module is used for acquiring the landslide influence factors of each slope unit in the target area every day in a set time period, wherein the landslide influence factors comprise dynamic factors and static factors;
The dynamic probability value determining module is used for determining the dynamic probability value of each slope unit for generating landslide based on the dynamic factors;
the static probability value determining module is used for determining the static probability value of each slope unit for generating landslide based on the static factors;
the landslide occurrence probability determining module is used for obtaining the landslide occurrence probability of the target area according to the dynamic probability value and the static probability value;
The obtaining the landslide occurrence probability of the target area according to the dynamic probability value and the static probability value comprises the following steps:
Inputting the dynamic probability value and the static probability value into a third prediction model to obtain landslide occurrence probability of the target area;
inputting the dynamic probability value and the static probability value into a third prediction model to obtain the landslide occurrence probability of the target area, wherein the method comprises the following steps:
The dynamic probability value and the static probability value are used as input variables of a maximum expected algorithm in a third prediction model, a maximum likelihood estimator is determined based on the maximum expected algorithm, and the landslide occurrence probability of the target area is determined according to the maximum likelihood estimator;
the determining a maximum likelihood estimator based on the maximum expectation algorithm, determining a landslide occurrence probability of the target area according to the maximum likelihood estimator, includes:
inputting the dynamic probability value and the static probability value into a fourth prediction model to obtain a probability prediction value of a target area;
And determining landslide occurrence probability of the target area based on the probability prediction value, the maximum likelihood estimator and weights respectively corresponding to the probability prediction value and the maximum likelihood estimator.
6. A prediction apparatus for landslide, characterized in that the apparatus comprises:
one or more processors;
a memory for storing one or more programs;
When executed by the one or more processors, causes the one or more processors to implement the landslide prediction method of any one of claims 1-4.
7. A storage medium containing computer executable instructions which, when executed by a computer processor, are for performing a method of landslide prediction as claimed in any one of claims 1 to 4.
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