A method and device for determining critical rainfall threshold for landslide prediction

Abstract

The present invention discloses a method and device for determining a critical rainfall threshold for landslide prediction, the method comprising: determining a landslide kernel density spatial distribution map based on a complete catalog of landslides caused by extreme rainfall clusters; obtaining regional rainfall data corresponding to each landslide in the clusters based on a preset time window, the regional rainfall data including previous rainfall data and current day rainfall data; performing correlation processing on the landslide kernel density spatial distribution map and the regional rainfall data using the Pearson correlation coefficient to determine the optimal regional rainfall data; determining an optimal fitting curve based on the optimal regional rainfall data and the landslide surface density spatial distribution map, and converting the optimal fitting curve into a probability distribution function based on a rainfall interval range, the probability distribution function being specifically a probability distribution function relationship curve between landslide surface density and rainfall; using the landslide surface density as an absolute scale, generating an early warning matrix based on the probability distribution function, thereby improving the accuracy and applicability of the critical rainfall threshold for landslide prediction and alarm.

Description

Technical Field

[0001] The present invention belongs to the field of computer technology, and in particular relates to a method and device for determining a critical rainfall threshold for landslide prediction. Background Art

[0002] Rainfall, as an important triggering factor of landslide disasters, is an important indicator for landslide warning or risk assessment. In the current context of climate change, landslides and other disasters caused by extreme rainfall are becoming more and more frequent, resulting in a large number of casualties and property losses.

[0003] There are two types of research on the critical rainfall threshold during landslides in the existing technology. One is a deterministic model based on physical and mechanical mechanisms. By simulating the impact of rainfall infiltration on the changes in parameters such as slope geotechnical strength, moisture content, and groundwater level, the stability of the landslide is quantitatively analyzed to determine the critical rainfall threshold that induces landslide instability. However, this method has difficulty in obtaining geotechnical parameter data, resulting in insufficient accuracy. The other is a study of the mathematical relationship between rainfall and landslides based on mathematical statistics models. Through historical random landslide disaster events and their corresponding process rainfall data, mathematical fitting methods such as linear regression are used to establish pairwise relationships between rainfall parameters such as average rainfall intensity, accumulated rainfall, and rainfall duration. The critical rainfall threshold curve is determined based on the probability of landslide occurrence. This method is more suitable for determining critical rainfall thresholds for landslides and risk warnings at the regional scale, and has become the main method for studying critical rainfall thresholds for regional landslides. However, this method has the following problems: First, the rainfall type is a random rainfall process with different types of historical landslide disasters, which is not suitable for early warning scenarios of extreme rainfall disasters in the context of climate change. Second, the required data base is based on multiple landslide disaster events and their corresponding process rainfall monitoring data, which requires high temporal and spatial accuracy and a large amount of data. However, the current environment lacks accurate historical monitoring records of landslides and rainfall, especially monitoring data that accurately corresponds to the two, which greatly reduces the accuracy of early warnings. Third, the selection of rainfall parameters for landslide rainfall threshold research is relatively subjective, mostly rainfall parameters such as average rainfall intensity, cumulative rainfall, and rainfall duration, which fail to effectively reflect the water and soil infiltration process and the disaster-causing mechanism of rainfall-induced landslides. Fourth, the critical rainfall threshold division is achieved based on fitting threshold curves based on the probability of landslide occurrence, and sometimes the threshold curves will cross, resulting in the problem of confusion between threshold levels and thresholds, resulting in great uncertainty in the threshold results.

[0004] Therefore, how to improve the accuracy and applicability of the critical rainfall threshold for landslide prediction and alarm is a technical problem to be solved by those skilled in the art. Summary of the Invention

[0005] The purpose of the present invention is to solve the technical problems in the prior art of low accuracy and low adaptability of critical rainfall thresholds for landslide prediction and alarm.

[0006] To achieve the above technical objectives, the present invention provides, on the one hand, a method for determining a critical rainfall threshold for landslide prediction, the method comprising:

[0007] Based on the complete cataloging of landslide clusters caused by extreme rainfall, the spatial distribution map of landslide kernel density was determined;

[0008] Acquiring regional rainfall data corresponding to each landslide in a cluster based on a preset time window, wherein the regional rainfall data includes previous rainfall data and current day rainfall data;

[0009] The optimal regional rainfall data was determined by correlating the landslide kernel density spatial distribution map with regional rainfall data using the Pearson correlation coefficient.

[0010] Determining an optimal fitting curve based on the rainfall data of the optimal area and the spatial distribution map of the landslide surface density, and converting the optimal fitting curve into a probability distribution function based on a rainfall interval range, wherein the probability distribution function is specifically a probability distribution function relationship curve between the landslide surface density and the rainfall;

[0011] Taking the landslide surface density as an absolute scale, an early warning matrix is ​​generated based on the probability distribution function.

[0012] Furthermore, the previous rainfall data Re It is determined specifically by the following formula:

[0013] ;

[0014] Where, R 0 The rainfall data for the day. a is the effective reduction factor, R n is the rainfall on the nth day of the previous period.

[0015] Furthermore, the correlation processing is specifically performed by the following formula:

[0016] ;

[0017] Where, is the Pearson correlation coefficient, is the number of days in the previous period, The first factor to be compared, is the second factor to be compared, is the mean value of the first factor, is the mean value of the second factor.

[0018] Furthermore, determining the optimal fitting curve based on the optimal regional rainfall data and the landslide surface density spatial distribution map specifically includes:

[0019] Divide the optimal regional rainfall data into rainfall intervals;

[0020] The landslide surface density and its corresponding rainfall range are placed in the preset software for comparison and optimization to determine the optimal fitting curve.

[0021] Furthermore, converting the optimal fitting curve into a probability distribution function based on the rainfall interval range specifically includes:

[0022] Converting the best-fit curve into a standard probability density function within the rainfall interval, wherein constraints of the probability density function include non-negativity and normalization;

[0023] The probability density function is converted into a probability distribution function.

[0024] Furthermore, the above-mentioned method of generating a warning matrix based on the probability distribution function using the landslide surface density as an absolute scale specifically includes:

[0025] Dividing the landslide surface density into intervals according to preset warning levels to obtain multiple levels of landslide surface density warning intervals;

[0026] Determining the previous rainfall and the current day rainfall in each landslide surface density warning interval based on the probability distribution function;

[0027] An early warning matrix is ​​generated based on the previous rainfall and the current day rainfall corresponding to each landslide surface density early warning interval.

[0028] On the other hand, the present invention also provides a device for determining a critical rainfall threshold for landslide prediction, the device comprising:

[0029] A determination module is used to determine the spatial distribution map of landslide kernel density based on a complete catalog of landslide clusters caused by extreme rainfall;

[0030] An acquisition module is used to acquire regional rainfall data corresponding to each landslide in a group based on a preset time window, wherein the regional rainfall data includes previous rainfall data and current day rainfall data;

[0031] The correlation module is used to perform correlation processing on the landslide kernel density spatial distribution map and regional rainfall data through the Pearson correlation coefficient to determine the optimal regional rainfall data;

[0032] A fitting module is used to determine an optimal fitting curve based on the rainfall data of the optimal area and the spatial distribution map of the landslide surface density, and convert the optimal fitting curve into a probability distribution function based on the rainfall interval range, wherein the probability distribution function is specifically a probability distribution function relationship curve of the landslide surface density and rainfall;

[0033] The generating module is used to generate an early warning matrix based on the probability distribution function with the landslide surface density as an absolute scale.

[0034] The present invention provides a method and device for determining a critical rainfall threshold for landslide prediction. Compared with the prior art, the method comprises: determining a landslide kernel density spatial distribution map based on a complete catalog of landslides caused by extreme rainfall clusters; obtaining regional rainfall data corresponding to each landslide in the cluster based on a preset time window, wherein the regional rainfall data includes previous rainfall data and current-day rainfall data; performing correlation processing on the landslide kernel density spatial distribution map and the regional rainfall data using the Pearson correlation coefficient to determine the optimal regional rainfall data; determining an optimal fitting curve based on the optimal regional rainfall data and the landslide surface density spatial distribution map, and converting the optimal fitting curve into a probability distribution function based on a rainfall interval range, wherein the probability distribution function is specifically a probability distribution function relationship curve between landslide surface density and rainfall; and generating an early warning matrix based on the probability distribution function using the landslide surface density as an absolute scale, thereby improving the accuracy and applicability of the critical rainfall threshold for landslide prediction and alarm. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of this specification or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0036] Figure 1 1 is a flow chart of a method for determining a critical rainfall threshold for landslide prediction provided by an embodiment of this specification;

[0037] Figure 2 1 is a schematic diagram of the structure of a device for determining a critical rainfall threshold for landslide prediction provided by an embodiment of this specification;

[0038] Figure 3 Shown is a landslide kernel density spatial distribution map of the target area in the embodiment of this specification;

[0039] Figure 4 Shown are the landslide kernel density spatial distribution map and regional rainfall data correlation statistics in the embodiments of this specification;

[0040] Figure 5 Shown is the spatial distribution map of effective rainfall 3 days before the landslide in the embodiment of this specification;

[0041] Figure 6 Shown is the spatial distribution diagram of the rainfall stimulated 5 hours before the landslide in the embodiment of this specification;

[0042] Figure 7 The figure shows a fitting curve diagram of effective rainfall 3 days before the landslide and landslide surface density in the embodiment of this specification;

[0043] Figure 8 The figure shows a fitting curve diagram of the rainfall induced 5 hours before the landslide and the landslide surface density in the embodiment of this specification;

[0044] Figure 9 Shown is a schematic diagram of the effective rainfall and probability distribution function three days before the landslide in the embodiment of this specification;

[0045] Figure 10 Shown is a schematic diagram of the rainfall stimulated 5 hours before the landslide and the probability distribution function in the embodiment of this specification. DETAILED DESCRIPTION

[0046] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of this application.

[0047] like Figure 1 The flowchart shown is a method for determining a critical rainfall threshold for landslide prediction, as provided in an embodiment of this specification. Although this specification provides the method operating steps or device structures shown in the following embodiments or figures, the method or device may include more or fewer operating steps or module units based on routine or no creative effort. In steps or structures that do not logically have a necessary causal relationship, the execution order of these steps or the module structure of the device are not limited to the execution order or module structure shown in the embodiments or figures of this specification. When the method or module structure is applied in actual devices, servers, or terminal products, it can be executed sequentially or in parallel according to the method or module structure shown in the embodiments or figures (for example, in a parallel processor or multi-threaded processing environment, or even in a distributed processing or server cluster implementation environment).

[0048] The method for determining the critical rainfall threshold for landslide prediction provided in the embodiments of this specification can be applied to terminal devices such as clients and servers, such as Figure 1 As shown, the method specifically includes the following steps:

[0049] Step S101: Determine a landslide kernel density spatial distribution map based on a complete catalog of landslides caused by extreme rainfall.

[0050] Specifically, based on the landslide inventory results, the kernel density analysis tool was used in ArcGIS to generate and draw the landslide kernel density spatial distribution map of the target area.

[0051] Step S102: obtaining regional rainfall data corresponding to each landslide in the cluster based on a preset time window, wherein the regional rainfall data includes previous rainfall data and current day rainfall data.

[0052] Specifically, the pre-fall data refers to the rainfall data from the five days before the landslide, while the on-day rainfall data refers to the hourly rainfall data from the day before the landslide. For example, the schematic area in a certain location experienced four consecutive periods of heavy rainfall from June 19 to July 26, 2013. The final phase of heavy rainfall occurred from July 20 to July 25. In particular, on July 25, the schematic area experienced widespread heavy rainfall, indicating a dense population and a concentrated distribution of landslides. For rainfall data collection, rainfall data was collected from 33 stations within and surrounding the schematic area. Hourly rainfall data from each station from July 20 to 25 was extracted and compiled. Using the Kriging interpolation tool in ArcGIS, the rainfall data from each station was spatially gridded to a precision of 1 km, with daily rainfall (July 20 to 24) and hourly rainfall (July 24).

[0053] Based on the obtained rainfall grids, the effective rainfall (effective coefficient The spatial distribution raster layers of rainfall for 31 rainfall scenarios were taken, including the previous effective rainfall of 0.5~0.9) and the daily stimulated rainfall of the accumulated rainfall from 1 hour to 6 hours before.

[0054] In the embodiment of the present application, the previous rainfall data Re It is determined specifically by the following formula:

[0055] ;

[0056] Where, R 0 The rainfall data for the day. a is the effective reduction factor, R n is the rainfall on the nth day of the previous period.

[0057] Step S103: performing correlation processing on the landslide kernel density spatial distribution map and the regional rainfall data using the Pearson correlation coefficient to determine the optimal regional rainfall data.

[0058] Specifically, for the inversion study of extreme rainfall events in the schematic area, from the perspective of disaster mechanism, the above-mentioned rainfall scenarios such as the previous effective rainfall (daily segment) and the rainfall stimulated on the same day (time period) were analyzed with the landslide kernel density distribution for spatial correlation. The Pearson correlation coefficient was used as the comparison basis to screen the effective days of the previous effective rainfall, the effective reduction coefficient a, and the effective time period of the rainfall stimulated on the same day, such as the key parameters. Figure 4 The figure shows the spatial distribution map of landslide kernel density and the correlation statistical map of regional rainfall data. Finally, the effective rainfall in the first three days (a=0.8) and the rainfall stimulated in the first five hours are determined as the characteristic rainfall parameters for the study of the critical rainfall threshold of landslide in this embodiment. In other words, the effective rainfall in the early stage is extracted from the early stage rainfall data based on the effective reduction coefficient. The effective reduction coefficient is also Figure 4 The effective coefficient in Figure 4 The induced rainfall is also the induced rainfall on that day. Figure 4 The process rainfall in the period is also the effective rainfall in the early period, such as Figure 5 The figure shows the spatial distribution of effective rainfall three days before the landslide. The stimulated rainfall of the day was extracted from the rainfall data of the day, as shown in Figure 2. Figure 6 Shown is the spatial distribution of the induced rainfall 5 hours before the landslide.

[0059] In the embodiment of the present application, the correlation processing is specifically performed by the following formula:

[0060] ;

[0061] Where, is the Pearson correlation coefficient, is the number of days in the previous period, The first factor to be compared, is the second factor to be compared, is the mean value of the first factor, is the mean value of the second factor.

[0062] Step S104: determining an optimal fitting curve based on the rainfall data in the optimal area and the spatial distribution map of landslide surface density, and converting the optimal fitting curve into a probability distribution function based on the rainfall interval range, wherein the probability distribution function is specifically a probability distribution function relationship curve between landslide surface density and rainfall.

[0063] In an embodiment of the present application, determining the optimal fitting curve based on the optimal regional rainfall data and the landslide surface density spatial distribution map specifically includes:

[0064] Divide the optimal regional rainfall data into rainfall intervals;

[0065] The landslide surface density and its corresponding rainfall range are placed in the preset software for comparison and optimization to determine the optimal fitting curve.

[0066] Specifically, the two key characteristic rainfall parameters, effective rainfall over the preceding three days and induced rainfall over the preceding five hours, were divided into eight sub-intervals using the natural break method. The corresponding landslide surface density for each sub-interval was then calculated, as shown in Table 1 below. This objective was to enhance the positive correlation between rainfall and landslide density, thereby obtaining a better fitting curve and further ensuring the accuracy of the critical threshold results. It should be noted that the landslide core density refers to the distribution density of landslides within the "core region" per unit area. Here, "core region" refers to areas of concentrated landslide distribution, reflecting the overall landslide surface density distribution characteristics of the study area. This was used to analyze the spatial correlation between the spatial distribution of landslides in the study area and characteristic rainfall parameters from different time series, aiming to identify characteristic rainfall parameters with strong correlations with landslide development. The landslide surface density refers to the landslide surface density within each rainfall interval for the characteristic rainfall parameter, i.e., the overall density per unit area. This was used to statistically analyze the relationship between landslide development and rainfall within each rainfall interval, thereby achieving a subsequent fitting relationship analysis between rainfall and landslide surface density.

[0067] Table 1

[0068] ;

[0069] The characteristic rainfall parameter rainfall interval and the corresponding landslide surface density data were fitted with multiple models in Origin. As a basis for comparison, the final fitting curve was selected. The effective rainfall interval (x) of the previous three days and the landslide surface density (y) were subjected to nonlinear multi-class model fitting comparison and optimization. The results showed that the ExpDec1 model had the best fitting effect among the multi-class nonlinear curve fitting results, and the functional relationship was: ”, is 0.987, such as Figure 7 Shown is a schematic diagram of the fitting curve of effective rainfall three days before the landslide and landslide kernel density.

[0070] Similarly, the cumulative rainfall interval (x) and landslide surface density (y) 5 hours before the landslide were compared with the linear and nonlinear multi-type model fitting to find the best result. The results showed that the Gauss model had the best fitting effect among the multi-type nonlinear curve fitting results, and the functional relationship was: ”, is 0.921, such as Figure 8 The figure shows the fitting curve of the rainfall induced 5 hours before the landslide and the landslide kernel density.

[0071] In an embodiment of the present application, converting the optimal fitting curve into a probability distribution function based on the rainfall interval range specifically includes:

[0072] Converting the best-fit curve into a standard probability density function within the rainfall interval, wherein constraints of the probability density function include non-negativity and normalization;

[0073] The probability density function is converted into a probability distribution function.

[0074] Specifically, non-negativity means that f(x) ≥ 0 within the domain of definition; normalization means that the integral within the domain of definition must be 1. Preprocessing is required to calculate the constant k (the inverse of the integral value of the initial nonlinear function within the domain of definition) and multiply it with the initial function to make it a standardized probability density function. Furthermore, the standardized probability density function of the characteristic rainfall interval-landslide surface density is integrated within the interval to obtain the final probability distribution function of each characteristic rainfall interval-landslide surface density, that is, the cumulative distribution function relationship between the characteristic rainfall parameter rainfall interval (value: 0~8) and the landslide cumulative distribution density (value: 0~1).

[0075] In a specific embodiment, the initial nonlinear functional relationship of the effective rainfall in the first three days and the initial nonlinear functional relationship of the stimulated rainfall in the first five hours are integrated in the domain [0, 8] to obtain the constant k (effective rainfall in the first three days: 0.2043; stimulated rainfall in the first five hours: 0.1972), and multiplied by the initial function respectively to obtain the normalized standard probability density function, that is, the standard probability density function:

[0076] (1) The probability density function of the landslide surface density g(x) and the effective rainfall interval x in the previous three days is “ ”;

[0077] (2) The landslide surface density g(x) and the rate density function of the first 5 hours of the induced rainfall interval x are “ ”.

[0078] Integrate separately to get the corresponding probability distribution function:

[0079] (1) The probability distribution function of the cumulative landslide density G(x) and the effective rainfall interval x in the previous three days is “ ",like Figure 9 Shown is a schematic diagram of the effective rainfall and probability distribution function three days before the landslide;

[0080] (2) The probability distribution function of the cumulative landslide distribution density G(x) and the rainfall interval x in the first five hours is “ ",like Figure 10Shown is a schematic diagram of the rainfall triggered 5 hours before the landslide and the probability distribution function.

[0081] Step S105: using the landslide surface density as an absolute scale and generating an early warning matrix based on the probability distribution function.

[0082] In the embodiment of the present application, the landslide surface density is used as an absolute scale, and the early warning matrix is ​​generated based on the probability distribution function, specifically including:

[0083] Dividing the landslide surface density into intervals according to preset warning levels to obtain multiple levels of landslide surface density warning intervals;

[0084] Determining the previous rainfall and the current day rainfall in each landslide surface density warning interval based on the probability distribution function;

[0085] An early warning matrix is ​​generated based on the previous rainfall and the current day rainfall corresponding to each landslide surface density early warning interval.

[0086] Specifically, based on the above probability distribution function, we substitute G(x) = 0.15, 0.25, 0.4, and 0.6 respectively to obtain the x rainfall interval corresponding to each characteristic rainfall parameter (effective rainfall in the first 3 days, rainfall stimulated in the first 5 hours), and then divide the critical rainfall threshold of the characteristic rainfall parameter according to the actual rainfall corresponding to the characteristic rainfall parameter interval assignment, as shown in Table 2 below:

[0087] Table 2

[0088] ;

[0089] Based on the above classification results of critical rainfall thresholds of characteristic rainfall parameters, the warning threshold intervals of characteristic rainfall parameters such as effective rainfall in the previous three days and stimulated rainfall in the previous five hours are matrix-superimposed to form a comprehensive warning matrix of characteristic rainfall parameters. That is, based on the probability distribution functions of landslide surface density and rainfall intervals of characteristic rainfall parameters such as effective rainfall in the previous period and stimulated rainfall on the same day, respectively, combined with the five meteorological warning levels of the target area (such as no warning [0-0.15), low warning [0.15-0.25), medium warning [0.25-0.4), high warning [0.4-0.6), and very high warning (≥0.6)), or a preset warning level, the landslide cumulative distribution density is divided into 0-1, and then the corresponding characteristic rainfall parameter rainfall interval value is obtained according to the probability distribution function relationship. Then, according to the actual rainfall range corresponding to the rainfall interval value, the critical landslide rainfall threshold for different warning levels of each characteristic rainfall parameter (effective rainfall in the previous period and stimulated rainfall on the same day) is determined.

[0090] Comprehensive rainfall warning matrix: Based on the critical threshold intervals of the previous effective rainfall and the rainfall stimulated on the day (extremely high, high, medium, low, and no warning), the critical threshold intervals of the rainfall characteristic parameters of the previous effective rainfall and the rainfall stimulated on the day are superimposed in a two-dimensional matrix to form a rainfall warning matrix with comprehensive characteristic rainfall parameters in the target area, as shown in Table 3. The comprehensive rainfall warning matrix with characteristic rainfall parameters in the study area is obtained.

[0091] Table 3

[0092] ;

[0093] The above method focuses on extreme rainfall disaster events. Through technical methods such as characteristic rainfall parameter selection based on spatial correlation analysis and probability density and distribution function conversion based on mathematical theory, it significantly reduces the subjective bias and uncertainty of threshold division of traditional methods, providing a highly timely and low-cost scientific decision-making basis for geological disaster early warning under the background of climate change.

[0094] Based on the above-mentioned method for determining a critical rainfall threshold for landslide prediction, one or more embodiments of this specification also provide a platform or terminal for determining a critical rainfall threshold for landslide prediction. The platform or terminal may include devices, software, modules, plug-ins, servers, clients, etc. that use the methods described in the embodiments of this specification and devices combined with necessary hardware for implementation. Based on the same innovative concept, the system in one or more embodiments provided in the embodiments of this specification is as described in the following embodiments. Since the implementation scheme and method for solving the problem of the system are similar, the specific implementation of the system in the embodiments of this specification can refer to the implementation of the aforementioned method, and the repetitions will not be repeated. The terms "unit" or "module" used below can be a combination of software and / or hardware that implements the predetermined function. Although the system described in the following embodiments is preferably implemented in software, hardware or a combination of software and hardware is also possible and conceivable.

[0095] Specifically, Figure 2 This is a schematic diagram of the module structure of an embodiment of a device for determining a critical rainfall threshold for landslide prediction provided in this specification. Figure 2 As shown, the critical rainfall threshold determination device for landslide prediction provided in this specification includes:

[0096] Determination module 201, for determining a landslide kernel density spatial distribution map based on a complete catalog of landslides caused by extreme rainfall clusters;

[0097] An acquisition module 202 is configured to acquire regional rainfall data corresponding to each landslide in a cluster based on a preset time window, wherein the regional rainfall data includes previous rainfall data and current day rainfall data;

[0098] Correlation module 203, for performing correlation processing on the landslide kernel density spatial distribution map and regional rainfall data through Pearson correlation coefficient to determine the optimal regional rainfall data;

[0099] A fitting module 204 is configured to determine an optimal fitting curve based on the optimal area rainfall data and the landslide surface density spatial distribution map, and convert the optimal fitting curve into a probability distribution function based on a rainfall interval, wherein the probability distribution function is specifically a probability distribution function relationship curve between the landslide surface density and the rainfall;

[0100] The generating module 205 is configured to generate an early warning matrix based on the probability distribution function and taking the landslide surface density as an absolute scale.

[0101] It should be noted that the above-mentioned system may also include other implementation methods according to the description of the corresponding method embodiment. The specific implementation methods can refer to the description of the above-mentioned corresponding method embodiment, and will not be described one by one here.

[0102] An embodiment of the present application further provides an electronic device, including:

[0103] processor;

[0104] a memory for storing instructions executable by the processor;

[0105] The processor is configured to execute the method provided in the above embodiment.

[0106] The electronic device provided in the embodiment of the present application stores executable instructions of the processor in a memory. When the processor executes the executable instructions, it can determine a landslide kernel density spatial distribution map based on a complete catalog of landslides in clusters caused by extreme rainfall; obtain regional rainfall data corresponding to each landslide in the cluster based on a preset time window, and the regional rainfall data includes previous rainfall data and current-day rainfall data; perform correlation processing on the landslide kernel density spatial distribution map and the regional rainfall data through the Pearson correlation coefficient to determine the optimal regional rainfall data; determine an optimal fitting curve based on the optimal regional rainfall data and the landslide surface density spatial distribution map, and convert the optimal fitting curve into a probability distribution function based on a rainfall interval range, the probability distribution function being specifically a probability distribution function relationship curve between landslide surface density and rainfall; use the landslide surface density as an absolute scale and generate an early warning matrix based on the probability distribution function, which can improve the accuracy and applicability of the critical rainfall threshold for landslide prediction and alarm.

[0107] The foregoing description of this specification describes specific embodiments. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0108] The methods or devices described in the above embodiments of this specification can implement business logic through computer programs and record them on storage media. The storage media can be read and executed by a computer to achieve the effects of the solutions described in the embodiments of this specification, such as:

[0109] Based on the complete cataloging of landslide clusters caused by extreme rainfall, the spatial distribution map of landslide kernel density was determined;

[0110] Acquiring regional rainfall data corresponding to each landslide in a cluster based on a preset time window, wherein the regional rainfall data includes previous rainfall data and current day rainfall data;

[0111] The optimal regional rainfall data was determined by correlating the landslide kernel density spatial distribution map with regional rainfall data using the Pearson correlation coefficient.

[0112] Determining an optimal fitting curve based on the rainfall data of the optimal area and the spatial distribution map of the landslide surface density, and converting the optimal fitting curve into a probability distribution function based on a rainfall interval range, wherein the probability distribution function is specifically a probability distribution function relationship curve between the landslide surface density and the rainfall;

[0113] Taking the landslide surface density as an absolute scale, an early warning matrix is ​​generated based on the probability distribution function.

[0114] The storage medium may include a physical device for storing information, typically digitizing the information and then storing it in a medium utilizing electrical, magnetic, or optical means. Examples of such storage media include: devices that store information electrically, such as various types of memory devices like RAM and ROM; devices that store information magnetically, such as hard disks, floppy disks, magnetic tapes, magnetic core memories, bubble memories, and USB flash drives; and devices that store information optically, such as CDs and DVDs. Of course, other types of readable storage media exist, such as quantum memories and graphene memories.

[0115] The embodiments of this specification are not limited to those that must comply with industry communication standards, standard computer resource data update and data storage rules, or the situations described in one or more embodiments of this specification. Certain industry standards or slightly modified implementation plans based on the implementation described in the embodiments using custom methods or embodiments can also achieve the same, equivalent, or similar implementation effects as the above embodiments, or the expected implementation effects after deformation. The embodiments obtained by applying these modified or deformed data acquisition, storage, judgment, processing methods, etc. can still fall within the scope of the optional implementation plans of the embodiments of this specification.

[0116] The controller can be implemented in any suitable manner. For example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel ATMEL AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller in pure computer-readable program code, the controller can also be implemented in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, such a controller can be considered a hardware component, and the means for implementing various functions included therein can also be considered as structures within the hardware component. Alternatively, the means for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0117] The device embodiments described above are merely illustrative. For example, the division of units described is merely a logical functional division. Actual implementations may employ alternative divisions, such as combining or integrating multiple units or plug-ins into another system, or omitting or disabling certain features. Furthermore, the coupling or direct coupling or communication connection shown or discussed between devices or units may be through interfaces, or indirect coupling or communication connection between devices or units may be electrical, mechanical, or otherwise.

[0118] These computer program instructions can also be loaded onto a computer or other programmable resource data updating device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable device to implement the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0119] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from the other embodiments. In particular, since the system embodiments are generally similar to the method embodiments, their description is relatively simple, and relevant parts can be referenced to the partial description of the method embodiments. Throughout this specification, reference to the terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of this specification. In this specification, the schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, those skilled in the art may combine and integrate the different embodiments or examples, and features of different embodiments or examples, described in this specification, without conflict.

[0120] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and it should be understood that the scope of protection of the present invention is not limited to such specific descriptions and embodiments. Those skilled in the art can make various other specific variations and combinations based on the technical teachings disclosed in the present invention without departing from the essence of the present invention, and such variations and combinations are still within the scope of protection of the present invention.

Claims

1. A method for determining a critical rainfall threshold for landslide prediction, characterized in that: The method comprises: Based on the complete cataloging of landslide clusters caused by extreme rainfall, the spatial distribution map of landslide kernel density was determined; Acquiring regional rainfall data corresponding to each landslide in a cluster based on a preset time window, wherein the regional rainfall data includes previous rainfall data and current day rainfall data; The optimal regional rainfall data was determined by correlating the landslide kernel density spatial distribution map with regional rainfall data using the Pearson correlation coefficient. Determining an optimal fitting curve based on the rainfall data of the optimal area and the spatial distribution map of the landslide surface density, and converting the optimal fitting curve into a probability distribution function based on a rainfall interval range, wherein the probability distribution function is specifically a probability distribution function relationship curve between the landslide surface density and the rainfall; Taking the landslide surface density as an absolute scale, generating an early warning matrix based on the probability distribution function; Among them, the previous rainfall data It is determined specifically by the following formula: ； Where, The rainfall data for the day. is the effective reduction factor, For the early Daily rainfall.

2. The method for determining a critical rainfall threshold for landslide prediction according to claim 1, wherein: The correlation processing is specifically performed by the following formula: ； Where, is the Pearson correlation coefficient, is the number of days in the previous period, The first factor to be compared, is the second factor to be compared, is the mean value of the first factor, is the mean value of the second factor.

3. The method for determining a critical rainfall threshold for landslide prediction according to claim 1, wherein: Determining the optimal fitting curve based on the optimal regional rainfall data and the landslide surface density spatial distribution map specifically includes: Divide the optimal regional rainfall data into rainfall intervals; The landslide surface density and its corresponding rainfall range are placed in the preset software for comparison and optimization to determine the optimal fitting curve.

4. The method for determining a critical rainfall threshold for landslide prediction according to claim 1, wherein: The converting the optimal fitting curve into a probability distribution function based on the rainfall interval specifically includes: Converting the best-fit curve into a standard probability density function within the rainfall interval, wherein constraints of the probability density function include non-negativity and normalization; The probability density function is converted into a probability distribution function.

5. The method for determining a critical rainfall threshold for landslide prediction according to claim 1, wherein: The method of generating a warning matrix based on the probability distribution function and taking the landslide surface density as an absolute scale specifically includes: Dividing the landslide surface density into intervals according to preset warning levels to obtain multiple levels of landslide surface density warning intervals; Determining the previous rainfall and the current day rainfall in each landslide surface density warning interval based on the probability distribution function; An early warning matrix is ​​generated based on the previous rainfall and the current day rainfall corresponding to each landslide surface density early warning interval.

6. A device for determining a critical rainfall threshold for landslide prediction, characterized in that: The device comprises: A determination module is used to determine the spatial distribution map of landslide kernel density based on a complete catalog of landslide clusters caused by extreme rainfall; An acquisition module is used to acquire regional rainfall data corresponding to each landslide in a group based on a preset time window, wherein the regional rainfall data includes previous rainfall data and current day rainfall data; The correlation module is used to perform correlation processing on the landslide kernel density spatial distribution map and regional rainfall data through the Pearson correlation coefficient to determine the optimal regional rainfall data; A fitting module is used to determine an optimal fitting curve based on the rainfall data of the optimal area and the spatial distribution map of the landslide surface density, and convert the optimal fitting curve into a probability distribution function based on the rainfall interval range, wherein the probability distribution function is specifically a probability distribution function relationship curve of the landslide surface density and rainfall; A generating module, configured to generate an early warning matrix based on the probability distribution function and taking the landslide surface density as an absolute scale; Among them, the previous rainfall data It is determined specifically by the following formula: ； Where, The rainfall data for the day. is the effective reduction factor, For the early Daily rainfall.

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