Disaster early warning method and system considering soil moisture content

Abstract

The invention relates to a disaster early warning method considering soil moisture content. The method comprises the following steps: acquiring meteorological data and geological data of a region and preprocessing the meteorological data and the geological data; constructing a soil moisture content disaster assessment model, and inputting the preprocessed meteorological data and geological data into the soil moisture content disaster assessment model to obtain a disaster score of the area; constructing disaster scoring grades, wherein each disaster scoring grade corresponds to one disaster scoring interval; and judging the disaster grade of the current area according to the interval where the disaster score of the current area is located, and implementing corresponding protection measures according to the disaster grade.

Description

Technical Field

[0001] The invention relates to a disaster early warning method and system taking soil moisture into consideration, belonging to the technical field of disaster early warning. Background Art

[0002] Disaster prediction is a method based on multidimensional data, dynamic models and statistical analysis, which aims to predict the probability of occurrence, spatial and temporal distribution and impact range of natural disasters through real-time monitoring, historical data analysis and physical process simulation. Its core lies in integrating key technologies in the fields of meteorology, hydrology, geology, soil, etc., to provide a scientific basis for disaster prevention and reduction.

[0003] Disaster prediction mainly includes the following core elements:

[0004] Physical drivers: Natural disasters (such as floods, landslides, mudslides, droughts, etc.) are caused by the combined effects of external physical drivers (such as rainfall, earthquakes) and internal environmental vulnerabilities (such as soil saturation and terrain conditions);

[0005] Dynamic change process: The occurrence and development of disasters are usually dynamic, involving nonlinear processes such as rainfall infiltration, surface runoff generation, soil moisture changes and geological body destruction;

[0006] Uncertainty in prediction: The spatial and temporal distribution of disasters is affected by a variety of random factors (such as climate change and geological changes), and errors and uncertainties need to be fully considered in the model;

[0007] Traditional disaster prediction usually focuses on a single or a few driving factors, such as rainfall intensity and terrain characteristics, and lacks in-depth analysis of soil moisture dynamics and its comprehensive impacts, so there is still room for improvement. Summary of the invention

[0008] In order to solve the above problems existing in the prior art, the present invention proposes a disaster early warning method and system taking soil moisture conditions into consideration.

[0009] The technical solution of the present invention is as follows:

[0010] In one aspect, the present invention provides a disaster early warning method taking soil moisture into consideration, comprising the following steps:

[0011] Collect regional meteorological and geological data and pre-process them;

[0012] Construct a soil moisture disaster assessment model, input the pre-processed meteorological data and geological data into the soil moisture disaster assessment model to obtain the disaster score of the area;

[0013] Construct a disaster rating level, each disaster rating level corresponds to a disaster rating interval;

[0014] The disaster level of the current area is determined according to the disaster score range of the current area, and corresponding protective measures are implemented according to the disaster level.

[0015] As a preferred embodiment of the present invention, the specific step of preprocessing the meteorological data and geological data includes interpolating or filtering out missing or abnormal data.

[0016] As a preferred embodiment of the present invention, the soil moisture disaster assessment model is specifically shown in the following formula:

[0017] DWI=α1·P I +α2·M+α3·L c +α4·E+α5·(ΔSM t )

[0018] Where: DWI represents the hazard score; P I represents rainfall influence; M represents soil moisture influence; L c represents the influence of terrain; E represents the influence of evapotranspiration; S represents the influence of soil moisture change; ΔSM t It represents the change of soil moisture in a region within a fixed time period; α1, α2, α3, α4, and α5 represent weight parameters respectively.

[0019] As a preferred embodiment of the present invention, the rainfall impact is specifically shown in the following formula:

[0020]

[0021] Where: P cum Indicates the cumulative rainfall in a region within a fixed period of time; I c represents the soil infiltration capacity of the region; β represents the sensitivity coefficient.

[0022] As a preferred embodiment of the present invention, the soil moisture influence is specifically shown in the following formula:

[0023]

[0024] Among them: SM represents the current average soil moisture in the area; SM min Indicates the lowest soil moisture in the region; SM max It represents the highest historical soil moisture in the region; δ represents the adjustment coefficient.

[0025] As a preferred embodiment of the present invention, the terrain influence is specifically shown in the following formula:

[0026] L c =f(sin(S),ρ soil ,C)

[0027] Where: f represents the terrain and cover influence function; ρ soil represents the regional soil density; C represents the regional land cover type.

[0028] As a preferred embodiment of the present invention, the water evaporation effect is specifically shown in the following formula:

[0029]

[0030] Among them: ET0 represents the baseline value of water evapotranspiration in the region; ET represents the current water evapotranspiration in the region.

[0031] On the other hand, the present invention also provides a disaster early warning system considering soil moisture conditions, including a data acquisition module, a soil moisture disaster assessment module and a disaster rating module;

[0032] The data acquisition module is used to collect regional meteorological data and geological data and perform preprocessing;

[0033] The soil moisture disaster assessment module is used to construct a soil moisture disaster assessment model, and input the pre-processed meteorological data and geological data into the soil moisture disaster assessment model to obtain a disaster score for the area;

[0034] The disaster rating module is used to construct a disaster rating level, each disaster rating level corresponds to a disaster rating interval; the disaster level of the current area is determined according to the interval in which the disaster score of the current area is located, and corresponding protective measures are implemented according to the disaster level.

[0035] On the other hand, the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in any embodiment of the present invention when executing the program.

[0036] In yet another aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in any embodiment of the present invention.

[0037] The present invention has the following beneficial effects:

[0038] 1. The present invention integrates multiple factors such as rainfall, soil, topography, groundwater and evapotranspiration to build a more comprehensive disaster prediction system. The parameters can be adjusted regionally to adapt to different geological and climatic environments. The model has moderate complexity and is easy to integrate into disaster warning and emergency response systems. These improvements provide important guarantees for the scientificity, practicality and efficiency of disaster prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] It should be understood that the step numbers used in this document are only for convenience of description and are not intended to limit the order in which the steps are executed.

[0042] It should be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0043] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0044] The term "and / or" means and includes any and all possible combinations of one or more of the associated listed items.

[0045] Embodiment 1:

[0046] See also Figure 1 , a disaster early warning method considering soil moisture conditions, comprising the following steps:

[0047] Collect regional meteorological and geological data and pre-process them;

[0048] Construct a soil moisture disaster assessment model, input the pre-processed meteorological data and geological data into the soil moisture disaster assessment model to obtain the disaster score of the area;

[0049] Construct a disaster rating level, each disaster rating level corresponds to a disaster rating interval;

[0050] The disaster level of the current area is determined according to the disaster score range of the current area, and corresponding protective measures are implemented according to the disaster level.

[0051] As a preferred implementation mode of this embodiment, the specific steps of preprocessing meteorological data (regional rainfall, evapotranspiration and other data collected in real time or predicted by machine learning models) and geological data (using soil moisture sensors, satellite remote sensing or numerical simulation to obtain soil moisture content in the region, and combining historical records to obtain the upper and lower limits of soil moisture content, etc.) include interpolation or filtering of missing or abnormal data.

[0052] As a preferred implementation of this embodiment, the soil moisture disaster assessment model is specifically shown in the following formula:

[0053] DWI=α1·P I +α2·M+α3·L c +α4·E+α5·(ΔSM t )

[0054] Where: DWI represents the hazard score; P I represents rainfall influence; M represents soil moisture influence; L c represents the influence of terrain; E represents the influence of evapotranspiration; S represents the influence of soil moisture change; ΔSM t It represents the change of soil moisture in a fixed time period (such as 1 hour, 3 hours) in a region (which can reflect the speed of rainstorm infiltration or evaporation consumption); α1, α2, α3, α4, and α5 represent weight parameters respectively.

[0055] ΔSM t =P cum -RD-ET

[0056] Where: R represents surface runoff, that is, the amount of water lost due to rainfall, which is calculated based on the distributed hydrological model. If the soil is highly permeable (such as sandy), R can be ignored; D represents deep seepage, that is, the amount of water that infiltrates into deep soil or groundwater, which is estimated based on the saturated hydraulic conductivity of the soil. If the rainfall is not enough to reach the saturated state, D can be assumed to be 0;

[0057] As a preferred implementation of this embodiment, the rainfall impact is specifically shown in the following formula:

[0058]

[0059] Where: P cum Indicates the cumulative rainfall in a region within a fixed time period (such as the past 24 hours or the past 72 hours); c represents the soil infiltration capacity of the region; β represents the sensitivity coefficient;

[0060] In this embodiment, the soil infiltration capacity calculation formula of the area is:

[0061]

[0062] Where: I c (t) represents the soil infiltration capacity at time t; K represents the soil saturated hydraulic conductivity, which can be obtained through experimental measurement (such as double ring infiltration test, constant head infiltration test) or by consulting the soil database. Typical values ​​are: sandy soil: 30-300 mm / hr; loam: 10-30 mm / hr; clay: <10 mm / hr; Indicates the wet front suction head, the typical range is 100 ~ 400mm (larger for clay and smaller for sand); θ s represents the initial soil moisture content; θ i Soil saturation moisture content; θ i Usually lower than θ s , the greater the difference between the two, the higher the initial infiltration rate; F(t) represents the cumulative infiltration, that is, the cumulative infiltration from the beginning of rainfall to time t;

[0063] As a preferred implementation of this embodiment, the soil moisture influence is specifically shown in the following formula:

[0064]

[0065] Among them: SM represents the current average soil moisture in the area; SM min Indicates the lowest soil moisture in the region; SM max It represents the highest historical soil moisture in the region; δ represents the adjustment coefficient.

[0066] As a preferred implementation of this embodiment, the terrain influence is specifically shown in the following formula:

[0067] L c =f(sin(S),ρ soil ,C)

[0068] Where: f represents the terrain and cover influence function; ρ soil represents the regional soil density; C represents the regional land cover type; S represents the average slope of the region.

[0069] In this embodiment, the terrain and cover influence function is specifically shown in the following formula:

[0070]

[0071] Wherein: w represents the weight parameter; LCF represents the land cover factor, and different values ​​are given for different land cover types. In this embodiment, forest: 0.2 (relatively stable), grassland: 0.4, cultivated land: 0.5, bare land: 0.8 (most unstable);

[0072] The average slope of the area is calculated based on the digital elevation model, and the specific steps are as follows:

[0073] Get the two-dimensional discrete grid data of the terrain in the area. Each grid includes an elevation value. Calculate the slope of each grid, which can be understood as the synthesis of the elevation change rate in the east-west and north-south directions:

[0074]

[0075] in: represents the terrain slope in the east-west (x) direction; represents the terrain slope in the north-south (y) direction;

[0076] The average of all grid slopes is taken as the average slope of the area;

[0077] As a preferred implementation of this embodiment, the water evaporation effect is specifically shown in the following formula:

[0078]

[0079] Among them: ET0 represents the reference value of water evapotranspiration in the region; ET represents the current water evapotranspiration in the region;

[0080] The calculation formula for the reference value of water evapotranspiration in the area is:

[0081]

[0082] Where: Δ represents the slope of saturated water vapor pressure versus temperature, which represents the rate of change of water vapor saturation state; R n represents net radiation, including shortwave radiation and longwave radiation; G represents soil heat flux; γ represents the moisture constant, which is related to atmospheric pressure; u represents wind speed; e represents wind speed. s Indicates the saturated water vapor pressure, calculated based on temperature; e a Indicates the actual water vapor pressure, calculated based on relative humidity.

[0083]

[0084] Where: T represents temperature;

[0085]

[0086] Where: RH stands for relative humidity;

[0087] R n =R s (1-α)-R l

[0088] Where: R s represents solar radiation; α represents the surface reflectivity (such as the reflectivity of grass is 0.23); R l Represents the longwave radiation from the surface.

[0089] In this embodiment, the disaster assessment levels are divided into:

[0090] Level I (low risk): The current soil moisture content is low or within a safe range, and the possibility of causing serious geological disasters or waterlogging in the short term is low.

[0091] Level II (medium risk): There will be significant rainfall and rising soil moisture, and it is necessary to enter the early warning monitoring stage and strengthen risk inspections and data collection.

[0092] Level III (high risk): The soil moisture content is high. If there is heavy or continuous rainfall, the possibility of disasters (landslides, collapses, mudslides, waterlogging, etc.) will increase significantly. A high-risk warning needs to be issued, and emergency preparations for personnel and facilities need to be made.

[0093] Level IV (serious risk): The soil is extremely saturated or the groundwater level is high, and the rainfall situation is severe. The risk of disaster outbreak is very high. It is necessary to immediately enter the emergency response state and initiate personnel evacuation or key protection measures.

[0094] Embodiment 2:

[0095] A disaster early warning system considering soil moisture conditions, comprising a data collection module, a soil moisture condition disaster assessment module and a disaster rating module;

[0096] The data acquisition module is used to collect regional meteorological data and geological data and perform preprocessing;

[0097] The soil moisture disaster assessment module is used to construct a soil moisture disaster assessment model, and input the pre-processed meteorological data and geological data into the soil moisture disaster assessment model to obtain a disaster score for the area;

[0098] The disaster rating module is used to construct a disaster rating level, each disaster rating level corresponds to a disaster rating interval; the disaster level of the current area is determined according to the interval in which the disaster score of the current area is located, and corresponding protective measures are implemented according to the disaster level.

[0099] The system is used to implement the method in Example 1, which will not be described in detail here.

[0100] Embodiment three:

[0101] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in any embodiment of the present invention when executing the program.

[0102] Embodiment 4:

[0103] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method described in any embodiment of the present invention is implemented.

[0104] In the embodiments of the present application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, c can be single or multiple.

[0105] Those of ordinary skill in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented in a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0106] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0107] In several embodiments provided in the present application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory; hereinafter referred to as: ROM), random access memory (Random Access Memory; hereinafter referred to as: RAM), disk or optical disk, and other media that can store program codes.

[0108] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A disaster early warning method considering soil moisture conditions, characterized in that: The following steps are involved: Collect regional meteorological and geological data and pre-process them; Construct a soil moisture disaster assessment model, input the pre-processed meteorological data and geological data into the soil moisture disaster assessment model to obtain the disaster score of the area; Construct a disaster rating level, each disaster rating level corresponds to a disaster rating interval; The disaster level of the current area is determined according to the disaster score range of the current area, and corresponding protective measures are implemented according to the disaster level.

2. A disaster early warning method considering soil moisture conditions according to claim 1, characterized in that: The specific steps of preprocessing meteorological data and geological data include interpolation or filtering of missing or abnormal data.

3. The disaster early warning method considering soil moisture conditions according to claim 1, characterized in that: The soil moisture disaster assessment model is specifically shown in the following formula: DWI=α1·P I +α2·M+α3·L c +α4·E+α5·(ΔSM t ) Where: DWI represents the hazard score; P I represents rainfall influence; M represents soil moisture influence; L c represents the influence of terrain; E represents the influence of evapotranspiration; S represents the influence of soil moisture change; ΔSM t It represents the change of soil moisture in a region within a fixed time period; α1, α2, α3, α4, and α5 represent weight parameters respectively.

4. A disaster early warning method considering soil moisture conditions according to claim 3, characterized in that: The rainfall impact is specifically shown in the following formula: Where: P cum Indicates the cumulative rainfall in a region within a fixed period of time; I c represents the soil infiltration capacity of the region; β represents the sensitivity coefficient.

5. The disaster early warning method considering soil moisture conditions according to claim 3, characterized in that: The soil moisture influence is specifically shown in the following formula: Among them: SM represents the current average soil moisture in the area; SM min Indicates the lowest historical soil moisture in the region; SM max It represents the highest historical soil moisture in the region; δ represents the adjustment coefficient.

6. A disaster early warning method considering soil moisture conditions according to claim 3, characterized in that: The terrain influence is specifically shown in the following formula: L c =f(sin(S),ρ soil ,C) Where: f represents the terrain and cover influence function; ρ soil represents the regional soil density; C represents the regional land cover type; S represents the average slope of the region.

7. The disaster early warning method considering soil moisture conditions according to claim 3, characterized in that: The influence of water evaporation is specifically shown in the following formula: Among them: ET0 represents the reference value of water evapotranspiration of the region; ET represents the current water evapotranspiration of the region.

8. A disaster early warning system taking soil moisture into consideration, characterized in that: It includes data collection module, soil moisture disaster assessment module and disaster rating module; The data acquisition module is used to collect regional meteorological data and geological data and perform preprocessing; The soil moisture disaster assessment module is used to construct a soil moisture disaster assessment model, and input the pre-processed meteorological data and geological data into the soil moisture disaster assessment model to obtain a disaster score for the area; The disaster rating module is used to construct a disaster rating level, each disaster rating level corresponds to a disaster rating interval; the disaster level of the current area is determined according to the interval in which the disaster score of the current area is located, and corresponding protective measures are implemented according to the disaster level.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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