Method and device for forecasting storm surge beach overtopping in forecasting area

Abstract

The embodiment of the invention provides a storm surge overtopping forecasting method and device for a forecasting area. The forecasting method comprises the steps that typhoon forecasting data and storm surge forecasting data of the forecasting area are acquired; preprocessing the typhoon forecast data and the storm surge forecast data to obtain standard data; inputting the standard data into a preset platform, and retrieving in a preset database in the preset platform to obtain a storm surge beach overtopping scene most similar to the standard data; and determining the storm surge beach overtopping influence data corresponding to the most similar storm surge beach overtopping scene as forecast data. According to the embodiment of the invention, large-scale numerical simulation or complex statistical calculation does not need to be repeatedly carried out, and dependence on computing resources is remarkably reduced.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of storm surge technology, and in particular to a method and device for forecasting storm surge floodplains in a forecast area. Background Art

[0002] Current storm surge floodplain forecasting methods typically rely on numerical simulations, which are computationally intensive and require significant computing resources and are expensive to use and maintain. This makes it difficult to make accurate storm surge emergency decisions quickly. Statistical methods based on historical experience also exist, but they still require the comparison of numerous real-time observations and geographic information data, requiring significant computing resources and storage space. These methods also yield low forecast accuracy and are also not conducive to making accurate storm surge emergency decisions quickly. Summary of the Invention

[0003] The technical problem to be solved by the embodiments of the present invention is to provide a method and device for forecasting storm surge floodplains in a forecast area, which does not require repeated large-scale numerical simulations or complex statistical calculations, and significantly reduces dependence on computing resources.

[0004] To solve the above technical problems, the technical solutions of the embodiments of the present invention are as follows:

[0005] A method for forecasting storm surge floodplains in a forecast area, comprising:

[0006] Obtain typhoon forecast data and storm surge forecast data for the forecast area;

[0007] Preprocessing the typhoon forecast data and storm surge forecast data to obtain standard data;

[0008] Inputting the standard data into a preset platform, and retrieving a storm surge floodplain scenario that is most similar to the standard data from a preset database in the preset platform;

[0009] Determining the storm surge floodplain impact data corresponding to the most similar storm surge floodplain scenario as forecast data;

[0010] The construction process of the preset platform includes:

[0011] Obtain historical typhoon data, historical storm surge data and basic information data for the forecast area;

[0012] Constructing a storm surge floodplain numerical model based on the basic information data;

[0013] Setting a combination of influencing factors based on the historical typhoon data and historical storm surge data;

[0014] Combining the influencing factors and simulating them in the storm surge floodplain numerical model;

[0015] Determine the factors affecting storm surge inundation based on the simulated storm surge floodplain impact data;

[0016] Based on the factor data, the corresponding storm surge floodplain scenario is constructed;

[0017] Constructing a preset database based on the storm surge floodplain scenario and the corresponding storm surge floodplain impact data;

[0018] The preset database is integrated into the preset platform.

[0019] Optionally, the historical typhoon data includes: typhoon location, typhoon time, typhoon intensity, typhoon moving direction and typhoon moving speed;

[0020] The historical storm surge data include: hourly storm surge data, astronomical tide data and total tide level data;

[0021] The basic information data includes: basic geographical data and tide and wave data.

[0022] Optionally, constructing a storm surge floodplain numerical model based on the basic information data includes:

[0023] Based on the basic geographic data, a discrete grid of the forecast area is constructed using an unstructured triangular grid;

[0024] According to the tide and wave data, a coupled model of a hydrodynamic model and a wave model is used to construct a storm surge floodplain numerical model.

[0025] Optionally, according to the historical typhoon data and historical storm surge data, a combination of influencing factors is set, including:

[0026] By controlling variables, the various influencing factors of historical typhoon data and historical storm surge data are grouped and combined to obtain an influencing factor combination.

[0027] Optionally, combining the influencing factors and simulating them in the storm surge floodplain numerical model includes:

[0028] Combining the influencing factors, simulating in the storm surge floodplain numerical model, and obtaining simulated storm surge floodplain impact data;

[0029] Among them, the storm surge floodplain impact data include the inundation range, inundation depth and classification, average inundation depth, affected population, estimated economic losses, impact on disaster-bearing bodies, high tide duration, storm surge process curve, key inundation areas, emergency evacuation routes and / or risk zoning maps.

[0030] Optionally, based on the simulated storm surge floodplain impact data, determine the factors affecting storm surge inundation, including:

[0031] Determine the factors affecting storm surge inundation based on the simulated inundation range, inundation depth, classification and average inundation depth;

[0032] The factor data are influencing factors of historical typhoon data or influencing factors of historical storm surge data.

[0033] Optionally, constructing a corresponding storm surge floodplain scenario based on the factor data includes:

[0034] According to Z i =[X 1i , X 2i ,...,X mi ], constructing storm surge floodplain scenarios;

[0035] Among them, Z i For the storm surge floodplain scenario, X ji is the data of each factor, X ji ∈[x 1i , x 2i ,...,x mi ], x ji is the factor instance of each factor data, i=1, 2, ..., n, n is the total number of factor data, j=1, 2, ..., m, m is the total number of factor instances.

[0036] Optionally, the typhoon forecast data includes: real-time typhoon position, real-time typhoon time, real-time typhoon intensity, real-time typhoon moving direction and real-time typhoon moving speed;

[0037] The storm surge forecast data includes: real-time hourly storm surge data, real-time astronomical tide data and real-time total tide level data.

[0038] Optionally, preprocessing the typhoon forecast data and storm surge forecast data to obtain standard data includes:

[0039] extracting real-time factor instances corresponding to the factor data from the typhoon forecast data and storm surge forecast data;

[0040] The real-time factor instance is preprocessed to obtain standard data.

[0041] An embodiment of the present invention further provides a storm surge floodplain forecasting device for a forecast area, comprising:

[0042] An acquisition module is used to obtain typhoon forecast data and storm surge forecast data for the forecast area; and to obtain historical typhoon data, historical storm surge data and basic information data for the forecast area;

[0043] A processing module is used to preprocess the typhoon forecast data and storm surge forecast data to obtain standard data; input the standard data into a preset platform, and retrieve the storm surge floodplain scenario most similar to the standard data from a preset database in the preset platform; determine the storm surge floodplain impact data corresponding to the most similar storm surge floodplain scenario as forecast data; construct a storm surge floodplain numerical model based on the basic information data; set an influencing factor combination based on the historical typhoon data and historical storm surge data; simulate the influencing factor combination in the storm surge floodplain numerical model; determine the factor data affecting storm surge inundation based on the simulated storm surge floodplain impact data; construct a corresponding storm surge floodplain scenario based on the factor data; construct a preset database based on the storm surge floodplain scenario and the corresponding storm surge floodplain impact data; and integrate the preset database into the preset platform.

[0044] The above solution of the embodiment of the present invention has at least the following beneficial effects:

[0045] The above-mentioned solution of the present invention utilizes a pre-defined platform and database to pre-store storm surge floodplain scenarios and impact data derived from historical data simulations. Actual forecasting requires only pre-processing the real-time data and performing similarity searches, eliminating the need for repeated large-scale numerical simulations or complex statistical calculations. This significantly reduces reliance on computing resources, hardware investment, and maintenance costs.

[0046] A large number of pre-simulated scenarios have been stored in the preset database. During real-time forecasting, the most similar scenarios can be quickly located and the results can be output through retrieval, which greatly shortens the forecasting time and can provide accurate floodplain impact data in a short time, providing timely support for emergency command, personnel transfer, resource allocation and other decisions.

[0047] A systematic simulation of historical data using a numerical storm surge floodplain model identifies key factors influencing inundation, ensuring the scientific and comprehensive nature of the scenarios in the database. A forecasting logic based on the most similar scenario maintains the accuracy of numerical simulation while avoiding the latency of real-time calculations through pre-storage, achieving a balance between technical rigor and practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a flow chart of a storm surge floodplain forecasting method for a forecast area provided by an embodiment of the present invention.

[0049] Figure 2 It is a module schematic diagram of a storm surge floodplain forecasting device for a forecast area provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0050] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0051] like Figure 1 As shown, an embodiment of the present invention provides a method for forecasting storm surge floodplains in a forecast area, comprising:

[0052] Step 11, obtaining typhoon forecast data and storm surge forecast data for the forecast area;

[0053] Step 12, preprocessing the typhoon forecast data and storm surge forecast data to obtain standard data;

[0054] Step 13: inputting the standard data into a preset platform, and searching a preset database in the preset platform to obtain a storm surge floodplain scenario that is most similar to the standard data;

[0055] Step 14, determining the storm surge floodplain impact data corresponding to the most similar storm surge floodplain scenario as forecast data;

[0056] The construction process of the preset platform includes:

[0057] Step 101, obtaining historical typhoon data, historical storm surge data and basic information data of the forecast area;

[0058] Step 102: constructing a storm surge floodplain numerical model based on the basic information data;

[0059] Step 103: setting a combination of influencing factors based on the historical typhoon data and historical storm surge data;

[0060] Step 104, combining the influencing factors and performing simulation in the storm surge floodplain numerical model;

[0061] Step 105, determining data on factors affecting storm surge flooding based on the simulated storm surge floodplain impact data;

[0062] Step 106, constructing a corresponding storm surge floodplain scenario based on the factor data;

[0063] Step 107: constructing a preset database based on the storm surge floodplain scenario and the corresponding storm surge floodplain impact data;

[0064] Step 108: Integrate the preset database into the preset platform.

[0065] In this example, a pre-configured platform and database were constructed to pre-store storm surge floodplain scenarios and impact data derived from historical data simulations. Actual forecasting only requires pre-processing real-time data and performing similarity searches, eliminating the need for repeated large-scale numerical simulations or complex statistical calculations. This significantly reduces reliance on computing resources, hardware investment, and maintenance costs.

[0066] A large number of pre-simulated scenarios have been stored in the preset database. During real-time forecasting, the most similar scenarios can be quickly located and the results can be output through retrieval, which greatly shortens the forecasting time and can provide accurate floodplain impact data in a short time, providing timely support for emergency command, personnel transfer, resource allocation and other decisions.

[0067] A systematic simulation of historical data using a numerical storm surge floodplain model identifies key factors influencing inundation, ensuring the scientific and comprehensive nature of the scenarios in the database. A forecasting logic based on the most similar scenario maintains the accuracy of numerical simulation while avoiding the latency of real-time calculations through pre-storage, achieving a balance between technical rigor and practical application value.

[0068] In an optional embodiment of the present invention, in step 101, the historical typhoon data includes: typhoon position, typhoon time, typhoon intensity, typhoon moving direction and typhoon moving speed;

[0069] The historical storm surge data include: hourly storm surge data, astronomical tide data and total tide level data;

[0070] The basic information data includes: basic geographical data and tide and wave data.

[0071] Specifically, the basic geographic data include: digital elevation model, seawall elevation, embankment boundary, disaster shelter point and preset area boundary of estuary area; the tide and wave data include: tide level, wave height, period and direction.

[0072] In this example, step 101 defines the content of historical typhoon, storm surge, and basic information data. Historical typhoon data, including location and intensity, provides the core driving conditions for storm surge simulation. The hourly, astronomical, and total tide levels in historical storm surge data are key to calibrating the model's accuracy. Basic geographic data, such as digital elevation models and seawall elevations, determine the accuracy of floodplain calculations, while information such as disaster shelters enhances the practicality of forecasts. Tidal and wave data reflect the impact of wave surges and enhance the simulation of the nearshore dynamic environment.

[0073] In an optional embodiment of the present invention, in step 102, constructing a storm surge floodplain numerical model based on the basic information data includes:

[0074] Step 1021: Based on the basic geographic data, a high-resolution unstructured triangular mesh is used to construct a discrete grid for the forecast area. The high-resolution unstructured triangular mesh can flexibly fit irregular boundaries (such as the direction of seawalls and the curvature of river channels), avoiding the "stair-step" approximation error of traditional rectangular meshes at curved boundaries.

[0075] Step 1022: Based on the tide and wave data, a coupled model of a hydrodynamic model and a wave model is used to construct a storm surge floodplain numerical model; the storm surge floodplain numerical model is used to simulate water level, flow velocity and wave parameters under different typhoon scenarios.

[0076] In this example, step 102 uses high-resolution unstructured triangular meshes and coupled models to construct a numerical model of the storm surge floodplain, which has significant advantages. High-resolution unstructured triangular meshes can flexibly fit irregular boundaries such as seawalls and river channels, avoid the approximate errors of rectangular meshes, and enable the model to more accurately depict complex coastline morphology and improve the simulation accuracy of nearshore areas. The coupling of hydrodynamic and wave models can comprehensively consider the interaction between tides, waves and storm surges, truly reflect the movement characteristics of nearshore water bodies, accurately simulate water level changes, flow velocity distribution and wave parameters under different typhoon scenarios, and provide reliable support for the prediction of floodplain range and inundation depth. This method enhances the adaptability of the model to complex dynamic environments, improves the reliability and practicality of forecast results, and provides more effective data support for storm surge disaster prevention and mitigation decisions.

[0077] In an optional embodiment of the present invention, in step 103, setting a combination of influencing factors based on the historical typhoon data and historical storm surge data includes:

[0078] Step 1031 : Grouping and combining various influencing factors of historical typhoon data and various influencing factors of historical storm surge data by controlling variables to obtain an influencing factor combination.

[0079] Specifically, for example, the typhoon position is A, the typhoon time is B, the typhoon intensity is C, ... the hourly storm surge data is F, the astronomical tide data is G, and the total tide level data is H;

[0080] When typhoon position A is set as a variable, there are four typhoon landing locations: A1, A2, A3, and A4, typhoon time is B, typhoon intensity is C,... hourly storm surge data is F, astronomical tide data is G, and total tide data H is quantitative, four groups of influencing factor combinations are obtained;

[0081] When the total tide data H is set as a variable, the total tide data includes six total tide levels: H1, H2, H3, H4, H5, and H6, the typhoon position is A, the typhoon time is B, the typhoon intensity is C, ... the hourly storm surge data is F, and the astronomical tide data is G. For quantitative analysis, six groups of influencing factor combinations are obtained;

[0082] Each influencing factor is taken as a variable in turn, and other influencing factors are taken as quantitative factors to obtain several combinations of influencing factors.

[0083] In this example, step 103 uses the control variable method to set a combination of influencing factors, which can systematically analyze the impact of each factor on the storm surge floodplain. By grouping and combining the various elements of historical typhoon and storm surge data, it is possible to independently explore the impact of changes in each variable (such as typhoon landfall location and total tide level) on storm surge, remove interference from other factors, and clarify the independent contribution and sensitive range of each factor. This method of constructing multiple groups of influencing factor combinations can cover complex scenarios, help discover the interactions between different factors, and improve understanding of storm surge formation mechanisms. It can also provide diverse testing scenarios for numerical models, optimize model parameters, and significantly improve the model's simulation and forecasting capabilities for different storm surge scenarios, providing accurate data support for disaster prevention and mitigation decision-making.

[0084] In an optional embodiment of the present invention, in step 104, combining the influencing factors and simulating them in the storm surge floodplain numerical model includes:

[0085] Step 1041 , combining the influencing factors and simulating them in the storm surge floodplain numerical model to obtain simulated storm surge floodplain impact data;

[0086] Among them, the storm surge floodplain impact data include the inundation range, inundation depth and classification, average inundation depth, affected population, estimated economic losses, impact on disaster-bearing bodies, high tide duration, storm surge process curve, key inundation areas, emergency evacuation routes and / or risk zoning maps.

[0087] Specifically, the above-mentioned several influencing factors are combined and simulated in the storm surge floodplain numerical model to obtain simulated storm surge floodplain impact data.

[0088] In this example, the simulation results include data such as the scope, depth and classification of flooding, economic losses, and affected population. These data not only meet basic hydrological analysis (such as high tide duration and process curves), but also support risk quantification (such as the impact on disaster-prone objects and risk zoning maps). They also directly serve emergency decision-making (such as key flooding areas and evacuation routes), achieving a full-chain assessment from physical processes to social impacts.

[0089] By combining multiple groups of influencing factors obtained by the control variable method and comparing the simulation results under different combinations (such as the differences in inundation range corresponding to different typhoon locations), we can accurately identify the impact weight of each factor on the floodplain and deepen our understanding of the storm surge disaster mechanism.

[0090] Diversified data outputs can be directly converted into visual results, helping decision makers quickly grasp key information and providing specific and operational basis for the formulation of disaster prevention and mitigation measures.

[0091] In an optional embodiment of the present invention, in step 105, determining the factor data affecting storm surge inundation based on the simulated storm surge floodplain impact data includes:

[0092] Step 1051, based on the simulated flooding range, flooding depth, classification and average flooding depth, determine the factor data affecting the storm surge flooding; specifically,

[0093] according to ,

[0094] Determine data on factors that affect storm surge inundation,

[0095] Among them, P is the impact of the change of influencing factors on the storm surge floodplain, x 1max is the maximum value of the flooding range corresponding to the same variable influencing factor, x 1min is the minimum value of the flooding range corresponding to the same variable influencing factor, P1 is the first reference data, x 2max is the maximum flooding depth corresponding to the same variable influencing factor, x 2min is the minimum flooding depth corresponding to the same variable influencing factor, P2 is the second reference data, x 3max is the maximum average flooding depth corresponding to the same variable influencing factor, x 3min is the minimum average flooding depth corresponding to the same variable influencing factor, and P3 is the third reference data;

[0096] For example, when typhoon position A is set as a variable, there are four typhoon landing locations: A1, A2, A3, and A4, typhoon time is B, typhoon intensity is C, hourly storm surge data is F, astronomical tide data is G, and total tide data H is quantitative, four groups of influencing factor combinations are obtained; x 1max is the maximum value of the flooding range corresponding to the four groups of influencing factors, x 1min is the minimum value of the flooding range corresponding to the four groups of influencing factors;

[0097] According to P>P4, determine the factors affecting storm surge inundation;

[0098] Wherein, P4 is the fourth reference data;

[0099] When the degree of influence P of the change in influencing factors on the storm surge floodplain is greater than the fourth reference data, the corresponding influencing factor as a variable is the factor data affecting storm surge inundation (key factor); other influencing factors (non-key factors) are discarded.

[0100] In this example, a formula based on the extreme differences in inundation range, depth, and average depth, combined with reference data P1-P4, was constructed to transform the abstract impact of these factors into a specific numerical value, P, thus avoiding subjective judgment bias. For example, by calculating the maximum difference in inundation range corresponding to different typhoon landfall locations, the actual impact on the floodplain can be accurately quantified.

[0101] Using a threshold of P > P4, non-critical factors with minimal impact on floodplains (such as minor meteorological parameters) are eliminated, reducing the amount of redundant data required for subsequent scenario construction, database storage, and real-time retrieval, thereby lowering system complexity. Simultaneously, models and forecasts are focused on core factors such as typhoon intensity and total tide level, enhancing the relevance of simulations and forecasts.

[0102] After identifying the key factors, we can optimize defense measures in a targeted manner (such as focusing on strengthening seawalls in areas affected by strong typhoons) and deepen our understanding of disaster-causing laws (such as the correlation mechanism between total tide level and inundation depth).

[0103] The scenario library built based on key factors is more streamlined. When making real-time forecasts, only core factors (such as typhoon movement direction and total tide level) need to be matched, which shortens the retrieval time, improves the accuracy of matching similar scenarios, and ensures the reliability of real-time output results.

[0104] In an optional embodiment of the present invention, in step 106, constructing a corresponding storm surge floodplain scenario based on the factor data includes:

[0105] Step 1061, according to Z i =[X 1i , X 2i ,...,X mi ], constructing storm surge floodplain scenarios;

[0106] Among them, Z i For the storm surge floodplain scenario, X ji is the data of each factor, X ji ∈[x 1i , x 2i ,...,x mi ], x ji is the factor instance of each factor data, i=1, 2, ..., n, n is the total number of factor data, j=1, 2, ..., m, m is the total number of factor instances.

[0107] For example, after determining that the typhoon moving direction D and the total tide level data H are factor data (key factors affecting storm surge inundation), the typhoon moving directions include eight moving directions: D1, D2, D3, D4, D5, D6, D7, and D8, and the total tide level data include ten total tide levels: H1, H2, H3, H4, H5, H6, H7, H8, H9, and H10;

[0108] Construct storm surge floodplain scenarios, including [D1, H1], [D1, H2], [D1, H3], [D1, H4], [D1, H5], [D1, H6], [D1, H7], [D1, H8], [D1, H9], [D1, H10], ..., [D8, H1], [D8, H2], [D8, H3], [D8, H4], [D8, H5], [D8, H6], [D8, H7], [D8, H8], [D8, H9], [D8, H10].

[0109] In this example, scenarios are constructed only around identified key factors (such as typhoon movement direction and total tide level). By combining all instances of each factor (such as 8 typhoon directions × 10 total tide levels to generate 80 scenarios), we ensure that all possible high-impact combinations are covered while avoiding scenario redundancy caused by the inclusion of non-key factors, so that the database focuses on the core scenarios that play a decisive role in floodplain outcomes.

[0110] The standardized scenario structure makes similarity retrieval more efficient during real-time forecasting. By simply comparing the real-time instances of key factors (such as the current typhoon direction and total tide level) with the corresponding parameters of the scenarios in the database, the most matching historical cases can be quickly located, reducing interference from irrelevant data and improving matching accuracy.

[0111] The structured scenario design provides a clear storage logic for the preset database. Each scenario corresponds to a unique combination of key factors and simulation results (such as flooding range and economic losses), which facilitates data management and retrieval.

[0112] Since the scenarios are constructed based on key factors, the corresponding floodplain impact data can more accurately reflect the actual disaster-causing laws, making the real-time retrieval output forecast results (such as key flooded areas and evacuation routes) more in line with the actual impact of current typhoons and storm surges, providing a reliable reference for decision-making.

[0113] An optional embodiment of the present invention includes:

[0114] Step 107: constructing a preset database based on the storm surge floodplain scenario and the corresponding storm surge floodplain impact data;

[0115] Step 108: Integrate the preset database into the preset platform.

[0116] For example, the storm surge floodplain scenarios of [D1, H1]...[D8, H10] are simulated in a storm surge floodplain numerical model to obtain corresponding storm surge floodplain impact data; the storm surge floodplain scenarios and the corresponding storm surge floodplain impact data are stored in a preset database; and the preset database is integrated into a preset platform (visualization platform).

[0117] In this example, the preset database associates and stores storm surge floodplain scenarios (such as key factor combinations such as [D1, H1]) with their corresponding multi-dimensional impact data (flooding range, economic losses, evacuation routes, etc.) to form a structured data system.

[0118] This one-to-one scenario-result storage mode enables similarity retrieval during real-time forecasting to directly locate the complete impact data of the matching scenario, avoiding repeated calculations, greatly improving data retrieval efficiency, and providing support for the rapid output of forecast results.

[0119] After integrating the database into a preset platform (such as a visualization platform), abstract numerical data can be converted into intuitive charts, maps, etc. (such as risk zoning maps and inundation depth classification visualizations), allowing decision makers to quickly understand the spatial distribution and severity of storm surge impacts.

[0120] In an optional embodiment of the present invention, in step 11, the typhoon forecast data includes: real-time typhoon position, real-time typhoon time, real-time typhoon intensity, real-time typhoon moving direction and real-time typhoon moving speed;

[0121] The storm surge forecast data includes: real-time hourly storm surge data, real-time astronomical tide data and real-time total tide level data.

[0122] In this example, real-time typhoon data (location, time, intensity, etc.) is the immediate driving force behind the generation of storm surges. It can accurately capture the dynamic characteristics of the current typhoon and provide the core input for simulating its real-time pushing effect on seawater. Real-time storm surge data (hourly water increase, astronomical tide, total tide level) reflects the current tide level superposition state and is the key basis for judging the real-time progress of the storm surge floodplain.

[0123] In an optional embodiment of the present invention, in step 12, the typhoon forecast data and storm surge forecast data are preprocessed to obtain standard data, including:

[0124] Step 121: extracting real-time factor instances corresponding to the factor data from the typhoon forecast data and storm surge forecast data; for example, extracting typhoon movement direction instances and total tide level data instances;

[0125] Step 122: pre-process the real-time factor instance to obtain standard data. Specifically, the pre-processing may include:

[0126] Standardized coding, interpolation supplementation and outlier removal are performed on real-time factor instances.

[0127] In this example, by extracting real-time instances corresponding to key factor data (such as typhoon movement direction and total tide level), the input data is accurately matched with the identified key disaster-causing factors, avoiding interference from irrelevant information and strengthening the correlation between data and models.

[0128] Standardized coding unifies data formats to ensure that data from different sources can be directly used for calculations; interpolation supplements data to solve missing data problems and ensure the continuity of time / space series; removing outliers (such as monitoring errors) reduces noise interference, provides reliable standard data for subsequent simulations or predictions, and reduces model output deviations.

[0129] The preprocessed standard data can be directly connected to subsequent models (such as storm surge floodplain numerical models), reducing the data adaptation cost in real-time calculations, improving the response speed of the forecasting system, and better adapting to the timeliness requirements of short-term emergency warnings.

[0130] An optional embodiment of the present invention includes:

[0131] Step 13: Input the standard data into a preset platform, and retrieve the storm surge floodplain scenario that is most similar to the standard data from a preset database in the preset platform; for example, compare the typhoon movement direction instance and the total tide level data instance with the storm surge floodplain scenarios [D1, H1] ... [D8, H10] one by one to obtain the most similar storm surge floodplain scenario;

[0132] Step 14: Determine the storm surge floodplain impact data corresponding to the most similar storm surge floodplain scenario as forecast data; the storm surge floodplain impact data includes inundation range, inundation depth and classification, average inundation depth, affected population, estimated economic loss, impact on hazard-bearing objects, high tide duration, storm surge process curve, key inundation areas, emergency evacuation routes and / or risk zoning maps.

[0133] In this example, historical simulation results of similar scenarios in the preset database (such as flooding range and economic losses) are directly called, eliminating the time required to repeatedly run complex numerical models, quickly outputting forecast data, and meeting the timeliness requirements of storm surge emergency warnings (especially suitable for short-term responses during fast-moving typhoons).

[0134] Based on historical cases that are highly matched with key factors (such as typhoon movement direction and total tide level), the corresponding floodplain impact data (such as key inundation areas and evacuation routes) have been verified through preliminary simulations, which can provide a realistic reference for the current scenario and reduce the uncertainty of real-time calculations.

[0135] The output multi-dimensional data (such as risk zoning maps and estimated losses) can be directly used to formulate emergency plans, helping decision makers quickly determine the scope and severity of disaster impacts and optimize disaster prevention measures such as evacuation scheduling and resource allocation.

[0136] There is no need to run high-computation numerical models in real time; forecasts can be generated through scenario matching, which simplifies the architecture of the real-time forecasting system.

[0137] The present invention uses historical data to pre-build a numerical model to simulate the floodplain scenarios and results corresponding to a large number of key factor combinations (such as typhoon movement direction and total tide level), and stores them in a database.

[0138] When making real-time forecasts, it is only necessary to pre-process the real-time typhoon and storm surge data, search for matching scenarios through the platform, and directly call the ready-made results, eliminating the time spent running high-computing models in real time. It can quickly output key information such as the flood range and evacuation routes, perfectly adapting to the short-term requirements of typhoon emergency response.

[0139] Historical typhoon and storm surge data and high-precision geographic information (DEM, seawall elevation, etc.) provide a solid foundation for the model; high-resolution unstructured grids and hydrodynamic-wave coupling models accurately depict complex coastlines and dynamic processes, reducing simulation errors.

[0140] Through the control variable method and quantitative formula, the core factors that have a significant impact on the floodplain (such as typhoon intensity and total tide level) are screened out, so that scenario construction and retrieval can focus on key disaster-causing factors, avoid redundant information interference, and improve matching accuracy.

[0141] The scenario and impact data in the database have been optimized through historical case simulation to ensure representativeness of the actual process and make the search results close to the real disaster impact.

[0142] The forecast data not only includes hydrological parameters (flooding depth, high tide duration), but also integrates socio-economic impacts (affected population, economic losses) and emergency information (key flooding areas, evacuation routes), achieving full chain coverage of "physical process-risk assessment-emergency measures".

[0143] The preset platform converts data into intuitive charts (such as risk zoning maps) and supports multi-dimensional linkage queries, making it easier for decision makers to quickly locate high-risk areas, allocate resources, and improve the targeted nature of disaster prevention measures.

[0144] like Figure 2 As shown, an embodiment of the present invention further provides a storm surge floodplain forecasting device 20 for a forecast area, comprising:

[0145] An acquisition module 21 is used to acquire typhoon forecast data and storm surge forecast data for the forecast area; and to acquire historical typhoon data, historical storm surge data, and basic information data for the forecast area;

[0146] The processing module 22 is used to pre-process the typhoon forecast data and storm surge forecast data to obtain standard data; input the standard data into a preset platform, and retrieve the storm surge floodplain scenario that is most similar to the standard data from a preset database in the preset platform; determine the storm surge floodplain impact data corresponding to the most similar storm surge floodplain scenario as forecast data; construct a storm surge floodplain numerical model based on the basic information data; set an influencing factor combination based on the historical typhoon data and historical storm surge data; simulate the influencing factor combination in the storm surge floodplain numerical model; determine the factor data that affects storm surge inundation based on the simulated storm surge floodplain impact data; construct a corresponding storm surge floodplain scenario based on the factor data; construct a preset database based on the storm surge floodplain scenario and the corresponding storm surge floodplain impact data; and integrate the preset database into the preset platform.

[0147] Optionally, the historical typhoon data includes: typhoon location, typhoon time, typhoon intensity, typhoon moving direction and typhoon moving speed;

[0148] The historical storm surge data include: hourly storm surge data, astronomical tide data and total tide level data;

[0149] The basic information data includes: basic geographical data and tide and wave data.

[0150] Optionally, constructing a storm surge floodplain numerical model based on the basic information data includes:

[0151] Based on the basic geographic data, a discrete grid of the forecast area is constructed using an unstructured triangular grid;

[0152] According to the tide and wave data, a coupled model of a hydrodynamic model and a wave model is used to construct a storm surge floodplain numerical model.

[0153] Optionally, according to the historical typhoon data and historical storm surge data, a combination of influencing factors is set, including:

[0154] By controlling variables, the various influencing factors of historical typhoon data and historical storm surge data are grouped and combined to obtain an influencing factor combination.

[0155] Optionally, combining the influencing factors and simulating them in the storm surge floodplain numerical model includes:

[0156] Combining the influencing factors, simulating in the storm surge floodplain numerical model, and obtaining simulated storm surge floodplain impact data;

[0157] Among them, the storm surge floodplain impact data include the inundation range, inundation depth and classification, average inundation depth, affected population, estimated economic losses, impact on disaster-bearing bodies, high tide duration, storm surge process curve, key inundation areas, emergency evacuation routes and / or risk zoning maps.

[0158] Optionally, based on the simulated storm surge floodplain impact data, determine the factors affecting storm surge inundation, including:

[0159] Determine the factors affecting storm surge inundation based on the simulated inundation range, inundation depth, classification and average inundation depth;

[0160] The factor data are influencing factors of historical typhoon data or influencing factors of historical storm surge data.

[0161] Optionally, constructing a corresponding storm surge floodplain scenario based on the factor data includes:

[0162] According to Z i =[X 1i , X 2i ,...,X mi ], constructing storm surge floodplain scenarios;

[0163] Among them, Z i For the storm surge floodplain scenario, X ji is the data of each factor, X ji ∈[x 1i , x 2i ,...,x mi ], x ji is the factor instance of each factor data, i=1, 2, ..., n, n is the total number of factor data, j=1, 2, ..., m, m is the total number of factor instances.

[0164] Optionally, the typhoon forecast data includes: real-time typhoon position, real-time typhoon time, real-time typhoon intensity, real-time typhoon moving direction and real-time typhoon moving speed;

[0165] The storm surge forecast data includes: real-time hourly storm surge data, real-time astronomical tide data and real-time total tide level data.

[0166] Optionally, preprocessing the typhoon forecast data and storm surge forecast data to obtain standard data includes:

[0167] extracting real-time factor instances corresponding to the factor data from the typhoon forecast data and storm surge forecast data;

[0168] The real-time factor instance is preprocessed to obtain standard data.

[0169] It should be noted that this device is a device corresponding to the above method, and all implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0170] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for predicting storm surge floodplains in a forecast area, characterized in that: include: Obtain typhoon forecast data and storm surge forecast data for the forecast area; Preprocessing the typhoon forecast data and storm surge forecast data to obtain standard data; Inputting the standard data into a preset platform, and retrieving a storm surge floodplain scenario that is most similar to the standard data from a preset database in the preset platform; Determining the storm surge floodplain impact data corresponding to the most similar storm surge floodplain scenario as forecast data; The construction process of the preset platform includes: Obtain historical typhoon data, historical storm surge data and basic information data for the forecast area; Constructing a storm surge floodplain numerical model based on the basic information data; Setting a combination of influencing factors based on the historical typhoon data and historical storm surge data; Combining the influencing factors and simulating them in the storm surge floodplain numerical model; Determine the factors affecting storm surge inundation based on the simulated storm surge floodplain impact data; Based on the factor data, the corresponding storm surge floodplain scenario is constructed; Constructing a preset database based on the storm surge floodplain scenario and the corresponding storm surge floodplain impact data; The preset database is integrated into the preset platform.

2. The method for storm surge floodplain forecasting in a forecast area according to claim 1, characterized in that: The historical typhoon data includes: typhoon location, typhoon time, typhoon intensity, typhoon moving direction and typhoon moving speed; The historical storm surge data include: hourly storm surge data, astronomical tide data and total tide level data; The basic information data includes: basic geographical data and tide and wave data.

3. The method for storm surge floodplain forecasting in a forecast area according to claim 2, wherein: Based on the basic information data, a storm surge floodplain numerical model is constructed, including: Based on the basic geographic data, a discrete grid of the forecast area is constructed using an unstructured triangular grid; According to the tide and wave data, a coupled model of a hydrodynamic model and a wave model is used to construct a storm surge floodplain numerical model.

4. The method for storm surge floodplain forecasting in a forecast area according to claim 2, wherein: According to the historical typhoon data and historical storm surge data, a combination of influencing factors is set, including: By controlling variables, the various influencing factors of historical typhoon data and historical storm surge data are grouped and combined to obtain an influencing factor combination.

5. The method for predicting storm surge floodplains in a forecast area according to claim 1, characterized in that: The influencing factors are combined and simulated in the storm surge floodplain numerical model, including: Combining the influencing factors, simulating in the storm surge floodplain numerical model, and obtaining simulated storm surge floodplain impact data; Among them, the storm surge floodplain impact data include the inundation range, inundation depth and classification, average inundation depth, affected population, estimated economic losses, impact on disaster-bearing bodies, high tide duration, storm surge process curve, key inundation areas, emergency evacuation routes and / or risk zoning maps.

6. The method for predicting storm surge floodplains in a forecast area according to claim 5, characterized in that: Based on the simulated storm surge floodplain impact data, determine the factors affecting storm surge inundation, including: Determine the factors affecting storm surge inundation based on the simulated inundation range, inundation depth, classification and average inundation depth; The factor data are influencing factors of historical typhoon data or influencing factors of historical storm surge data.

7. The method for storm surge floodplain forecasting in a forecast area according to claim 1, characterized in that: Based on the above factor data, the corresponding storm surge floodplain scenario is constructed, including: According to Z i =[X 1i , X 2i ,...,X mi ], constructing storm surge floodplain scenarios; Among them, Z i For the storm surge floodplain scenario, X ji is the data of each factor, X ji ∈[x 1i , x 2i ,...,x mi ], x ji is the factor instance of each factor data, i=1, 2, ..., n, n is the total number of factor data, j=1, 2, ..., m, m is the total number of factor instances.

8. The method for predicting storm surge floodplains in a forecast area according to claim 1, wherein: The typhoon forecast data includes: real-time typhoon position, real-time typhoon time, real-time typhoon intensity, real-time typhoon moving direction and real-time typhoon moving speed; The storm surge forecast data includes: real-time hourly storm surge data, real-time astronomical tide data and real-time total tide level data.

9. The method for predicting storm surge floodplains in a forecast area according to claim 1, wherein: Preprocessing the typhoon forecast data and storm surge forecast data to obtain standard data includes: extracting real-time factor instances corresponding to the factor data from the typhoon forecast data and storm surge forecast data; The real-time factor instance is preprocessed to obtain standard data.

10. A storm surge floodplain forecasting device for a forecast area, characterized in that: include: An acquisition module is used to obtain typhoon forecast data and storm surge forecast data in the forecast area; And obtain historical typhoon data, historical storm surge data and basic information data of the forecast area; A processing module, configured to pre-process the typhoon forecast data and storm surge forecast data to obtain standard data; Inputting the standard data into a preset platform, retrieving a storm surge floodplain scenario most similar to the standard data from a preset database in the preset platform; determining storm surge floodplain impact data corresponding to the most similar storm surge floodplain scenario as forecast data; constructing a storm surge floodplain numerical model based on the basic information data; setting an influencing factor combination based on the historical typhoon data and historical storm surge data; simulating the influencing factor combination in the storm surge floodplain numerical model; and determining factor data affecting storm surge inundation based on the simulated storm surge floodplain impact data; constructing a corresponding storm surge floodplain scenario based on the factor data; constructing a preset database based on the storm surge floodplain scenario and the corresponding storm surge floodplain impact data; The preset database is integrated into the preset platform.

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