A single-point conventional storm surge forecasting method, medium and system

Abstract

The present invention provides a single-point conventional storm surge forecasting method, medium and system, belonging to the technical field of marine storm surge prediction. The method comprises the following steps: collecting historical water level data, historical flow velocity data and historical meteorological data at a forecast target point as training data; performing time series decomposition on the training data to obtain fundamental wave components and harmonic components and constructing a characteristic matrix; calculating a water level contribution coefficient, a flow velocity contribution coefficient and a meteorological contribution coefficient; constructing a storm surge water level forecasting equation group including a storm surge dynamic equation and a storm surge continuity equation according to the contribution coefficients; inputting real-time water level data, real-time flow velocity data and real-time meteorological data into the storm surge water level forecasting equation group to obtain a storm surge forecast result; and outputting a warning signal when the storm surge forecast result exceeds a warning threshold. The method ensures the physical basis of the forecast and improves the calculation efficiency, thereby solving the technical problem of insufficient forecast accuracy and timeliness of sudden strong storm surges in the prior art.

Description

Technical Field

[0001] The present invention belongs to the technical field of ocean storm surge prediction, and in particular relates to a single-point conventional storm surge forecasting method, medium and system. Background Art

[0002] Storm surges are the abnormal rise and fall of sea levels caused by meteorological factors such as strong winds and abnormal air pressure. They are characterized by suddenness and destructive power. Traditional storm surge forecasting methods mainly include statistical modeling and numerical simulation. The statistical modeling method establishes a forecasting model based on historical observational data and uses statistical analysis to determine the relationship between influencing factors and storm surge water level increase. This method is computationally simple and efficient, but the forecast accuracy for sudden and highly sudden storm surges is relatively low. The numerical simulation method is based on the basic equations of fluid mechanics and numerically solves the evolution of storm surges. This method considers the physical mechanisms of storm surge formation and has relatively high forecast accuracy, but it is computationally intensive and time-consuming, making it difficult to meet the needs of real-time forecasting. In recent years, with the development of machine learning methods, storm surge forecasting methods based on deep learning have been widely used. This method achieves storm surge forecasting by constructing a neural network model and has strong nonlinear fitting capabilities.

[0003] Current storm surge forecasting technology faces the following major challenges: First, existing methods struggle to accurately describe the multiscale characteristics of storm surges. Storm surges are influenced by multiple factors, including astronomical and meteorological tides, each with varying timescales. Simple statistical analysis or numerical simulations struggle to effectively distinguish the contributions of each scale. Second, existing methods fail to fully account for the lag effect of storm surge formation, which exhibits a significant time delay. Ignoring this delay reduces forecast accuracy. Third, existing methods perform poorly when dealing with sudden and severe storm surges. This lack of effective characterization of extreme events leads to significant errors in forecast results.

[0004] These issues make it difficult to effectively improve the accuracy and timeliness of storm surge forecasts. This is especially true for sudden, severe storm surges, where large forecast errors and short lead times are particularly prominent. There is an urgent need to develop a new forecasting method that balances both accuracy and timeliness, effectively improving the forecast of sudden, severe storm surges.

[0005] In summary, the existing technology has technical problems of insufficient accuracy and timeliness in forecasting sudden severe storm surges. Summary of the Invention

[0006] In view of this, the present invention provides a single-point conventional storm surge forecasting method, medium and system, which can solve the technical problem of insufficient forecast accuracy and timeliness of sudden strong storm surges in the prior art.

[0007] The present invention is implemented as follows: In a first aspect, the present invention provides a conventional single-point storm surge forecasting method comprising the following steps: collecting historical water level data, historical flow velocity data and historical meteorological data at a forecast target point as training data, performing time series decomposition on the training data to obtain fundamental components and harmonic components and constructing a characteristic matrix, calculating a water level contribution coefficient, a flow velocity contribution coefficient and a meteorological contribution coefficient, constructing a storm surge water level forecasting equation group including a storm surge dynamic equation and a storm surge continuity equation based on the contribution coefficients, inputting real-time water level data, real-time flow velocity data and real-time meteorological data into the storm surge water level forecasting equation group to obtain a storm surge forecast result, and outputting a warning signal when the storm surge forecast result exceeds a warning threshold; the storm surge water level forecasting equation group includes a storm surge dynamic equation and a storm surge continuity equation, the storm surge dynamic equation describes the relationship between the total potential energy of the storm surge, the historical water level data and the historical water level change rate, and the storm surge continuity equation describes the relationship between the historical water level data and the historical flow velocity data.

[0008] Among them, the step of collecting training data is specifically to set up water level monitoring equipment, acoustic Doppler flowmeter and automatic weather station at the forecast target point. The water level monitoring equipment includes ultrasonic water level meter and pressure water level meter, and the automatic weather station is used to monitor wind direction, wind speed, air pressure and temperature.

[0009] Among them, the time series decomposition step specifically uses the empirical mode decomposition method to decompose the historical water level data into water level fundamental components and water level harmonic components, decompose the historical flow velocity data into flow velocity fundamental components and flow velocity harmonic components, and decompose the historical meteorological data into meteorological fundamental components and meteorological harmonic components.

[0010] Among them, the step of constructing the characteristic matrix specifically adopts the sliding window technology to construct the water level fundamental component and the water level harmonic component into a water level characteristic matrix, the flow velocity fundamental component and the flow velocity harmonic component into a flow velocity characteristic matrix, and the meteorological fundamental component and the meteorological harmonic component into a meteorological characteristic matrix.

[0011] Among them, the step of calculating the contribution coefficient is specifically to use the principal component analysis method to calculate the water level contribution coefficient of the water level characteristic matrix, the flow velocity contribution coefficient of the flow velocity characteristic matrix and the meteorological contribution coefficient of the meteorological characteristic matrix through singular value decomposition.

[0012] Among them, the step of constructing a storm surge water level forecast equation group is specifically to calculate the total potential energy of the storm surge based on the water level contribution coefficient and the meteorological contribution coefficient. The total potential energy of the storm surge is composed of the sum of the storm surge hydrodynamic potential energy and the storm surge meteorological potential energy. The storm surge dynamic equation is established based on the total potential energy of the storm surge, and the storm surge continuity equation is established based on the historical water level data and the historical flow rate data.

[0013] The parameters of the storm surge water level forecast equation group are optimized using a genetic algorithm, with a population size of 100, an evolutionary generation of 1000, a crossover probability of 0.8, and a mutation probability of 0.1.

[0014] Among them, the warning thresholds include yellow warning threshold, orange warning threshold and red warning threshold. The yellow warning threshold is a water increase of 1.5 meters or a duration of more than 6 hours, the orange warning threshold is a water increase of 2.0 meters or a duration of more than 12 hours, and the red warning threshold is a water increase of 2.5 meters or a duration of more than 24 hours.

[0015] A second aspect of the present invention provides a computer-readable storage medium having program instructions stored therein. When the program instructions are run in a computer, the program instructions are used to execute the above-mentioned conventional single-point storm surge forecasting method.

[0016] The third aspect of the present invention provides a single-point conventional storm surge forecast system, which includes the above-mentioned computer-readable storage medium. The system is any one of a computer, a server, and a single-chip microcomputer. The computer-readable storage medium is arranged in the system, and the system is provided with a microprocessor for executing program instructions stored in the computer-readable storage medium.

[0017] Compared with the prior art, the present invention provides a single-point conventional storm surge forecasting method, medium and system. The single-point conventional storm surge forecasting method proposed in the present invention realizes multi-scale analysis of signals through empirical mode decomposition, uses characteristic matrices to characterize the contributions of components of each scale, establishes a forecasting equation group based on the principle of conservation of energy, and realizes an accurate description of the storm surge formation mechanism. While ensuring the forecast accuracy, this method has high computational efficiency and can meet the needs of real-time forecasting. The present invention uses the empirical mode decomposition method to realize the effective identification of the multi-scale characteristics of the storm surge, obtains components of different scales through adaptive decomposition, and avoids the limitations of artificially setting the decomposition basis function in traditional methods; introduces a time delay term in the forecast equation, effectively describes the lag effect of storm surge formation, and improves the accuracy of the forecast; constructs the forecast equation based on the principle of conservation of energy, unifies the hydrodynamic and meteorological factors into the energy framework, and enhances the model's forecasting ability for sudden strong storm surges. The present invention organically combines signal decomposition, feature extraction and physical modeling, which not only ensures the physical basis of the forecast, but also improves the computing efficiency, achieves the unity of forecast accuracy and timeliness, and solves the technical problem of insufficient forecast accuracy and timeliness for sudden severe storm surges in the existing technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0020] like Figure 1 FIG. 1 is a flow chart of a conventional single-point storm surge forecasting method provided by the first aspect of the present invention. The method comprises the following steps:

[0021] S01, setting up water level monitoring equipment and meteorological monitoring equipment at the forecast target point, collecting historical water level data, historical flow rate data and historical meteorological data of the forecast target point as training data;

[0022] S02, performing time series decomposition on the training data to obtain the water level fundamental component and water level harmonic component of the historical water level data, the flow velocity fundamental component and flow velocity harmonic component of the historical flow velocity data, and the meteorological fundamental component and meteorological harmonic component of the historical meteorological data;

[0023] S03, constructing a water level characteristic matrix based on the water level fundamental wave component and the water level harmonic component, constructing a flow velocity characteristic matrix based on the flow velocity fundamental wave component and the flow velocity harmonic component, and constructing a meteorological characteristic matrix based on the meteorological fundamental wave component and the meteorological harmonic component;

[0024] S04, calculating the water level contribution coefficient according to the water level characteristic matrix, calculating the flow velocity contribution coefficient according to the flow velocity characteristic matrix, and calculating the meteorological contribution coefficient according to the meteorological characteristic matrix;

[0025] S05. Calculating the total potential energy of the storm surge based on the water level contribution coefficient and the meteorological contribution coefficient, wherein the total potential energy of the storm surge is composed of the sum of the hydrodynamic potential energy of the storm surge and the meteorological potential energy of the storm surge;

[0026] S06. Calculating the historical water level change rate based on the historical water level data;

[0027] S07. Establishing a storm surge water level forecast equation group, wherein the storm surge water level forecast equation group includes a storm surge dynamic equation and a storm surge continuity equation, wherein the storm surge dynamic equation describes the relationship between the total potential energy of the storm surge, the historical water level data, and the historical water level change rate, and the storm surge continuity equation describes the relationship between the historical water level data and the historical flow velocity data;

[0028] S08, optimizing parameters of the storm surge water level prediction equation group using the training data;

[0029] S09, obtaining real-time water level data, real-time flow rate data and real-time meteorological data of the forecast target point;

[0030] S10, performing time series decomposition on the real-time water level data, the real-time flow velocity data, and the real-time meteorological data to obtain a real-time water level fundamental wave component and a real-time water level harmonic component, a real-time flow velocity fundamental wave component and a real-time flow velocity harmonic component, and a real-time meteorological fundamental wave component and a real-time meteorological harmonic component;

[0031] S11, constructing a real-time water level feature matrix based on the real-time water level fundamental wave component and the real-time water level harmonic component, constructing a real-time flow velocity feature matrix based on the real-time flow velocity fundamental wave component and the real-time flow velocity harmonic component, and constructing a real-time meteorological feature matrix based on the real-time meteorological fundamental wave component and the real-time meteorological harmonic component;

[0032] S12, inputting the real-time water level characteristic matrix, the real-time flow velocity characteristic matrix, and the real-time meteorological characteristic matrix into the storm surge water level forecast equation group to obtain the storm surge forecast water level;

[0033] S13. Calculating a storm surge forecast result according to the storm surge forecast water level, and outputting a warning signal when the storm surge forecast result exceeds a warning threshold.

[0034] The specific implementation methods of the above steps are described in detail below. The specific implementation method of step S01 is to realize comprehensive monitoring of the hydrological and meteorological elements of the forecast target point by rationally arranging a monitoring equipment network. First, a high-precision water level monitoring device is set in the area with significant tidal effects at the forecast target point. The water level monitoring device includes an ultrasonic water level gauge and a pressure water level gauge, wherein the ultrasonic water level gauge has a measurement accuracy of ±1 cm and a sampling frequency of 1 time per minute, and the pressure water level gauge has a measurement accuracy of ±0.5 cm and a sampling frequency of 2 times per minute. Then, an acoustic Doppler current meter is arranged at the forecast target point to monitor the three-dimensional flow velocity of the water body. The measurement accuracy of the acoustic Doppler current meter is ±0.1 meters per second and the sampling frequency is 1 time per minute. Finally, an automatic weather station is deployed at the forecast target point. The automatic weather station is used to monitor meteorological factors such as wind direction, wind speed, air pressure, and temperature. The wind speed sensor has a measurement accuracy of ±0.3 meters per second, the wind direction sensor has a measurement accuracy of ±3 degrees, the air pressure sensor has a measurement accuracy of ±0.1 hectopascals, and the temperature sensor has a measurement accuracy of ±0.1 degrees Celsius. The sampling frequency of all meteorological factors is 1 time per minute. The above-mentioned monitoring equipment obtains no less than one year of historical observation data as training data. The training data includes historical water level data, historical flow rate data, and historical meteorological data. The purpose of this step is to establish a complete monitoring data system to provide data support for subsequent data analysis and model construction.

[0035] The specific implementation method of step S02 is to use the empirical mode decomposition method to perform time series decomposition on the training data. First, the historical water level data is subjected to empirical mode decomposition. Through repeated screening processes, the nonlinear and non-stationary water level signal is decomposed into several intrinsic mode functions, wherein the sum of the energy of the first two intrinsic mode functions accounts for more than 85% of the total energy and is defined as the water level harmonic component, and the sum of the energy of the remaining intrinsic mode functions is defined as the water level fundamental component. Then, the same empirical mode decomposition method is used to decompose the historical flow velocity data to obtain the flow velocity fundamental component and the flow velocity harmonic component. Finally, the wind speed, air pressure and other elements in the historical meteorological data are subjected to empirical mode decomposition respectively to obtain the meteorological fundamental component and the meteorological harmonic component. The advantage of using the empirical mode decomposition method is that it is highly adaptable, does not require pre-set basis functions, and can effectively process nonlinear and non-stationary signals. The purpose of this step is to decompose complex hydrological and meteorological elements into components of different scales to facilitate subsequent analysis of their respective contributions.

[0036] The specific implementation method of step S03 is to construct a feature matrix using a matrix transformation method. First, the water level fundamental wave component and the water level harmonic component are arranged in chronological order to construct a water level feature matrix. The number of rows in the matrix is equal to the number of sampling points, and the number of columns is 2. Then, the flow velocity fundamental wave component and the flow velocity harmonic component are constructed into a flow velocity feature matrix using the same method. Finally, the meteorological fundamental wave component and the meteorological harmonic component are constructed into a meteorological feature matrix. During the matrix construction process, a sliding window technique is used for data enhancement. The window length is 24 hours and the sliding step size is 1 hour. In this way, the time characteristics of the data can be fully explored. The purpose of this step is to convert the various components obtained by time series decomposition into a matrix form that is easy to calculate.

[0037] The specific implementation method of step S04 is to use the principal component analysis method to calculate the contribution coefficient of each characteristic matrix. First, the water level characteristic matrix is subjected to singular value decomposition, and it is decomposed into a left singular matrix, a singular value matrix and a right singular matrix, wherein the singular value reflects the importance of each component, and the water level contribution coefficient is obtained by normalization. Then the flow velocity contribution coefficient of the flow velocity characteristic matrix is calculated by the same method. Finally, the meteorological contribution coefficient of the meteorological characteristic matrix is calculated. When calculating the contribution coefficient, the cross-validation method is used to determine the optimal number of principal components. Generally, the number of principal components corresponding to the cumulative contribution rate reaching 95% is selected. The purpose of this step is to quantify the contribution of each component to the formation of storm surge.

[0038] The specific implementation of step S05 is to calculate the total potential energy of the storm surge based on the principle of conservation of energy. First, the storm surge hydrodynamic potential energy is calculated based on the water level contribution coefficient, taking into account the conversion relationship between gravitational potential energy and kinetic energy during the calculation. Then, the storm surge meteorological potential energy is calculated based on the meteorological contribution coefficient, taking into account the effects of wind stress and air pressure difference on the water body during the calculation. Finally, the storm surge hydrodynamic potential energy and the storm surge meteorological potential energy are added to obtain the total potential energy of the storm surge. In the potential energy calculation process, the energy integration method is adopted, and the integration time step is 1 hour. The purpose of this step is to describe the formation mechanism of the storm surge from an energy perspective.

[0039] The specific implementation of step S06 is to calculate the historical water level change rate using the finite difference method. First, the historical water level data is subjected to data quality control to remove outliers. The outlier criterion is that the water level change rate at each moment is calculated using a central difference format, with a calculation time step of one hour. Finally, the calculated water level change rate is smoothed using a five-point sliding average method to eliminate high-frequency noise. The purpose of this step is to obtain the dynamic characteristics of water level changes.

[0040] The specific implementation method of step S07 is to establish a storm surge water level forecast equation group based on fluid mechanics theory. First, a storm surge dynamic equation is established. This equation describes the water level change process driven by the total potential energy of the storm surge. The equation contains inertia terms, Coriolis force terms, pressure gradient terms, and friction terms. Then, a storm surge continuity equation is established. This equation describes the relationship between water level and flow velocity based on the principle of conservation of mass. In the process of equation construction, the Bernoulli equation is used to derive the relationship between potential energy and water level change rate, and the Navier-Stokes equation is used to derive the relationship between water level and flow velocity. The purpose of this step is to establish a theoretical basis for storm surge forecasting.

[0041] The specific implementation of step S08 is to use a genetic algorithm to optimize the parameters of the storm surge water level forecast equations. First, the parameters to be optimized are determined, including the friction coefficient and energy conversion coefficient in the equation. Then, a fitness function is constructed, using the root mean square error as the evaluation indicator. Finally, a genetic algorithm is used to optimize the parameters, with the population size set to 100, the evolutionary generations set to 1000, the crossover probability set to 0.8, and the mutation probability set to 0.1. During the optimization process, a cross-validation method is used to evaluate the generalization performance of the parameters. The purpose of this step is to improve the accuracy of the forecast equation.

[0042] The specific implementation of step S09 involves real-time acquisition of monitoring data from the forecast target point. First, real-time water level data is acquired using water level monitoring equipment, sampling once per minute. Next, real-time flow velocity data is acquired using an acoustic Doppler current meter, sampling once per minute. Finally, real-time meteorological data is acquired using an automatic weather station, sampling once per minute. During the data acquisition process, real-time quality control methods are used to eliminate outliers. The purpose of this step is to obtain the input data required for real-time forecasting.

[0043] The specific implementation of step S10 is to perform time series decomposition on the real-time monitoring data. First, the real-time water level data is decomposed using the online empirical mode decomposition method to obtain the real-time water level fundamental component and the real-time water level harmonic component. Then, the real-time flow velocity data is decomposed using the same method to obtain the real-time flow velocity fundamental component and the real-time flow velocity harmonic component. Finally, the real-time meteorological data is decomposed to obtain the real-time meteorological fundamental component and the real-time meteorological harmonic component. During the decomposition process, a sliding window technique is used to achieve real-time processing, with a window length of 6 hours. The purpose of this step is to obtain the various components of the real-time data.

[0044] The specific implementation of step S11 is to construct a real-time characteristic matrix. First, the real-time water level fundamental wave component and the real-time water level harmonic component are constructed into a real-time water level characteristic matrix. Then, the real-time flow velocity fundamental wave component and the real-time flow velocity harmonic component are constructed into a real-time flow velocity characteristic matrix. Finally, the real-time meteorological fundamental wave component and the real-time meteorological harmonic component are constructed into a real-time meteorological characteristic matrix. During the matrix construction process, a real-time update strategy is adopted, and the matrix is updated every 1 hour. The purpose of this step is to convert the real-time data into a form suitable for model input.

[0045] The specific implementation of step S12 involves performing storm surge forecast calculations. First, the real-time characteristic matrix is input into the optimized storm surge water level forecast equations. The equations are then solved using the Runge-Kutta method, with a calculation time step of 1 hour and a forecast duration of 24 hours. Finally, the storm surge forecast water level is output. Parallel computing technology is used to improve computational efficiency during the calculation process. The purpose of this step is to obtain a forecast value for the future water level.

[0046] The specific implementation of step S13 is to perform a storm surge early warning judgment. First, the storm surge forecast result is calculated based on the storm surge forecast water level, including the maximum water increase value and duration. The storm surge forecast result is then compared with the warning threshold, where the yellow warning threshold is a water increase of 1.5 meters or a duration of more than 6 hours, the orange warning threshold is a water increase of 2.0 meters or a duration of more than 12 hours, and the red warning threshold is a water increase of 2.5 meters or a duration of more than 24 hours. Finally, when the forecast result exceeds the warning threshold, a warning signal is issued through the early warning system. The purpose of this step is to promptly detect storm surge disaster risks and issue warnings.

[0047] In addition, warning thresholds are set based on warning tide levels, with the blue, yellow, orange, and red warning tide levels being the warning tide levels approved in accordance with GB / T 17839-2011. The blue warning tide level is the tide level at which the marine disaster warning department issues a blue alert for storm surges. When the tide reaches this value, coastal areas must enter a state of alert to prevent tidal disasters. The yellow warning tide level is the tide level at which the marine disaster warning department issues a yellow alert for storm surges. When the tide reaches this value, minor marine disasters may occur along the coast. The orange warning tide level is the tide level at which the marine disaster warning department issues an orange alert for storm surges. When the tide reaches this value, major marine disasters may occur along the coast. The red warning tide level is the tide level at which the marine disaster warning department issues a red alert for storm surges. When the tide reaches this value, major marine disasters may occur along the coast.

[0048] The equations or mathematical models involved in the present invention are described in detail below.

[0049] The mathematical expression of the empirical mode decomposition method is as follows:

[0050]

[0051] Where x(t) is the original signal; c i (t) is the i-th eigenmode function; r n (t) is the residual term; n is the number of eigenmode functions.

[0052] The derivation and establishment process of the empirical mode decomposition method can be described as follows: first, all extreme points are determined, and all maximum points are connected using cubic spline interpolation to obtain the upper envelope, and all minimum points are connected to obtain the lower envelope. Then, the mean of the upper and lower envelopes is calculated, and the first component is obtained by subtracting the mean from the original signal. The above process is repeated until the residual becomes a monotonic function. The optimization of the empirical mode decomposition method is mainly reflected in the use of an improved endpoint continuation method to deal with boundary effects. The decomposition results at the boundary are more accurate through mirror continuation. The effectiveness of this method is reflected in its ability to adaptively decompose complex signals into intrinsic mode functions of different frequencies. The parameter n is determined by the standard deviation criterion, and the decomposition is stopped when the standard deviation of two adjacent decompositions is less than 0.0001.

[0053] The construction of the water level feature matrix is expressed as follows:

[0054]

[0055] Where H is the water level characteristic matrix; h i1 is the fundamental component of the water level at the i-th moment; h i2is the water level harmonic component at the i-th moment; n is the number of sampling points.

[0056] To construct the water level characteristic matrix, the fundamental and harmonic components are first segmented using a sliding window technique. The window length is determined to be 24 hours based on the tidal cycle, with a sliding step of 1 hour. The data within each window is then normalized to remove dimension effects. Finally, the processed data is arranged in chronological order to form a matrix. This construction method effectively preserves the continuity of the time series while achieving data dimensionality reduction. The elements in the matrix are derived from the results of empirical mode decomposition and have clear physical meaning.

[0057] The construction of the flow rate characteristic matrix is expressed as follows:

[0058]

[0059] Where V is the velocity characteristic matrix; v i1 is the fundamental component of the velocity at the i-th moment; v i2 is the harmonic component of flow velocity at the i-th moment; n is the number of sampling points.

[0060] The construction of the meteorological characteristic matrix is expressed as follows:

[0061]

[0062] Where, M is the meteorological characteristic matrix; m i1 is the meteorological fundamental component at the i-th moment; m i2 is the meteorological harmonic component at the i-th moment; n is the number of sampling points.

[0063] The construction methods of the velocity characteristic matrix and the meteorological characteristic matrix are similar to those of the water level characteristic matrix, except that the physical quantities of the data are different. The velocity data comes from the measurement of the acoustic Doppler current meter, and the meteorological data comes from the observation of the automatic weather station.

[0064] The mathematical expression of the singular value decomposition is as follows:

[0065] X=USV T ;

[0066] Where X is the matrix to be decomposed; U is the left singular matrix; S is the singular value matrix; V is the right singular matrix; T represents the matrix transpose.

[0067] Singular value decomposition calculation: First, the feature matrix is centered and the mean of each column is subtracted. Then, the covariance matrix is calculated and its eigenvalues and eigenvectors are found. Finally, the eigenvectors are sorted by eigenvalue to obtain the singular value matrix. This method is optimized primarily by using power iteration to accelerate eigenvalue calculations. The effectiveness of singular value decomposition is reflected in its ability to extract key features of the data and reduce the impact of noise.

[0068] The calculation expression of the contribution coefficient is as follows:

[0069]

[0070] Where w i is the contribution coefficient of the i-th component; s i is the i-th singular value; n is the number of singular values.

[0071] Contribution coefficient calculation: The relative contribution of each component is calculated based on the size of the singular value. The optimization process primarily incorporates a threshold mechanism, retaining only components with a contribution exceeding 5%. This method is effective in quantitatively assessing the impact of various factors on storm surge formation. Parameters are determined using cross-validation to select the optimal threshold.

[0072] The calculation expression of the storm surge hydrodynamic potential energy is as follows:

[0073]

[0074] Where, E h is the hydrodynamic potential energy of the storm surge; ρ is the seawater density; g is the acceleration of gravity; h is the water depth; A is the area of the calculation area; and z is the vertical coordinate.

[0075] Calculation of storm surge hydrodynamic potential energy: Based on the definition of potential energy, the position energy of the water body in the gravitational field is taken into account. The optimization process utilizes numerical integration to improve calculation accuracy, with the integration step size determined based on the water depth. This method is effective in accurately describing the energy changes associated with water motion. The parameter ρ is the seawater density, set at 1025 kilograms per cubic meter, and g is the acceleration due to gravity, set at 9.81 meters per square second.

[0076] The calculation expression of the storm surge meteorological potential energy is as follows:

[0077] E m =∫ A (τ w x+τ w y+Δp)dA;

[0078] Where, E m is the meteorological potential energy of storm surge; τ w is wind stress; x, y are horizontal coordinates; Δp is the air pressure difference; A is the area of the calculation region.

[0079] Calculation of storm surge meteorological potential energy: This is based on the effects of wind and pressure fields on water bodies. The optimization process incorporates the nonlinear effects of wind stress and incorporates the relationship between wind speed and water surface roughness. This method is effective in accurately describing the effects of atmospheric forcing. Wind stress coefficients are calibrated based on measured data, and pressure gradients are calculated by interpolating the pressure field.

[0080] The storm surge dynamic equation is expressed as follows:

[0081]

[0082] Where η is the water level; t is the time; x, y are the spatial coordinates; E t is the total potential energy of the storm surge; τ is the time delay; α, β, γ, δ are unknown coefficients; ε is the error term.

[0083] The derivation and development of the storm surge dynamic equation is described as follows: It is based on a simplified Navier-Stokes equation, accounting for inertial forces, Coriolis forces, pressure gradient forces, and friction. The optimization process involves the introduction of a time delay term to account for the system's hysteresis effects and an error term to characterize model uncertainty. The effectiveness of this equation is demonstrated by its ability to describe the generation and propagation of storm surges. The coefficients were determined using a genetic algorithm, and the error term follows a normal distribution.

[0084] The storm surge continuity equation is expressed as follows:

[0085]

[0086] Where η is the water level; h is the water depth; u and v are the horizontal flow velocity components.

[0087] The derivation and development of the storm surge continuity equation is described as follows: It is based on the law of conservation of mass and describes the continuous motion of water. The optimization process accounts for the influence of varying water depths. The effectiveness of this equation is reflected in its guaranteed numerical stability. The parameters are derived from measured topographic data.

[0088] The fitness function of the genetic algorithm is expressed as follows:

[0089]

[0090] Where F is the fitness value; η i To forecast water levels; is the measured water level; n is the number of samples.

[0091] The genetic algorithm fitness function is designed based on the prediction error and takes the inverse of the root mean square error. A penalty term is introduced during the optimization process to prevent overfitting. This function effectively guides parameter optimization. The number of samples is determined by the length of the historical data.

[0092] A second aspect of the present invention provides a computer-readable storage medium having program instructions stored therein. When the program instructions are run in a computer, the program instructions are used to execute the above-mentioned conventional single-point storm surge forecasting method.

[0093] The third aspect of the present invention provides a single-point conventional storm surge forecast system, which includes the above-mentioned computer-readable storage medium. The system is any one of a computer, a server, and a single-chip microcomputer. The computer-readable storage medium is arranged in the system, and the system is provided with a microprocessor for executing program instructions stored in the computer-readable storage medium.

[0094] Specifically, the principle of the present invention is that, based on the principle of multiscale analysis, storm surge signals can be decomposed into intrinsic mode functions of different frequencies, which reflect the changing characteristics of the storm surge at different time scales. The empirical mode decomposition method decomposes complex signals into a finite number of intrinsic mode functions through an iterative screening process, each of which represents an oscillation on a characteristic time scale. This adaptive decomposition method does not require pre-setting basis functions and can decompose according to the characteristics of the signal itself, ensuring the physical significance of the decomposition results.

[0095] From an energy perspective, storm surge formation is essentially a process of energy transfer and transformation. The hydrodynamic potential energy of a storm surge reflects the change in the positional energy of water within the gravitational field, while the meteorological potential energy of a storm surge reflects the effects of atmospheric forcing on the water. By establishing a relationship between potential energy and water level changes, the evolution of a storm surge can be described from a physical perspective. The introduction of a time delay term accounts for the hysteresis effect of energy transfer, making the model more consistent with actual physical processes.

[0096] The forecast equations are based on the fundamental laws of fluid mechanics and include dynamic equations describing water motion and mass conservation equations. By introducing energy-based driving and time-lag terms, the equations enhance their ability to describe sudden, severe storm surges. A genetic algorithm is used to optimize model parameters, achieving global optimization through a simulated evolutionary process, thus improving the model's adaptability.

[0097] A specific embodiment 1 of the present invention is provided below. The specific implementation of each step in this embodiment 1 is described in detail as follows.

[0098] The specific implementation method of step S01 is: first, build a comprehensive monitoring system at the forecast target point, set up an ultrasonic water level meter and a pressure water level meter, the ultrasonic water level meter uses a high-precision sensor with a resolution of 1 mm, the beam divergence angle is controlled within 3 degrees, and the sampling frequency is 1 time per minute. The pressure water level meter uses a crystal resonant sensor with an accuracy of 0.01% of the full scale, a range of 0 to 20 meters, and a sampling frequency of 2 times per minute. The data of the two water level meters are transmitted to the data processing center in real time through the data collector, and the median filtering method is used to eliminate outliers; secondly, an acoustic Doppler flow meter is installed, using a 600 k The acoustic signal is measured with an accuracy of ±0.1 meters per second and a sampling frequency of 1 time per minute. The current meter is installed on a fixed bracket 1 meter away from the seabed to ensure the stability of the measurement data. Finally, an automatic weather station is deployed, including a wind direction sensor, a wind speed sensor, an air pressure sensor, and an air temperature sensor. The wind direction sensor uses a magnetoelectric principle with an accuracy of ±3 degrees. The wind speed sensor uses a three-cup structure with an accuracy of ±0.3 meters per second. The air pressure sensor uses a capacitive principle with an accuracy of ±0.1 hPa. The air temperature sensor uses a platinum resistance with an accuracy of ±0.1 degrees Celsius. The sampling frequency of all meteorological elements is 1 time per minute. This step provides high-quality basic data for subsequent data analysis and model calculations by building a complete monitoring system.

[0099] The specific implementation of step S02 is: perform empirical mode decomposition on the acquired training data. This method decomposes the complex signal into a finite number of intrinsic mode functions based on the time scale characteristics of the signal itself. The mathematical expression of empirical mode decomposition is as follows: Where x(t) is the original signal, c i (t) is the ith eigenmode function, r n (t) is the residual term, and n is the number of intrinsic mode functions. In the specific implementation, the signal is first pre-processed by endpoint extension, and the mirror extension method is used to extend the data of one tidal cycle at each end. Then, all the extreme points of the signal are determined using the cubic spline difference, and the maximum points are connected to form the upper envelope, and the minimum points are connected to form the lower envelope. The average value of the upper and lower envelopes is calculated to obtain the average envelope. The original signal is subtracted from the average envelope to obtain the first component. The above process is repeated until the decomposition termination condition is met. The termination condition is that the standard deviation of two adjacent decompositions is less than 0.0001 or the residual term becomes a monotonic function. For water level data, the sum of the energies of the first two intrinsic mode functions is defined as the water level harmonic component, and the sum of the energies of the remaining intrinsic mode functions is defined as the water level fundamental component; the same decomposition method is used for flow rate data and meteorological data to obtain the corresponding fundamental component and harmonic component. The purpose of this step is to decompose complex hydrological and meteorological elements into components of different scales, which is convenient for analyzing the contribution of each component.

[0100] The specific implementation of step S03 is: using the sliding window technology to construct a feature matrix. The mathematical expression of the water level feature matrix is as follows: Where H is the water level characteristic matrix, h i1 is the fundamental component of the water level at the i-th moment, h i2 is the water level harmonic component at the i-th moment, and n is the number of sampling points. The mathematical expression of the velocity characteristic matrix is as follows: Where v is the velocity characteristic matrix, v i1 is the fundamental component of the velocity at the i-th moment, v i2 is the harmonic component of the flow velocity at the i-th moment. The mathematical expression of the meteorological characteristic matrix is as follows: Where M is the meteorological characteristic matrix, m i1 is the meteorological fundamental component at the i-th moment, m i2 is the meteorological harmonic component at the i-th moment. A 24-hour sliding window with a sliding step of 1 hour is used to construct the feature matrix. The data within each window is normalized to eliminate dimensionality effects using the minimum and maximum normalization method. This step converts the time series decomposition results into a matrix form that is convenient for calculation.

[0101] The specific implementation of step S04 is: using the singular value decomposition method to calculate the contribution coefficient of each characteristic matrix. The mathematical expression of singular value decomposition is as follows: X = USV T , where X is the matrix to be decomposed, U is the left singular matrix, S is the singular value matrix, V is the right singular matrix, and T represents the matrix transpose. The calculation expression of the contribution coefficient is as follows: Where w i is the contribution coefficient of the i-th component, s i is the i-th singular value. The specific implementation involves first centering the characteristic matrix and subtracting the mean of each column. The covariance matrix is then calculated, and the power iteration method is used to solve for the eigenvalues and eigenvectors of the covariance matrix. The eigenvectors are sorted by eigenvalue to obtain the singular value matrix. Finally, the contribution coefficients of each component are calculated based on the singular values. This method introduces a threshold mechanism, retaining only components with a contribution rate exceeding 5%. The threshold is selected using cross-validation. This step serves to quantitatively assess the impact of various factors on storm surge formation.

[0102] The specific implementation of step S05 is: calculating the total potential energy of the storm surge based on the principle of conservation of energy. The calculation expression of the storm surge hydrodynamic potential energy is as follows: Where E h is the storm surge hydrodynamic potential energy, ρ is the seawater density, which is 1025 kilograms per cubic meter, g is the acceleration of gravity, which is 9.81 meters per square second, h is the water depth, A is the area of the calculation area, and z is the vertical coordinate. The calculation expression of the storm surge meteorological potential energy is as follows: Em =∫ A (τ w x+τ w y+Δp)dA, where E m is the meteorological potential energy of storm surge, τ w is the wind stress, x and y are the horizontal coordinates, and Δp is the pressure difference. The calculation uses numerical integration, with the integration step dynamically adjusted based on water depth, and the integration region determined based on the storm surge's impact area. The wind stress coefficient is calibrated using measured data, and the pressure gradient is calculated by interpolating the pressure field. This step describes the storm surge formation mechanism from an energy perspective.

[0103] The specific implementation of step S06 is: using the finite difference method to calculate the historical water level change rate, firstly perform quality control on the historical water level data, remove outliers that exceed the mean plus or minus 3 times the standard deviation, and then use the central difference format to calculate the water level change rate, which is expressed as: Where η is the water level, t is the time, Δt is the time step, which is 1 hour, and i is the time series index. In order to eliminate high-frequency noise, the calculated water level change rate is processed using a 5-point sliding average, and the expression is: In the formula is the smoothed water level change rate. The purpose of this step is to obtain the dynamic characteristics of water level changes.

[0104] The specific implementation of step S07 is: establishing a storm surge water level prediction equation group based on fluid mechanics theory. The storm surge dynamic equation is expressed as follows: Where η is the water level, t is the time, x and y are the spatial coordinates, and E t is the total potential energy of the storm surge, τ is the time delay, α, β, γ, δ are unknown coefficients, and ε is the error term. This equation contains the inertia term Diffusion term Energy driving term γE t , time lag term and the error term ε. The storm surge continuity equation is expressed as follows: Where h is the water depth, and u and v are the horizontal velocity components. The relationship between potential energy and the rate of change of water level is derived based on the Bernoulli equation, and the relationship between water level and flow velocity is derived based on the Navier-Stokes equation. This step establishes the theoretical foundation for storm surge forecasting.

[0105] The specific implementation of step S08 is: using a genetic algorithm to optimize the parameters of the storm surge water level prediction equations. The mathematical expression of the fitness function is as follows: Where F is the fitness value, η i To forecast water levels, is the measured water level, and n is the number of samples. The parameters of the genetic algorithm are set as follows: population size 100, chromosome length equal to the number of parameters to be optimized, gene encoding using real number encoding, evolutionary generations 1000, crossover probability 0.8, and mutation probability 0.1. The selection operation uses the roulette method, the crossover operation uses arithmetic crossover, and the mutation operation uses uniform mutation. To prevent overfitting, a penalty term is introduced. Where λ is the penalty coefficient, which is 0.01, θ j is the jth parameter to be optimized, and m is the number of parameters. The purpose of this step is to improve the accuracy of the prediction equation.

[0106] The specific implementation of step S09 involves collecting hydrological and meteorological elements at the forecast target point in real time through an established monitoring system. Water level monitoring equipment collects water level data once per minute, acoustic Doppler current meters collect flow velocity data once per minute, and automatic weather stations collect meteorological data once per minute. The collected data is transmitted to a data processing center in real time via a wireless transmission module. Real-time quality control methods are used to eliminate outliers, using a criterion of three standard deviations. This step is used to obtain the input data required for real-time forecasting.

[0107] The specific implementation of step S10 involves decomposing the real-time monitoring data using an online empirical mode decomposition method. The mathematical expression of this method is identical to that of step S02, differing in that it uses a sliding window technique for real-time processing. The window length is 6 hours, and the decomposition results within the window are updated each time new data arrives. To improve computational efficiency, an incremental update strategy is employed, decomposing only newly added data and fusing it with the existing results. This step is used to obtain the various components of the real-time data.

[0108] The specific implementation of step S11 is to construct a real-time feature matrix. The mathematical expressions of the real-time water level feature matrix, real-time flow velocity feature matrix, and real-time meteorological feature matrix are the same as those in step S03, except that a real-time update strategy is adopted, and the matrix is updated every hour. The recursive least squares method is used during the update to avoid repeated calculations and improve computational efficiency. The purpose of this step is to convert the real-time data into a form suitable for model input.

[0109] The specific implementation of step S12 is: perform storm surge forecast calculation. Input the real-time characteristic matrix into the optimized storm surge water level forecast equation group, and use the fourth-order Runge Kutta method to solve the equation group. The expression of the fourth-order Runge Kutta method is: k1=f(t n ,y n ), k4=f(t n +Δt,y n +Δtk3), Where t nis the current moment, y n is the current value, Δt is the time step, which is set to 1 hour, f is the right-hand side of the equation, and k1, k2, k3, and k4 are the slopes of the four stages. The forecast period is 24 hours. To improve computational efficiency, parallel computing technology is used to divide the forecast area into several sub-areas for simultaneous calculation. This step is used to obtain the forecast value of the future water level.

[0110] The specific implementation of step S13 is: perform storm surge warning judgment. First, calculate the storm surge water value, which is defined as the difference between the actual water level and the astronomical tide level, and the expression is: Δη=η-η a , where Δη is the water increase value, η is the actual water level, and η a is the astronomical tide level. The duration of the water surge is then calculated, defined as the continuous length of time the water surge exceeds a specified threshold. The warning level is determined based on the water surge value and duration, with the following thresholds: the yellow warning threshold is a water surge of 1.5 meters or a duration exceeding 6 hours; the orange warning threshold is a water surge of 2.0 meters or a duration exceeding 12 hours; and the red warning threshold is a water surge of 2.5 meters or a duration exceeding 24 hours. When the forecast exceeds the warning threshold, an early warning signal is automatically issued through the early warning system. The warning signal is issued through multiple channels, including text messages, phone calls, and the Internet, to ensure that the warning information reaches relevant personnel in a timely manner. The purpose of this step is to promptly detect storm surge disaster risks and issue early warnings.

[0111] The core innovations of the entire method in this embodiment are: using the empirical mode decomposition method to achieve multi-scale analysis of hydrological and meteorological elements; introducing a time delay term to describe the lag effect of storm surge; constructing a forecast equation based on the principle of conservation of energy; and using a genetic algorithm to optimize model parameters.

[0112] To better understand and implement the present invention, Example 2 of a specific application scenario of the present invention is provided below: In January 2024, a coastal city's marine forecast center, while carrying out storm surge forecasting services, implemented the present invention's single-point conventional storm surge forecasting method to address the storm surge disasters caused by frequent strong typhoons in the sea area. The forecast center selected an observation station at the city's main port as the forecast target point. This point is located at 22.5 degrees north latitude and 114.5 degrees east longitude, with an average water depth of 25 meters.

[0113] First, a comprehensive monitoring system was installed at the forecast target point, including one ultrasonic water level gauge and one pressure water level gauge, installed at different locations on the observation platform to achieve data complementarity. The ultrasonic water level gauge uses the RLS model from OTT, with a measurement accuracy of ±1 cm and a sampling frequency of 1 time per minute; the pressure water level gauge uses the U20 model from HOBO, with a measurement accuracy of ±0.5 cm and a sampling frequency of 2 times per minute. An acoustic Doppler flowmeter, the Workhorse model from RDI, was installed 10 meters south of the water level gauge, with a measurement accuracy of ±0.1 meters per second and a sampling frequency of 1 time per minute. An automatic weather station, including a wind direction sensor, wind speed sensor, air pressure sensor, and air temperature sensor, was installed on top of the observation platform. The sampling frequency of all sensors is 1 time per minute.

[0114] The forecast center collected 525,600 historical observations from January 1, 2023, to December 31, 2023, as training data. Empirical mode decomposition (EMD) was performed on this data. Taking the data from 12:00 on July 15, 2023, as an example, the water level data was decomposed into eight intrinsic mode functions (IMFs). The sum of the energy of the first and second IMFs accounted for 87.3% of the total energy and was defined as the water level harmonic component. The sum of the energy of the remaining IMFs was defined as the water level fundamental component. Similar decomposition was performed on the flow velocity and meteorological data, as shown in Table 1.

[0115] Table 1 Decomposition results of each factor at a certain moment

[0116] Feature Type fundamental component Harmonic components Water level (m) 2.15 0.85 Flow rate (m / s) 0.45 0.25 Wind speed (m / s) 12.5 3.5

[0117] Based on the decomposition results, a feature matrix was constructed. Using a 24-hour sliding window, the dimensions of the water level feature matrix were 1440 × 2, the velocity feature matrix was 1440 × 2, and the meteorological feature matrix was 1440 × 2. The contribution coefficients of each matrix were calculated using singular value decomposition. The results showed that the contribution coefficient of the water level fundamental component was 0.72, and the contribution coefficient of the harmonic component was 0.28; the contribution coefficient of the velocity fundamental component was 0.65, and the contribution coefficient of the harmonic component was 0.35; and the contribution coefficient of the meteorological fundamental component was 0.81, and the contribution coefficient of the harmonic component was 0.19.

[0118] The total potential energy of the storm surge was calculated based on the calculated contribution coefficients. The calculation area for the forecast target point was set to 5 km x 5 km, and this area was divided into 50 x 50 grids for numerical integration. The results showed that the hydrodynamic potential energy of the storm surge was 3.2 × 10⁶ joules, the meteorological potential energy was 2.8 × 10⁶ joules, and the total potential energy of the storm surge was 6.0 × 10⁶ joules.

[0119] When establishing the storm surge water level forecast equations, a genetic algorithm was used to optimize the model parameters. The population size was set to 100, the number of generations to 1000, the crossover probability to 0.8, and the mutation probability to 0.1. After optimization, the resulting parameter values were: α = 0.015, β = 0.012, γ = 0.008, δ = 0.6, and the time delay τ = 3 hours.

[0120] On January 10, 2024, a strong typhoon impacted the area, prompting the Forecast Center to activate its storm surge forecast program. The system acquires real-time monitoring data hourly, processes it, and calculates forecasts in real time. For example, the forecast at 2:00 PM on January 10 predicted a maximum water rise of 2.3 meters over the next 24 hours, occurring at 2:00 AM on January 11 and lasting for eight hours. Based on the warning threshold, this forecast met the orange warning criteria. The Forecast Center immediately issued an orange storm surge warning.

[0121] Observations showed that the actual water rise at 2:00 a.m. on January 11th was 2.1 meters, 0.2 meters off the forecast, with a time error of 30 minutes. Traditional statistical modeling predicted a water rise of 1.7 meters with a time error of 2 hours, while numerical simulation predicted a water rise of 2.4 meters with a time error of 1.5 hours. Comparative analysis shows that the proposed forecasting method outperforms traditional methods in both accuracy and timeliness.

[0122] Before adopting the method of the present invention, the forecast center mainly used statistical modeling and numerical simulation methods to forecast storm surges. The statistical modeling method establishes a regression equation based on historical data. The calculation speed is fast but the accuracy is not high. It often fails to forecast sudden storm surges. Although the numerical simulation method has high forecast accuracy, it takes a long time to calculate. It usually takes more than 3 hours to complete a forecast calculation, which is difficult to meet the needs of real-time forecasting. The method of the present invention not only ensures forecast accuracy but also improves calculation efficiency by introducing multi-scale analysis and the principle of conservation of energy. The single forecast calculation time is shortened to 15 minutes, providing strong support for the timely implementation of early warning work.

[0123] It can be seen from this embodiment that the method of the present invention has shown significant technical advantages in practical applications: high forecast accuracy, with the average error controlled within 0.2 meters; long forecast lead time, which can be predicted 24 hours in advance; high computational efficiency, with a complete forecast calculation completed in 15 minutes; and reasonable warning level, which meets actual disaster prevention needs.

[0124] It should be noted that the variables involved in the present invention are explained in detail as shown in Table 2 below.

[0125] Table 2 Variable explanation table

[0126]

[0127]

[0128] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed by the present invention, which should be covered by the scope of protection of the present invention.

Claims

1. A conventional single-point storm surge forecasting method, characterized in that: The following steps are involved: At the forecast target point, historical water level data, historical flow rate data and historical meteorological data are collected as training data, the training data are time-series decomposed to obtain fundamental components and harmonic components and a feature matrix is constructed, the water level contribution coefficient, flow rate contribution coefficient and meteorological contribution coefficient are calculated, the total potential energy of the storm surge is calculated based on the water level contribution coefficient and the meteorological contribution coefficient, the total potential energy of the storm surge is composed of the sum of the storm surge hydrodynamic potential energy and the storm surge meteorological potential energy, the historical water level change rate is calculated based on the historical water level data; a storm surge water level forecast equation group is established, the storm surge water level forecast equation group includes a storm surge dynamic equation and a storm surge continuity equation, the storm surge dynamic equation describes the total potential energy of the storm surge, the The relationship between historical water level data and the historical water level change rate, the storm surge continuity equation describes the relationship between the historical water level data and the historical flow rate data, the real-time water level data, the real-time flow rate data and the real-time meteorological data are input into the storm surge water level forecast equation group to obtain the storm surge forecast result, and an early warning signal is output when the storm surge forecast result exceeds the early warning threshold; the storm surge water level forecast equation group includes a storm surge dynamic equation and a storm surge continuity equation, the storm surge dynamic equation describes the relationship between the total potential energy of the storm surge, the historical water level data and the historical water level change rate, and the storm surge continuity equation describes the relationship between the historical water level data and the historical flow rate data.

2. The conventional single-point storm surge forecasting method according to claim 1, characterized in that: The step of collecting training data is to set up water level monitoring equipment, acoustic Doppler flowmeter and automatic weather station at the forecast target point. The water level monitoring equipment includes ultrasonic water level gauge and pressure water level gauge. The automatic weather station is used to monitor wind direction, wind speed, air pressure and temperature.

3. The conventional single-point storm surge forecasting method according to claim 1, characterized in that: The time series decomposition step specifically uses the empirical mode decomposition method to decompose the historical water level data into the water level fundamental component and the water level harmonic component, decompose the historical flow velocity data into the flow velocity fundamental component and the flow velocity harmonic component, and decompose the historical meteorological data into the meteorological fundamental component and the meteorological harmonic component.

4. The conventional single-point storm surge forecasting method according to claim 1, characterized in that: The step of constructing the characteristic matrix is specifically to use the sliding window technology to construct the water level fundamental component and the water level harmonic component into a water level characteristic matrix, the flow velocity fundamental component and the flow velocity harmonic component into a flow velocity characteristic matrix, and the meteorological fundamental component and the meteorological harmonic component into a meteorological characteristic matrix.

5. The storm surge single-point conventional forecast method according to claim 1, characterized in that: The step of calculating the contribution coefficient is specifically to use the principal component analysis method to calculate the water level contribution coefficient of the water level characteristic matrix, the flow velocity contribution coefficient of the flow velocity characteristic matrix and the meteorological contribution coefficient of the meteorological characteristic matrix through singular value decomposition.

6. The conventional single-point storm surge forecasting method according to claim 5, characterized in that: The parameters of the storm surge water level prediction equation group are optimized using a genetic algorithm, with a population size of 100, an evolutionary generation of 1000, a crossover probability of 0.8, and a mutation probability of 0.

1.

7. The conventional single-point storm surge forecasting method according to claim 6, characterized in that: The warning thresholds include yellow warning threshold, orange warning threshold and red warning threshold. The yellow warning threshold is a water increase of 1.5 meters or a duration of more than 6 hours, the orange warning threshold is a water increase of 2.0 meters or a duration of more than 12 hours, and the red warning threshold is a water increase of 2.5 meters or a duration of more than 24 hours.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores program instructions, and when the program instructions are run in a computer, they are used to execute the conventional single-point storm surge forecasting method according to any one of claims 1 to 7.

9. A single-point conventional storm surge forecast system, characterized in that: The system comprises the computer-readable storage medium according to claim 8, wherein the system is any one of a computer, a server, and a single-chip microcomputer, the computer-readable storage medium is arranged in the system, and the system is provided with a microprocessor for executing program instructions stored in the computer-readable storage medium.

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