KR101568819B1 - Coastal erosion automatic forecasting method using active data collection type script and numerical model - Google Patents

Abstract

The present invention relates to a method for automatically predicting short-term coastal erosion by typhoon by using a seawater flow model such as an advanced circulation model, which simulates tides, typhoon, and tsunami, a sedimentation model such as an Xbeach model, which simulates costal erosion, and an active process program constructed with Perl or Phython. According to the present invention, tides are simulated in real time by continuously and repeatedly running the seawater flow model; the active process program accesses a typhoon predicting and warning institution such as a joint typhoon warning center (JTWC) in the U.S. and actively reads typhoon information; the active process program determines need of prediction of an impact of typhoon based on the read typhoon information and adds the read typhoon information to input information of the seawater flow model; and flow of seawater and coastal erosion and sedimentation thereof are automatically simulated. According to the present invention, impacts of tides and typhoon can be complexly applied to a numerical simulation of coastal erosion by typhoon. Therefore, coastal erosion can be precisely and rapidly predicted, and a coastal erosion forecasting and warning effect can be increased.

Description

Technical Field [0001] The present invention relates to a coastal erosion automatic prediction method using an active information collection script and a numerical model,

The present invention relates to a seawater flow model such as an ADCIRC (Advanced CIRCulation) model simulating a tidal and storm surge, a sedimentation model such as an Xbeach model simulating a coast sediment sediment, a script such as Perl or Python, , The system automatically predicts the short-term coastal erosion caused by typhoons, and simulates the tide in real time by repeatedly operating the seawater flow model, The Joint Typhoon Warning Center (JTWC) and other typhoon warning agencies actively read the typhoon information, and based on the read typhoon information, the active processing program determines the need for prediction of the typhoon impact, After adding typhoon information to the information, it is possible to automatically simulate sea water flow and subsequent coast sediment accumulation.

Typhoons, which are tropical low-pressure typhoons in the western part of the Pacific Ocean, account for about 30% of the typhoons per year, of which about 10% have a direct impact on the Korean peninsula.

Typhoons cause strong winds over 17m / s with strong storm surges and pressure changes over a wide area, resulting in massive flows in the sea surface of the typhoon. Especially short-term coastal erosion caused by typhoons causes severe damage .

In predicting short-term erosion due to typhoons, coastal erosion characteristics such as size, location, and range are determined by the seawater flow phenomenon such as typhoon seawall caused by typhoon, The main factors that dominate the phenomenon are the meteorological characteristics of typhoons such as scale, intensity and route, the seabed topography and coastal topography of typhoon passing area, and the tide of the sea area.

Therefore, for realistic prediction of short-term coastal erosion due to typhoons, it is necessary to collect information on these key factors quickly and systematically and accurately analyze the collected information. Like other meteorological phenomena, Not only is the collection and processing of vast amounts of information required, but there is also a variety of uncertainties in processing and normalization, resulting in the derivation of predictions that combine accuracy and speed.

An attempt has been made to predict Typhoon Tsunil and the like on the basis of computerized processing, and related art is Patent No. 756265.

The conventional computation-based typhoon damage prediction method including patent No. 756265 does not predict the characteristics of typhoon tsunamis according to the results of numerical simulations of physical phenomena, but rather estimates the available scenarios based on long- It is not a mathematical simulation of actual physical phenomena by systematically reflecting various factors that determine the characteristics of typhoon tsunami. It is based on the collected observation information, And classified them into types, and assigns them to typhoon damage cases by type of typhoon.

Therefore, it is hardly expected to be accurate enough to predict the point and point of occurrence of actual typhoon damage, and to take measures such as evacuation for the purpose of mitigating the damage in the area, and in particular, the conventional computation processing including patent No. 756265 Based hurricane damage prediction method has focused on the tsunami and prediction, and there is no consideration of the sedimentation phenomenon caused by the seawater flow.

In this paper, we can consider a numerical model that simulates the physical phenomena of coastal sediment sedimentation as well as the typhoon effect, as well as the discretization of the governing equations, However, real-time simulation or prediction of the typhoon effect through numerical model requires the construction of massive input information necessary for the operation of the numerical model, application of sufficient simulation period to derive meaningful results, and immediate reflection of the changing weather phenomenon , It takes a long time to operate the numerical model for actual prediction of the typhoon effect even when the numerical model operation is accompanied by many troubles.

In addition, as described above, the characteristics of short-term coast erosion due to typhoons are not limited to the meteorological characteristics of the typhoon itself such as scale, strength, and path, and the seabed topography and coastal topography of the typhoon passing area, The simple operation of the numerical model can not sufficiently reflect these various characteristics, and therefore, it is basically impossible to predict the coast erosion close to the actual phenomenon.

SUMMARY OF THE INVENTION The present invention has been made in view of the above problems, and it is an object of the present invention to provide a coastal erosion prediction method using a numerical model, in which an active processing program, a seawater flow model and a sedimentation model are mounted on an Internet- (S11) in which an active processing program accesses a typhoon warning agency web page, reads typhoon information and stores the read information in a storage device, and a geometry The initial flow information of the simulated object area is input as the initial condition, and the input information of the sedimentation model is input to the initial input step S12 (step S12) in which the geometric information composed of the lattice information and the terrain information of the simulated object area and the soil information are input ), And the seawater flow model simulates the seawater flow in the simulated area, Calculating the flow information by time of the time interval and storing the flow information in the memory device of the computer and periodically changing the future time as the actual time elapses, An iterative connection step S31 of reading the typhoon information by connecting the active processing program to the typhoon warning organ web page, comparing the information with the typhoon information stored in the storage device, periodically repeating reading and comparing the typhoon information, An overlap determination step (S32) of storing the changed typhoon information in the storage device of the computer when the typhoon information is changed and extracting the path information among the read typhoon information to determine whether the start area of the corresponding typhoon route is overlapped, When the path overlaps with the start area, the active processing program adds the corresponding typhoon information to the input information of the seawater flow model, A prediction start step S41 for applying calculation flow information at a corresponding point in time calculated flow information calculated at the base simulation step S20 as initial flow information of the flow guide flow model, Based on the initial flow information, the seawater flow model simulates the seawater flow in the simulated object area and calculates the predicted flow information by unit time interval from the present time to the preset future time and stores it in the computer storage device. A flow input step (S61) in which the active processing program adds the predicted flow information calculated in the water simulation step (S50) to the input information of the sedimentation model, and the sedimentation model (S61) And an immersion simulation step (S62) for outputting sedimentation information including a sedimentation height for each point of the simulation target area by simulating the active information acquisition script and the numerical model Is an automatic prediction method of coastal erosion.

In addition, the computer connected to the Internet is loaded with an active processing program, a seawater flow model, a sedimentation model, and a wave model. In the initial input step (S12), as input information of the seawater flow model, The initial flow information of the simulated object area is input as the initial condition, and the geometry information and the soil information composed of the lattice information and the terrain information of the simulated object area are inputted as the input information of the sedimentation model, The geomorphological information composed of the lattice information and the terrain information of the simulated object area is input as the input information of the model, and in the water simulation step (S50), based on the input information in which the typhoon information is added in the prediction start step (S41) A flow simulating step (S51) of calculating predicted flow information at a time point after a unit time interval at a present time by simulating the sea water flow in the simulation target area, (S52) in which predictive flow information calculated in the simulation step (S51) is input to the wave model as initial state information, and the wave model simulates wave generation of the simulated object region to calculate predictive wave information, The flow simulating step S51 and the blue simulating step S52 are repeated for each time unit time interval reaching the preset future time and the flow simulation step S51 is performed for the predictive wave information calculated in the simulation step S52. And the input information is repeatedly updated. The present invention is a coast erosion automatic prediction method using an active information collection script and a numerical model.

Through the present invention, it is possible to apply a combination of tidal and typhoon influences in the numerical simulation of the coastal erosion phenomenon caused by a typhoon, thereby making it possible to precisely and quickly predict coastal erosion, thereby enhancing the effect of coastal erosion warning .

Especially, it can accomplish automation of coastal erosion warning by performing overall operation of numerical model such as input information collection, input information update, operation condition change through script based active processing program, It is possible to improve the accuracy, thereby improving the effectiveness of coastal erosion warning and reducing the damage caused by coastal erosion.

In addition, in the operation of the numerical model of seawater flow, real time seawater flow simulations by tide are carried out at all times regardless of whether the typhoon information is reflected or not, so that the seawater flow state accurately reflecting the tide phenomenon is predicted, It is possible to dramatically improve the accuracy of seawater flow simulations and thereby improve the accuracy of coastal erosion prediction.

1 is a flow chart
FIG. 2 is a diagram illustrating an example of a web page of a typhoon warning agency
3 is a graphical illustration of a seawater flow simulation finite element mesh of the present invention
FIG. 4 is a diagram illustrating an example of the geographical information
FIG. 5 is a block diagram of the disclosure of the present invention
6 is an illustration of an acute sediment simulated water area and a finite difference lattice network of the present invention
FIG. 7 is a schematic view of the finite element mesh network and topographic information of the present invention.
8 is a diagram illustrating an example of output information of the present invention
FIG. 9 is a flowchart of the blue model hybrid embodiment of the present invention
FIG. 10 is a diagram illustrating a simulation result according to the blue model composite embodiment of the present invention

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings.

As shown in FIG. 1, which is a flow chart showing the overall process of the present invention, an active processing program is a program for executing an active processing program, And starts an initial connection step (S11) in which an active processing program is accessed to the typhoon warning organ web page and the typhoon information is read and stored in the storage device.

An active processing program constructed with scripts such as Perl or Python collects typhoon information by accessing a web page operated by a typhoon warning agency for the purpose of disclosing typhoon information, And the input data of the seawater flow model to be described later is established based on the stored typhoon information and the input data of the sedimentation model to be described later is set based on the output data of the seawater flow model.

In addition, the active processing program is an application program operating under the operating system of the computer, and reads the actual time from the real time clock of the operating system or the computer, or, as described above, Since it is performed by computer, real time can be obtained by connecting to various real-time servers. By operating the active processing program on a real time basis, it is possible to perform numerical simulation for predicting a typhoon effect, It can be processed on a real time basis.

Particularly, in performing a series of processes of collecting typhoon information from a typhoon warning organ web page through a real-time based active processing program and storing the information in a storage device of a computer, it is possible to periodically access a typhoon warning agency web page, Information can be collected and the collected typhoon information can be given time-wise information, so that typhoon information can be systematically recorded in the computer's memory device as time series information.

Typhoon warning systems that provide typhoon information through web pages include the Korea Meteorological Administration and the Joint Typhoon Warning Center (JTWC). These typhoon warning agencies are weather satellite, weather transmitter and ground Based on the weather information collected from observation facilities, the typhoon information at that time and the typhoon information at the future time will be predicted and posted on the web page.

The webpage on which the typhoon information and predicted typhoon information of the typhoon warning agency such as the Korea Meteorological Administration or the Joint Typhoon Alarm Center of the United States is published is not limited to the uniform resource locator (URL) and the format of the typhoon information Therefore, when the connection target web page and the collection target information format are set in the construction of the active processing program, the active processing program can automatically access the corresponding web page without any user operation, and the required information can be automatically read .

FIG. 2 is an example of a web page of the US Joint Typhoon Alert Center as an example of this typhoon information placement web page. As shown in the figure, the first row shows the date and time of the forecast, The name of the typhoon and the order of the prediction are described. In the eleventh row, the typhoon location at that time is described. In the 16th to 29th rows, the maximum wind speed and the quadrant radius are described After the 33rd line, the predicted typhoon location, maximum wind speed, and radius of quadrant are described.

In the web page of the US Joint Typhoon Alert Center as illustrated in FIG. 2, it can be seen that the center position of the typhoon is 132.2 degrees east longitude and 17.9 degrees north latitude, as shown in the eleventh row in the figure. It can be seen that the quadrant radius provides the north-east, south-east, south-west, and north-west radii of the wind speed of 64 knots (kt), 50 knots and 34 knots, respectively, in terms of dissociation (nm).

As shown in FIG. 1, the active processing program of the present invention is periodically connected to the web page of the typhoon warning agency to collect typhoon information. In addition, as shown in FIG. 1, Geometric information composed of lattice information and topographic information of the target area is input, initial flow information of the simulated object area is input as an initial condition, geometric information composed of lattice information and terrain information of the simulated object area as input information of the sedimentation model, The inputting step S12 is performed in which the soil information such as the particle size distribution of the soil particles of the coast and the seabed ground is inputted.

Here, it is not necessary that the simulation area of the seawater flow model coincides with the simulated area of the sedimentation model. Simulation of the seawater flow is performed in a relatively wide area It is necessary to carry out simulations of the sedimentation phenomenon concentrated on the coastal area, which is advantageous for the efficient utilization of the computational resources and securing the simulation precision, so that it is possible to set a relatively local simulated target area.

The sea water flow model is a numerical model simulating sea water flow due to oceanic weather phenomena such as tides and typhoons. The ADCIRC model (Finite-Volume Coastal Ocean Model) and the FVCOM model (oceanic coastal and estuarine water) And the like.

The ADCIRC model and the FVCOM model, which are finite and finite volume models, are numerical models constructed by discretizing the shallow water equations. The input and output data have different formats, but the depth and depth coefficients , The tidal harmonic constant of the open boundary and the typhoon information, ie, the typhoon's center position, the maximum wind speed, the quadrant radius and the atmospheric pressure according to the wind speed step are input, and the seawater flow according to the tide and sea weather phenomenon is simulated, And calculates the tide and flow velocity of each of the nodal points.

FIG. 3 illustrates an input finite element mesh network when an ADCIRC model is applied to a sea water flow model, and FIG. 4 illustrates an undersea topography input in the same embodiment in the form of an equi-depth line.

In the operation of the seawater flow model such as the ADCIRC model, simulated target area geometry information such as shoreline and seabed topography that affect physical behavior includes lattice information including structure and form of lattice network and depth of water of nodes of lattice network As shown in FIG.

In the initial input step S12, the initial flow information of the simulation object area is input together with the geometry information. Here, the initial flow information means a simulated object water flow state at the start of the simulation, The flow rate can be set.

In addition, the initial flow information may include stress acting by the wave or the like in addition to the water level and the flow velocity, and thus, the radiation stress formed by the wave as the initial flow information of the seawater flow model, The input of the surplus stress serves as a mediator between the exchange of information between the sea water flow model and the wave model, and the combined operation of both models in the wave model complex embodiment described later.

In the initial input step S12, the open boundary conditions of the openings on the ocean side as shown by dotted lines in FIG. 3 are also input. The open boundary conditions include the global earth tide model MAREE OCEANIQUE FES 2004 The amplitude and the phase angle of the main eight-tones extracted from the main eight-

When the initial input step (S12) is completed, the road, sea water flow model simulating the region of the time of the unit time (δt) intervals up to a preset future time (t _n) to simulate the water flow from the present time as in the first ( t) calculating base flow information and storing it in a storage device of a computer, and periodically calculating future flow information by periodically changing the future time as the actual time (t _r ) elapses, (S20) is performed.

As in the dictionary meaning, the baseline simulation step S20 is not a simulation corresponding to a specific event, but continuously simulation of a normal sea water flow state. It is expected that an invasion of a typhoon is expected, It does not simulate the effect based on the information, but it means that the meteorological phenomenon such as typhoon is excluded and the sea water flow by tide is continuously simulated.

That is, in the baseline simulation step S20, the seawater flow model simulates the tide using the geometry information and the initial flow information established in the initial input step S12 described above. However, once the typhoon information is excluded, It is only constantly simulating the flow and storing the results periodically in the computer's memory.

Circulation simulation of such a base simulation step (S20) That is, the tidal simulations that is, the predetermined future time (t _n) per unit time (δt) interval of time (t) by water flow leading to the state from the present time as shown in Figure 1 , Tide and flow rate, and the calculated sea water flow state is stored in the computer's storage device as calculated flow information.

The series of computation processes constituting the present invention, such as the initial input step S12 and the base simulation step S20 actually driving the seawater flow model, are controlled by the active processing program. As described above, the active processing program is operated chege, it is possible to obtain a real time from the real-time service server in conjunction with a real-time clock or an Internet computer, the active processing program end tidal simulation program in real time (t _r) a base simulation step (S20) with the passage of time (T _n ) of the future tide phenomenon during the predetermined period of time is continuously established and stored.

For example, if the future time t _n in the baseline simulation step S20 is set to 7 days later, the unit time? T is set to 1 hour, and the time t _{i at} which the simulation is started is set at noon 1, if the noon arrives every day, the seawater flow model simulates the tide from the day to the time point after 7 days according to the control of the active processing program, and calculates the tide and flow rate by the nodal point The tide and flow rate of 1 hour intervals are stored in the memory of the computer. When the next noon arrives, the tide simulation is repeated for 7 days in the same manner as described above. Calculated flow information such as tide and flow rate is always provided.

Thus, as the tide simulation for the next 7 days is repeated every day, the calculated flow information for seven days from the day is updated every day instead of seven days in which the future time t _n is completed, It is possible to calculate flow information.

At the present time is always provided in the basal simulation step (S20) setting per unit time calculated flow information up to the future time (t _n) is utilized as the initial flow information to the actual typhoon information added in the prediction starting step (S41) to be described later simulated And as described above, it is possible to generate a more precise and precise simulation by previously calculating the calculated flow information at an arbitrary time in the future, and immediately applying the calculated flow information to the execution of simulation in consideration of actual typhoon.

As described above, while the base simulation step S20 is continuously executed, the acquisition and analysis of the typhoon information by the active processing program is also continuously performed. As shown in FIG. 1, the collection and analysis of typhoon information, The series of operations of the active processing program for determining whether or not the model is applied is composed of an iterative connection step S31 and an overlap determination step S32, which are repeated after the initial connection step S11.

First, the active processing program accesses the web page of the typhoon warning agency, reads the typhoon information, compares the information with the typhoon information stored in the storage device, and repeats the step S31 of periodically repeating the reading and comparison of the typhoon information .

In the execution of the repeated access step S31, the active processing program periodically accesses the web page of the typhoon warning agency as shown in Fig. 2 based on the actual time, and reads the typhoon information. , The webpage of the typhoon warning organization includes information for identifying the typhoon information such as the time of day or the serial number, so that it can be confirmed whether the typhoon information is updated or not.

In other words, the typhoon information provided through the web page of the typhoon warning organization is continuously updated according to the accumulation of the observation data and the analysis data continuing after the typhoon occurrence, and the typhoon information agency updated the typhoon information on the web page , It is possible to distinguish between the typhoon information and the typhoon information that has already been posted by specifying the time of publication and giving the serial number.

The typhoon information providing web page illustrated in Fig. 2 displays the corresponding time of the typhoon information in the first row and the serial number of the typhoon information article in the end of the fifth row, The program reads this time and serial number to check whether the typhoon information is updated or not by comparing it with the time and serial number recorded in the computer's storage device.

Thus, through the iterative connection step (S31) in which the active processing program periodically repeats reading and comparing the typhoon information, the typhoon information which is intermittently given once is given time-wise information and is systematically constructed can do.

When the typhoon information is changed with the execution of the iterative connection step S31, the active processing program stores the changed typhoon information in the storage device of the computer, extracts the path information among the read typhoon information, The overlap determining step S32 is performed.

That is, when the typhoon information collected in the repeated connection step S31 is different from the typhoon information collected at the previous connection, the active processing program extracts the path information, which is the center position of the typhoon, , And compares this with a predetermined start area to determine whether or not to overlap.

As described above, the typhoon information displayed on the web page of the typhoon warning agency is predicted and provided not only for the typhoon location at that time, but also for the change of the position in accordance with the movement of the typhoon. On the agency webpage, predicted typhoon information of 24 hours, 36 hours, 48 hours, 72 hours, and 96 hours are displayed on the 33rd line, with predicted typhoon information after 12 hours. As shown in FIG. 5, when the predicted traveling path of the typhoon overlaps with the starting region indicated by the dotted line in the drawing, a significant change in the sea water flow state due to the influence of the typhoon occurs It is regarded as being predicted, and the predictive starting steps S41 to S55, which will be described later, are started.

The start area is a virtual area set in the simulation target sea area as indicated by a dotted line in Fig. 5, and in the embodiment illustrated in the figure, the horizontal line and the slanting line are combined to form a border line. The setting can be set in consideration of the frequency of typhoon influences by the location of the typhoon that has passed the area in the past.

In the execution of the superposition determination step S32, overlapping of the typhoon route and the start area does not simply mean that the typhoon location is present in the start area on the predicted typhoon information, The predicted typhoon route is established by connecting the central position of the typhoon by the predicted time, and when a part of the route is located within the start area, it is determined that the typhoon route and the start area are overlapped.

5, the center position of the typhoon in the predicted typhoon information directly enters into the start area indicated by the dotted line in the figure, as shown in the right diagram of FIG. 5, In the case where the path connecting the center position of the typhoon is overlapped with a part of the start area even though the start area does not overlap with the start area.

The typhoon information used in the overlap determination step S32 is the predicted value provided from the typhoon warning agency as described above. Particularly, the overlap determination step S32 is a part of the process of embarking on the actual simulation considering the typhoon effect to be described later , Predicted typhoon information is used instead of actual typhoon information, and predicted typhoon information is used only for a relatively long time in the predicted typhoon information. Therefore, rather than merely comparing the predicted position of the typhoon and the start region, By comparing this with the start area, it is possible to prepare against the uncertainty of the hurricane movement path and to prevent the actual start of the simulation start due to the prediction error.

When the typhoon route overlaps with the start area in the overlap determination step S32, the active processing program adds the corresponding typhoon information to the input information of the seawater flow model, and performs the base simulation step S20 (Step S41) is performed in which the calculated flow information at the corresponding point in time is calculated.

That is, as shown in FIG. 1, the active processing program calculates the input information of the seawater flow model based on the center position of the typhoon, the maximum wind speed, the wind velocity of 64 knots, 50 knots and 34 knots, The radar, the peripheral air pressure, and the central air pressure, and adds the calculated flow information at the time corresponding to the start time of the prediction start step (S41) among the calculated flow information at the time calculated in the base simulation step (S20) And inputs it as the initial flow information in the sea water flow simulations to be performed later.

When the prediction start step (S41) for establishing the input data of the sea water flow model for performing the sea water flow simulation considering the influence of the actual typhoon is completed, the sea water flow model is simulated based on the added input information and the initial flow information simulate the water flow of the region to calculate a preset future time (t _n) per unit time (δt) interval of time (t) by predicting the flow information ranging from the present time and water simulation and storing in the storage device of the computer (S50 ) Is performed.

The information output from the oceanic flow model, such as the ADCIRC model, is tidal, velocity, air pressure, and wind velocity at the nodes, and is the time of the unit time (δt) interval in the execution of the ocean flow model during the water simulation step (S50) the resultant value of the tide or the wind speed calculated for each t (t) is used as an input value for the later calculation time (t + δt) along with the typhoon information.

When the water simulation step S50 is completed, a flow input step S61 is performed in which the active processing program adds the predicted flow information calculated in the water simulation step S50 to the input information of the sedimentation model.

As described above, the simulation area of the sedimentation model can be locally set to a specific point requiring prediction of the sedimentation phenomenon, as compared with the simulated area of the seawater flow model. As shown in FIG. 6, Area can be set for a very limited area.

FIG. 6 shows an acute sedimentation area lattice network assuming the case where the Xbeach model is applied. In addition to the Xbeach model, the ECOMsed model can be used as the sedimentation model. As can be seen from the figure, The model is a finite difference model and can not share a mesh network when the ADCIRC model, a finite element model, is applied as the seawater flow model as in the embodiment of FIG.

Also, as shown in the upper drawing of FIG. 7, which extracts the sedimentation simulation region in the finite element mesh of the seawater flow model, since the mesh networks of the seawater flow model and the sedimentation model do not coincide with each other, Therefore, it is not possible to perform simple substitution. Therefore, the predicted flow information of the nodes calculated by the seawater flow model, that is, the flow velocity and the water level information, is interpolated considering the spatial position of the seawater flow model grid node and the sedimentation model grid network node And is input at each nodal point of the sedimentation model grid network.

Meanwhile, in inputting the topographic information among the geometry information of the sedimentation model in the initial input step S12 described above, the terrain information input to the seawater flow model is extracted and interpolated as shown in the lower drawing of FIG. 7, Can be input for each lattice network node.

When the flow input step (S61) is completed, a sedimentation simulation step (S62) is performed in which the sedimentation model simulates the sedimentation phenomenon of the simulated object area and outputs the sedimentation information including the sedimentation height at each point of the simulated object area, Such sediment information can be output as shown in FIG. 8 through a postprocessing process of converting the submergence into a graph or a contour line.

Through the sedimentation information illustrated in FIG. 8, it can be seen that abrupt erosion of at least 1 m occurs at two points in the sedimentation simulated area, so that the dangerous area due to short-term coast erosion can be detected quickly.

Thus, when the coast erosion simulation considering the typhoon effect is completed through the immersion simulating step (S62), the active processing program analyzes the calculated sediment information to determine whether or not erosion exceeding the reference value has occurred, As described above, the present invention can be implemented through a computer connected to the Internet, so that a web page constructed on the Internet can be displayed on the Internet. So that an unspecified number of users can view the alarm information.

Meanwhile, FIG. 9 is a flow chart of a combined wave model in which a wave model is combined with an ocean flow model such as the ADCIRC model as described above to apply a simulation to a typhoon effect simulation, In the flow simulation, a wave model such as an unstructured Simulation Waver Near Shore (unSWAN) model, which can simulate a relatively short-period wave model and an ocean flow model simulating a relatively short period wave, .

That is, as shown in FIG. 10, simulation of the ocean flow model, which can be represented by long-term elevation of the sea level indicated by the one-dot chain line in the drawing, and simulations of the wave model that can be expressed by the virtual line and solid line, By performing the combined operation, it is possible to obtain a result that is more accurate and conforms to the actual phenomenon.

In particular, the wave model can simulate the wave behavior such as the wave which can not be simulated by the ocean flow model, and the phenomenon such as the wave can have an important influence on the prediction of the short term coast erosion, It is possible to enhance the predictive efficiency of coastal erosion prediction according to the present invention.

Wave models applied to the wave model composite embodiment of the present invention include unSWAN or STWAVE (STeady State Spectral Wave). These wave models are different in terms of the input / output data format, but wave action balance Equation (2) is a numerical model based on the ocean flow information such as flow velocity, and simulates the wave phenomenon in the sea area, so that the wave stress and the radiation stress formed by waves are calculated.

However, the unSWAN model is a finite element model which is easy to construct a spatial non-structural lattice network, while STWAVE is a finite difference model. As described above, the ocean flow model applied to the present invention is a non-structural lattice network model such as ADCIRC model , It is advantageous to apply the unSWAN model, which can share the spatial grid network.

As shown in FIG. 7, the hybrid model of the present invention is a web model connected to the Internet and equipped with an active processing program, a seawater flow model, and a sedimentation model, In the initial input step (S12), geometry information composed of lattice information and topographic information of the simulated object area is input as input information of the seawater flow model, initial flow information of the simulated object area is input as an initial condition, And the geometry information composed of the lattice information and the terrain information of the simulated object area is inputted as the input information of the wave model in the initial input step S12.

As described above, when the unSWAN model, which is a finite element model, is applied as the wave model, the grid information and the topographic information as shown in FIGS. 3 and 4 can be shared with the ADCIRC model, which is a seawater flow model, It is possible to perform information transmission by nodes quickly and accurately, and it is possible to use computer resources efficiently.

In the wave model combined embodiment of the present invention, the wave model is operated in conjunction with the seawater flow model on the assumption that the simulation of the seawater flow considering the influence of the typhoon is started. As a result, (S50), the predicted flow information such as the flow rate of the nodal points calculated from the seawater flow model is inputted into the wave model, and the wave model is operated.

That is, as shown in FIGS. 1 and 9, in the water simulation step S50, based on the input information to which the typhoon information is added in the prediction start step S41, the sea water flow model simulates the sea water flow of the simulation subject area, A flow simulation step S51 for calculating predicted flow information at a time after a unit time interval in the flow simulation step S51; to be included the blue simulation step (S52) for the simulation of blue occur yield prediction blue information, unit time up to these flow simulation step (S51) and a prospective assignor the blue simulation step (S52) the time (t _n) ( the input information of the flow simulating step S51 is repeatedly updated with the predictive wave information calculated in the simulation step S52.

The unit time? T applied in the execution of the flow simulating step S51 and the blue simulating step S52 of the wave complex combined embodiment is the unit time? T applied in the water simulation step S50 of the embodiment of FIG. ), And the unit time may be reduced in order to ensure intelligent information transmission and mock accuracy between the sea water flow model and the wave model.

The input / output information exchange between the sea water flow model and the wave model is input to the wave model in the wave simulation step (S52) and the wave simulation step (S52) by the sea water flow model calculated in the flow simulation step (S51) The surplus stress, and the wave height of the nodal points calculated by the wave model are input to the seawater flow model in the flow simulating step (S51) in the subsequent calculation step. As a result, as shown in FIG. 10, So that accurate prediction can be performed.

S11: Initial connection step
S12: Initial input step
S20: Baseline phase
S31: Repeat connection step
S32: Overlap determination step
S41: prediction start step
S50: Water simulation stage
S51: Flow simulation step
S52: blue mock step
S55: Alarm output step

Claims (2)

In coastal erosion prediction method using numerical model,
An active processing program, a seawater flow model, and a sedimentation model are installed in a computer connected to the Internet,
An initial connection step (S11) in which an active processing program is executed, an active processing program is connected to a typhoon warning organ web page, and the typhoon information is read and stored in a storage device;
As the input information of the seawater flow model, the geometry information composed of the lattice information and the terrain information of the simulated object area is input, and the initial flow information of the simulated object area is input as the initial condition. As input information of the simulated object model, And inputting the geometry information and the soil information constituted by the terrain information (S12);
The sea water flow model simulates the sea water flow in the simulated object area and calculates the computed flow information of the unit time interval from the present time to the preset future time and stores it in the memory of the computer. As the actual time elapses, A base simulation step (S20) of repeating time calculation flow information calculation and storage device storage by changing the time;
An iterative connection step (S31) in which the active processing program accesses the typhoon warning organ web page, reads the typhoon information, compares the information with the typhoon information stored in the storage device, and repeatedly reads and compares the typhoon information;
An overlap determination step (S32) of storing the changed typhoon information in the storage device of the computer when the typhoon information is changed and extracting the route information among the read typhoon information to determine whether the start area of the corresponding typhoon route is overlapped;
When the hurricane path overlaps with the start area, the active processing program adds the corresponding typhoon information to the input information of the seawater flow model, and as the initial flow information of the seawater flow model, among the computed flow information calculated by the time base simulation step (S20) A prediction starting step (S41) of applying the calculated flow information at the time point;
Based on the input information and the initial flow information added in the prediction starting step S41, the sea water flow model simulates the sea water flow in the simulated object area and calculates the predicted flow information by time at the unit time interval from the present time to the preset future time And storing it in a storage device of a computer (S50);
A flow input step (S61) in which the active processing program adds the predicted flow information calculated in the water simulation step (S50) to the input information of the sedimentation model;
(S62), in which the sedimentation model simulates the sedimentation phenomenon of the simulated subject area and outputs the sedimentation information including the sedimentation height at each point of the simulated subject area (S62). The active information collection script and the numerical model A Method for Automatic Prediction of Coastal Erosion Using.
The method according to claim 1,
Computers connected to the Internet are equipped with an active processing program, seawater flow model, sedimentation model and wave model,
In the initial input step (S12), geometry information composed of lattice information and topographic information of the simulated object area is input as input information of the seawater flow model, initial flow information of the simulated object area is input as an initial condition, The geometry information and the soil information constituted by the lattice information and the terrain information of the simulated object area are inputted and the geometry information composed of the lattice information and the terrain information of the simulated object area are inputted as the input information of the wave model;
In the water simulation step S50, the seawater flow model simulates the seawater flow of the simulation target area based on the input information to which the typhoon information is added in the prediction start step S41, and calculates the predicted flow information at the time after the unit time interval at the present time (S51);
(S52) for calculating the predictive wave information by inputting the predicted flow information calculated in the flow simulation step (S51) into the wave model as initial state information and the wave model simulating wave generation of the simulated object region;
The flow simulating step S51 and the blue simulating step S52 are repeated for each time unit time interval reaching the preset future time and the flow simulation step S51 is performed for the predictive wave information calculated in the simulation step S52. Wherein the input information is repeatedly updated. 2. The method of claim 1, wherein the input information is repeatedly updated.

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