Hysteresis severity diagnostics method using water surface slope

Abstract

The present invention provides a method for diagnosing hysteresis salience, comprising: a step of establishing a hysteresis relationship equation using the internal area of ​​a relationship curve between water level (H) and water surface slope (S), and the internal area of ​​a relationship curve between water level (H) and flow rate (Q); and a step of diagnosing the salience of hysteresis using the established hysteresis relationship equation.

Description

Technology Field

[0001] The present invention relates to a method for diagnosing the salience of hysteresis phenomena in a river using a prediction / diagnosis system, and specifically, to a method for diagnosing the salience of hysteresis phenomena using the water level and surface slope of the river. Background Technology

[0003] Most currently used flood prediction technologies forecast the timing and areas of flood inundation based on statistical or hydrological analysis models. These flood prediction technologies lack accuracy.

[0004] Figures 1(a) and 1(b) are graphs illustrating hysteresis in a river, with the correlation between cross-sectional velocity and water level shown as a solid line on the left graph of Figure 1(a), the correlation between flow rate and water level shown as a solid line on the right graph of Figure 1(a), and a graph showing measurement data from a measuring device shown in Figure 1(b).

[0005] In Fig. 1(a), U represents the cross-sectional velocity, H represents the water level, Q represents the flow rate, and the arrows represent the flow over time. That is, time proceeds in a counterclockwise direction.

[0006] According to the left graph in Fig. 1(a), the cross-sectional velocity increases over time after a situation such as heavy rain occurs, reaching a maximum cross-sectional velocity (U max It represents ), where the water level is not at its maximum, and subsequently, the cross-sectional velocity (U max As ) decreases, after some time, the maximum water level (H max It can be seen that a phenomenon of divergence occurs by representing ).

[0007] Similarly, according to the right graph in Fig. 1(a), the flow rate increases over time after a situation such as heavy rain occurs, reaching a maximum flow rate (Q max It represents ), where the water level is not at its maximum, and thereafter, the flow rate (Q max As ) decreases, after some time, the maximum water level (Hmax It can be seen that a phenomenon of divergence occurs by representing ).

[0008] Figure 1(b) shows the stage and index velocity over time.

[0009] According to Fig. 1(b), the surface flow velocity reaches its maximum around March 12, 2015, but the water level is not at its maximum at this time, and the water level reaches its maximum around March 15, 2015, about 3 days later, so it can be seen that the timing of the maximums is out of sync.

[0010] This phenomenon, in which a time difference occurs between water level, velocity, and flow rate, is called hysteresis, and it frequently occurs in actual streams, especially in medium to large rivers or streams.

[0011] Existing flood prediction methods do not account for this hysteresis phenomenon, resulting in low accuracy. Specifically, most currently used flood prediction technologies do not consider the solid line representing the actual model in Fig. 1(a), but instead assume a linear model as shown by the dotted lines A and B. Consequently, they predict the time when the maximum flow velocity or flow rate occurs as the same as the time when the maximum water level occurs, which results in low accuracy. For example, the accuracy is low because the method predicts that the water level will be highest at the time of maximum rainfall or that a flood will occur at the time of maximum flow velocity.

[0012] Furthermore, during floods, it was difficult to directly measure high flow rates, making it impossible to measure flow velocity and only allowing for water level measurement; consequently, there were cases where prediction was difficult based on such minimal data.

[0013] In addition, when predicting flood occurrences using existing analytical methods, there was also the problem that a significant amount of time was required to build systems and acquire data to model medium- and large-sized rivers or streams.

[0014] Review related patent literature.

[0015] Patent Document 1 discloses a method for predicting peak water level using time series data of peak water level, but it includes all the aforementioned problems and cannot reflect the hysteresis phenomenon, so the prediction accuracy is low.

[0016] Patent document 2 discloses a method for establishing a correlation between flow rate and water level and determining the time of occurrence of peak values, but it includes the aforementioned problems as in the aforementioned patent document, and thus cannot reflect the hysteresis phenomenon, resulting in low prediction accuracy.

[0017] Patent Document 3 discloses a method for outputting a predicted water level using a flow rate-water level curve by utilizing a remote station measurement unit that measures water level, flow rate, and flow velocity. Although a remote station measurement unit is used, only the water level can be measured, and no means to reflect hysteresis phenomena are disclosed, resulting in low prediction accuracy.

[0018] Patent documents 4 and 5 are by the inventor of the present application and predict flood level and flood occurrence time by reflecting the above-mentioned hysteresis phenomenon, but require measuring flow velocity along with water level, and also cannot diagnose the salience of the hysteresis phenomenon itself. Prior art literature

[0020] Chinese Published Patent No. 2008-10970114, Chinese Registered Patent No. 104392111, Korean Registered Patent No. 10-1134631, Korean Registered Patent No. 10-2512531, Korean Registered Patent No. 10-2512532 The problem to be solved

[0021] The present invention was devised to solve the aforementioned conventional problems and aims to provide a method that predicts flood levels and flood occurrence times using only the water level and the surface slope obtained therefrom, while reflecting the hysteresis phenomenon occurring in rivers or streams, and also diagnoses the salience of the hysteresis phenomenon itself. means of solving the problem

[0023] To achieve the above objective, the present invention provides a method for diagnosing hysteresis salience, comprising: a step of establishing a hysteresis relationship equation using the internal area of ​​a relationship curve between water level (H) and water surface slope (S), and the internal area of ​​a relationship curve between water level (H) and flow rate (Q); and a step of diagnosing the salience of hysteresis using the established hysteresis relationship equation, wherein step (a) comprises: (a1) a step in which water level (H) and flow rate (Q) are measured in real time by a plurality of measuring devices (100); (a2) a step in which a data calculation module (210) receives the water level (H) and flow rate (Q) measured in real time by a plurality of measuring devices (100) and calculates the real-time water surface slope (S) using the received water level (H); and (a3) ​​a step in which a data selection module (220) selects a peak water level (H) among the calculated water level (H), water surface slope (S), and flow rate (Q). max ), peak flow rate (Q max) and peak surface slope (S max A step of selecting when ) is confirmed; (a4) the peak surface slope (S) selected by the relational expression operation module (230). max ), peak flow rate (Q max ) and peak water level (H max(a5) a step of setting a water level / surface slope relationship curve and a water level / flow rate relationship curve using multiple of them, and non-dimensionalizing them to calculate the internal area (η', η); (a6) a step of setting a linear relationship equation using multiple of the calculated internal area (η') of the water level / surface slope relationship curve and the internal area (η) of the water level / flow rate relationship curve, and determining variables to set a hysteresis relationship equation; (a6) a step of setting the internal area (η') of the water level / surface slope relationship curve corresponding to the pre-set internal area (η) of the water level / flow rate relationship curve in the set hysteresis relationship equation as a hysteresis salience criterion (k); and the above step (b) includes, (b1) a step of measuring the water level (H) in real time at the multiple measuring devices (100); (b2) A step in which the data calculation module (210) calculates a real-time water surface slope (S) using the water level (H) measured in real-time by a plurality of measuring devices (100); (b3) A step in which the data selection module (220) selects a peak water level (H) among the water level (H) and water surface slope (S) calculated in S620 max ) and peak surface slope (S max A step of verifying ); (b4) the peak surface slope (S) selected by the relationship calculation module (230) max ) and peak water level (H max A method for diagnosing hysteresis salience is provided, comprising: a step of setting a water level / water surface slope relationship curve using ) and nondimensionalizing it to calculate an internal area (η'); (b5) a step in which a prediction / diagnosis module (240) diagnoses the salience of hysteresis by comparing the internal area (η') of the calculated water level / water surface slope relationship curve with the hysteresis salience criterion (k) set in step S560.

[0024] The above step (b) preferably further includes, after the above step (b5), a step (b6) in which the prediction / diagnosis module (240) outputs the prominence of the diagnosed hysteresis phenomenon through the alarm device (300). Effects of the invention

[0026] The method for diagnosing the salience of hysteresis according to the present invention can quantitatively diagnose the salience of hysteresis by measuring and applying the water level and water surface slope to a graph constructed in a river where a nationwide measuring device is installed.

[0027] In addition, the method according to the present invention is highly economical as it can diagnose and provide the significance of hysteresis using an already installed measuring device, thus eliminating the need for separate additional equipment. Brief explanation of the drawing

[0029] Figure 1 (a) is a graph to explain the hysteresis phenomenon in a river, and (b) is a graph showing measurement data from a measuring device for a river flood event. FIG. 2 is a schematic diagram of a prediction / diagnosis system according to the present invention. Figure 3 is a diagram illustrating a measuring device installed in a river and a prediction / diagnosis system connected thereto. Figure 4 is a figure illustrating a method for calculating the water surface slope using water levels measured by multiple measuring devices. FIG. 5 is a flowchart illustrating a flood level prediction method using a prediction / diagnosis system according to the present invention. Figure 6 is an example of time series data of water level and water surface slope obtained from the water level measured by a measuring device. Here, H is the water level, S is the water surface slope, and H max is the peak water level, S max is the peak surface slope. Figure 7 shows a time series graph of the water surface slope and water level observed on an actual river for verification. Here, Stage represents the water level, and Smoothed Slope represents the water surface slope. Figure 8 shows S according to flood events max and H max This is an example of collecting, recording, and organizing data into a table. Here, Event is the sequence number of the flood event, and S according to the Event number max and H max The value of is Smax _1 ... S max _n, H max _1 .. H max It is _n. Figure 9 is S table-formatted in Figure 8. max and H max This is a figure plotted as a graph. Fig. 10 is S of Fig. 9 max and H max This figure shows the linear regression analysis performed on the graph representing [the graph] and the relationship equation established. In the relationship equation, A is the slope of the graph and B is the y-intercept. Figure 11 shows S, which was created for verification using an actual river. max and H max This is a figure plotted as a graph as shown in Fig. 9. Fig. 12 is S of Fig. 11 max and H max This is a figure showing linear regression analysis and the relationship equation established on the graph representing it. FIG. 13 is a flowchart illustrating a method for predicting the time of flood occurrence using a prediction / diagnosis system according to the present invention. FIG. 14 is an example of time series data of water level and water surface slope obtained from the water level measured by a measuring device. Here, H is the water level, and S is the water surface slope H max is the peak water level value, S max is the peak water level slope value. t1 is the time of occurrence of the peak water level slope, t2 is the time of occurrence of the peak water level, and t2-t1 is defined as the time difference Lag. Figure 15 shows a time series graph of the water surface slope and water level observed on an actual river for verification. Here, Stage represents the water level, and Smoothed Slope represents the water surface slope. Figure 16 shows S according to flood events max This is an example of collecting and recording Lag and creating a table. Here, Event is the sequence number of the flood event, and S according to the Event number. max The values ​​of wa and Lag are S max _1 ... Smax _n, Lag_1 .. Lag_n. FIG. 17 is S table-formatted in FIG. 16 max This is a figure plotting and Lag as a graph. Fig. 18 is S of Fig. 17 max This figure shows the linear regression analysis performed on the graph representing and Lag, and the relationship equation established. In the relationship equation, A is the slope of the graph, and B is the y-intercept. Figure 19 shows S, which was created for an actual river for verification. max Figure 9 is a graph plotting and Lag. Fig. 20 is S of Fig. 19 max This is a figure showing linear regression analysis and relationship equations established on a graph representing and Lag. FIG. 21 is a flowchart illustrating a method for diagnosing the salience of hysteresis phenomena using a prediction / diagnosis system according to the present invention. Figure 22 is a figure showing the calculation of the water level / flow rate relationship curve and the water level / water level slope relationship curve using flow rate, water surface slope, and water level data measured by a measuring device. Figure 23 shows a graph in which the water level / discharge relationship curve and the water level / surface slope relationship curve obtained from time series graphs of water level and surface slope for actual river flood events are each nondimensionalized. Figure 24 shows the calculation of the internal areas η and η' by nondimensionalizing the water level / flow rate relationship curve and the water level / water level slope relationship curve calculated from the time series graphs of flow rate, water surface slope, and water level of Figure 22, respectively. Figure 25 is an example of collecting and recording η and η' for each flood event and organizing them into a table. Here, Event is the sequence number of the flood event, and the values ​​of η and η' according to the Event number are η_1 ... η_n and η'_1 ..η'_n. Figure 26 shows the linear regression analysis performed on the graphs representing η and η' and the relationship equation calculated. The relationship equation is a graph passing through the origin, and A is the slope of the graph. The criterion for the significance of hysteresis set as an example in the graph is η=0.025, and the figure sets the value η'=k, which corresponds one-to-one with η=0.025, as the criterion for the significance of hysteresis. Figure 27 is a figure showing a linear regression analysis and a relationship equation established on a graph representing η and η' for an actual river. Figure 28 is a figure showing a graph representing η and η', in which the value η'=k, which is the diagnostic criterion for hysteresis salience, is set, and a criterion table for diagnosing hysteresis salience from the set value is presented. Specific details for implementing the invention

[0030] Hereinafter, a prediction / diagnosis system according to the present invention and a method for predicting flood level, a method for predicting the time of flood occurrence, and a method for diagnosing hysteresis phenomena using the same will be described in detail with reference to the drawings. Here, the components constituting the present invention may be used as an integrated unit or separately as needed. In addition, some components may be omitted depending on the form of use. Various modifications are also possible regarding the form of the present invention and the number of components.

[0032] Description of the system

[0033] As illustrated in FIG. 2, a system in which the method according to the present invention is performed includes a measuring device (100), a prediction / diagnosis system (200), and an alarm device (300).

[0034] As shown in FIG. 3, a plurality of measuring devices (100) are installed at a predetermined distance apart along the longitudinal direction of a river or stream, and the measuring devices (100) installed in this way measure the flow rate (Q) and water level (H) of the river or stream in real time.

[0035] The prediction / diagnosis system (200) includes a data calculation module (210), a data selection module (220), a time difference calculation module (225), a relationship calculation module (230), and a prediction / diagnosis module (240) for predicting flood levels, predicting flood occurrence times, and diagnosing hysteresis phenomena.

[0036] The data calculation module (210) receives information measured in real time from a plurality of measuring devices (100) and can calculate the water surface slope (S) using this information.

[0037] The data calculation module (210) receives the water level (H) and flow rate (Q) measured in real time by a plurality of measuring devices (100), and calculates the water surface slope (S) using the input water level (H) and a predetermined distance between the plurality of measuring devices (100).

[0038] Specifically, as shown in FIG. 4, the water level (H, stage 1) measured in real time at a measuring device (100) located upstream of the data calculation module (210), the water level (H, stage 2) measured in real time at a measuring device (100) located downstream of the data calculation module (210), and the water surface slope (S, Water surface slope) is calculated using a predetermined distance (Length) between the upstream and downstream measuring devices (100).

[0039] The data selection module (220) is a peak water level (H) among the water levels (H) measured by the measuring device (100). max ) and the peak surface slope (S) among the surface slopes (S) calculated in the data calculation module (210) max If you have confirmed ), select it.

[0040] In addition, the data selection module (220) selects the peak flow rate (Q) among the flow rates (H) measured by the measuring device (100). max You can choose more ).

[0041] Here, the peak surface slope (S max ) and peak water level (H maxThe occurrence time of ) will differ. This is due to the hysteresis phenomenon mentioned in the prior art.

[0042] The time difference calculation module (225) selects the peak surface slope (S) selected in the data selection module (220). max ) and peak water level (H max Calculates multiple time differences (Lag) between ). Specifically, the selected peak water level slope (S) at the occurrence of the first flood event. max ) and this peak surface slope (S max Peak water level (H) occurring after ) max The time difference between occurrences is calculated as the time difference (Lag), and this is repeated every time a flood event occurs.

[0043] The relationship calculation module (230) uses the data selected by the data selection module (220) to set a water level / water surface slope relationship, a time difference relationship, or a hysteresis relationship.

[0044] The relationship equation operation module (230) selects the peak surface slope (S) from the data selection module (220). max ) and peak water level (H max A water level / surface slope relationship is established using multiple values, and the established water level / surface slope relationship is used to predict flood levels.

[0045] The relationship equation calculation module (230) calculates the peak water surface slope (S) calculated in the time difference calculation module (225). max ) and this peak surface slope (S max ) Occurrence and peak water level (H max A time difference relationship is established using multiple time differences (Lag) between occurrences, and the established time difference relationship is used to predict the time of flood occurrence.

[0046] Meanwhile, the relational expression operation module (230) selects the peak surface slope (S) selected in the data selection module (220). max ), peak flow rate (Q max ) and peak water level (H maxA hysteresis relationship equation and hysteresis criteria are established using multiple methods, and the established hysteresis relationship equation and hysteresis salience criteria are used for hysteresis diagnosis.

[0047] The prediction / diagnosis module (240) uses the water level / water surface slope relationship set in the relationship calculation module (230) to determine the peak water level (H max Predict ) and the predicted peak water level (H max The flood level is predicted by determining whether ) is above a preset flood level.

[0048] The preset flood level can be set as needed to a water level that exceeds the normal water level due to heavy rainfall, rather than the overflow level.

[0049] The prediction / diagnosis module (240) predicts the peak water level (H max If it is determined that the level is above a preset flood level, the alarm device (300) described later can be activated.

[0050] Additionally, the prediction / diagnosis module (240) calculates the time difference (Lag) using the time difference relationship of the relationship calculation module (230) and predicts the time of flood occurrence using this. At this time, the prediction / diagnosis module (240) will activate the alarm device (300) when the predicted time of flood occurrence arrives.

[0051] And, the prediction / diagnosis module (240) uses the hysteresis relationship of the relationship calculation module (230) to set the water level / water surface slope relationship curve and nondimensionalize it to calculate the internal area (η'), and diagnoses the hysteresis phenomenon by comparing it with the set hysteresis phenomenon salience standard, which will be described in detail later.

[0052] Likewise, the prediction / diagnosis module (240) can activate the alarm device (300) described later so that the prominence of the diagnosed hysteresis phenomenon is output.

[0053] The alarm device (300) can be operated by receiving a signal from the prediction / diagnosis module (240). The configuration of the alarm device (300) is not limited and can be configured with any known means for notifying, displaying, or outputting information.

[0054] First, in the case of the flood level prediction method, the alarm device (300) predicts the peak water level (H) predicted by the prediction / diagnosis module (240) using the water level / water surface slope relationship equation. max ) receives the signal sent when it is determined that the flood level is above the preset level.

[0055] Next, in the case of the flood occurrence time prediction method, the alarm device (300) receives a signal sent when the flood occurrence time predicted by the prediction / diagnosis module (240) based on the time difference (Lag) calculated using the time difference relationship formula arrives.

[0056] And, in the case of the hysteresis phenomenon diagnosis method, the alarm device (300) receives a signal sent to the alarm device (300) by the prediction / diagnosis module (240) regarding the prominence of the hysteresis phenomenon diagnosed using the hysteresis phenomenon relationship equation and the hysteresis phenomenon prominence criterion.

[0058] Explanation of flood level prediction methods

[0059] Hereinafter, a flood level prediction method using a prediction / diagnosis system (200) according to the present invention will be described with further reference to FIGS. 5 to 12.

[0060] As shown in Fig. 5, the flood level prediction method is the peak water surface slope (S max ) and peak water level (H max It includes a step (S100) of setting a water level / surface slope relationship equation and a step (S200) of predicting a flood using the set water level / surface slope relationship equation.

[0061] Below, peak surface slope (S max ) and peak water level (H max The step (S100) of establishing the water level / water surface slope relationship equation is explained in detail.

[0062] First, the water level (H) is measured in real time by a plurality of measuring devices (100) (S110).

[0063] As shown in FIG. 2, a plurality of measuring devices (100) installed at predetermined intervals along the length of a river or stream measure the water level (H) of the river or stream in real time. Alternatively, the water level (H) may be selected from the measured water level (H) and the flow rate (Q).

[0064] Next, the data calculation module (210) receives the water level (H) measured in real time from a plurality of measuring devices (100) and calculates the real-time water surface slope (S) using it (S120).

[0065] The data calculation module (210) receives real-time measurement information of the water level (H) required for calculation and calculates the real-time water surface slope (S) using this information.

[0066] As shown in FIG. 4, the water level slope (S) is calculated using the water level (H) measured at each measuring device (100) installed upstream and downstream and the distance between the measuring devices (100).

[0067] Next, if the data selection module (220) confirms the peak water level (Hmax) and peak water surface slope (Smax) among the calculated water level (H) and water surface slope (S), it selects them (S130).

[0068] As disclosed in FIGS. 6 and 7, the data selection module (220) draws a graph of the calculated water level (H) and water surface slope (S) to determine the peak water level (H max ) and peak surface slope (S max You can select ), and the peak surface slope (S max ) and peak water level (H max The time of ) will be different. This is because, as mentioned, a time difference occurs due to the hysteresis phenomenon.

[0069] Next, the peak surface slope (S) selected by the relational equation operation module (230) max ) and peak water level (H maxA linear relationship is established using multiple variables, and a water level / water surface slope relationship is established by finding the variables (S140).

[0070] As shown in FIGS. 9 to 12, the relational calculation module (230) calculates the peak water surface slope (S max ) and peak water level (H max ) Use multiple values ​​to establish the water level / water surface slope relationship equation through a graph.

[0071] At this time, as shown in FIG. 8, a plurality of peak water surface slopes (S) for each flood event max ) and peak water level (H max You can create a table of ) and plot a graph using the created table.

[0072] Next, the step (S200) of predicting a flood using the established water level / water surface slope relationship is described in detail.

[0073] First, the water level (H) is measured in real time by a plurality of measuring devices (100) (S210).

[0074] In order to predict the flood level using the above-mentioned water level / water surface slope relationship, the water level (H) is measured in real time from a plurality of measuring devices (100).

[0075] Likewise, among the water level (H) and flow rate (Q) measured by the measuring device (100), the water level (H) can be selected.

[0076] Next, the data calculation module (210) receives the water level (H) measured in real time from a plurality of measuring devices (100) and calculates the real-time water surface slope (S) using it (S220).

[0077] The data calculation module (210) calculates the real-time water surface slope (S) from the water level (H) measured in real-time by a plurality of measuring devices (100), and this is calculated using the distance between each measuring device (100) and the water level (H) measured by each measuring device (100) installed upstream and downstream, as described above.

[0078] Next, the data selection module (220) selects the peak surface slope (S) among the surface slopes (S) of step S220. max Check ) (S230).

[0079] The data selection module (220) selects the peak water slope (S) which is a variable to be substituted into the water level / water slope relationship equation set in step S140 among the water slopes (S) of step S220. max Check )

[0080] At this time, as shown in Fig. 7, a graph of the water level (H, stage) and the water surface slope (S, smoothed slope) is plotted to determine the peak water level (H max ) and peak surface slope (S max You can select ), and as mentioned earlier, the peak surface slope (S max ) and peak water level (H max The time of ) is different.

[0081] Next, the data selection module (220) selects the peak surface slope (S max If ) is confirmed, the prediction / diagnosis module (240) [confirms] the water level / surface slope relationship of step S140 and the confirmed peak surface slope (S max Using ) peak water level (H max Predict ) and the predicted peak water level (H max Determines whether ) is above the preset flood level.

[0082] The data selection module (220) selects the peak surface slope (S max If ) is confirmed, the prediction / diagnosis module (240) checks the peak surface slope (S) confirmed in real time max Substitute ) into the water level / surface slope relationship to obtain the real-time peak water level (H max Predicts ) (S240).

[0083] That is, the peak surface slope (S) identified in the surface slope (S) calculated from the water level (H) measured in real time by the measuring device (100). max The predicted peak water level (H) obtained by substituting ) into the water level / surface slope relationship max ) determines whether it is above a preset flood level (S245).

[0084] Predicted peak water level (H max It determines whether the level is above or below a preset flood level and activates the alarm device (300).

[0085] Here, the preset flood level is not the overflow level, but a level that is set as necessary when the water level exceeds the normal volume due to heavy rainfall.

[0086] The prediction / diagnosis module (240) predicts the peak water level (H max If the level is higher than the preset flood level, the alarm device (300) is activated (S250), and if it is lower, the process is repeated starting from step S210.

[0088] Explanation of flood occurrence time prediction methods

[0089] Hereinafter, a method for predicting the time of flood occurrence using a prediction / diagnosis system (200) according to the present invention will be explained with further reference to FIGS. 13 to 20.

[0090] The method for predicting the time of flood occurrence is the peak water surface slope (S max ) and peak water level (H max It includes a step (S300) of setting a time difference relationship of ) and a step (S400) of predicting the time of flood occurrence using the set time difference relationship.

[0091] Below, peak surface slope (S max ) and peak water level (H max The step (S300) of establishing the time difference relationship equation of ) is explained in detail.

[0092] First, the water level (H) is measured in real time by a plurality of measuring devices (100) (S310).

[0093] A plurality of measuring devices (100) installed at predetermined intervals along the longitudinal direction of a river or stream measure the water level (H) of the river or stream in real time. Alternatively, the water level (H) may be selected from among the measured water level (H) and the flow rate (Q).

[0094] Next, the data calculation module (210) receives the water level (H) measured in real time from a plurality of measuring devices (100) and calculates the real-time water surface slope (S) using it (S320).

[0095] The data calculation module (210) receives real-time measurement information of the water level (H) required for calculation and calculates the real-time water surface slope (S) using this information.

[0096] As shown in FIG. 4, the water level slope (S) is calculated using the water level (H) measured at each measuring device (100) installed upstream and downstream and the distance between the measuring devices (100).

[0097] Next, among the calculated water level (H) and water surface slope (S) by the data selection module (220), the peak water level (H max ) and peak surface slope (S max If ) is confirmed, select it (S330).

[0098] As shown in FIGS. 14 and 15, the data selection module (220) draws a graph of the calculated water level (H) and water surface slope (S) to determine the peak water level (H max ) and peak surface slope (S max You can select ), and the peak surface slope (S max The time (t1) when ) occurs and the peak water level (H max The time (t2) at which ) occurs is different, and the peak surface slope (S max The time (t1) when ) occurs and the peak water level (H max The difference in the time (t2) when ) occurs becomes the time difference (Lag).

[0099] Next, the time difference calculation module (225) selects the peak surface slope (S max ) and peak water level (H max Calculate the time difference (Lag) corresponding to the majority (S340).

[0100] The time difference calculation module (225) uses the graph as described above to calculate the peak water surface slope (S max The time of occurrence of ), and the peak water level (H thereafter maxCalculate multiple time differences (Lag), which are the time differences (t2-t1) between the occurrence times of ).

[0101] Next, the peak surface slope (S) calculated by the relational equation calculation module (230) max A linear relationship is established using multiple ) and time difference (Lag), and a time difference relationship is established by finding the variable (S350).

[0102] As disclosed in FIGS. 17 to 20, the relational calculation module (230) calculates the peak surface slope (S max ) and multiple time differences (Lag) are used to establish a time difference relationship equation through a graph.

[0103] At this time, as shown in FIG. 16, a plurality of peak water surface slopes (S) for each flood event max ) and time difference (Lag) can be tabled, and a graph can be plotted using the table.

[0104] Next, the step (S200) of predicting the time of flood occurrence using the established time difference relationship is described in detail.

[0105] First, the water level (H) is measured in real time by a plurality of measuring devices (100) (S410).

[0106] In order to predict the flood level using the above-mentioned time difference relationship, the water level (H) is measured in real time from a plurality of measuring devices (100).

[0107] Likewise, among the water level (H) and flow rate (Q) measured by the measuring device (100), the water level (H) can be selected.

[0108] Next, the data calculation module (210) receives the water level (H) measured in real time from a plurality of measuring devices (100) and calculates the real-time water surface slope (S) using it (S420).

[0109] The data calculation module (210) calculates the real-time water surface slope (S) from the water level (H) measured in real-time by a plurality of measuring devices (100), and this is calculated using the distance between each measuring device (100) and the water level (H) measured by each measuring device (100) installed upstream and downstream, as described above.

[0110] Next, the data selection module (220) selects the peak surface slope (S) among the surface slopes (S) of step S420. max Check ) (S430).

[0111] The data selection module (220) selects the peak water slope (S) which is a variable to be substituted into the time difference relationship set in step S350 among the water slopes (S) of step S420. max Check )

[0112] At this time, as shown in FIG. 15, a graph of the water level (H, stage) and surface slope (S, smoothed slope) is plotted to determine the peak surface slope (S max You can select ), and as mentioned earlier, the peak surface slope (S max ) and peak water level (H max The time of ) is different.

[0113] Next, the data selection module (220) selects the peak surface slope (S max If ) is confirmed, the prediction / diagnosis module (240) checks the peak surface slope (S) confirmed in real time max Substituting ) into the time difference relationship, the real-time peak water level (H max Predicts ) (S440).

[0114] The data selection module (220) selects the peak surface slope (S max When ) is confirmed, the prediction / diagnosis module (240) calculates the time difference (Lag) using the time difference relationship of step S330, and predicts the flood occurrence time by adding the calculated time difference (Lag) to the occurrence time of the confirmed peak water surface slope (Smax).

[0115] Next, when the predicted flood occurrence time arrives, the prediction / diagnosis module (240) activates the alarm device (300) to display it (S440).

[0117] Explanation of Hysteresis Diagnosis Methods

[0118] Hereinafter, a method for diagnosing hysteresis phenomena using a prediction / diagnosis system (200) according to the present invention will be described with further reference to FIGS. 21 to 28.

[0119] A method for diagnosing hysteresis includes the step (S500) of establishing a hysteresis relationship equation using the internal area of ​​the relationship curve between water level (H) and water surface slope (S), and the internal area of ​​the relationship curve between water level (H) and flow rate (Q); and the step of diagnosing the salience of the hysteresis phenomenon using the established hysteresis relationship equation.

[0120] Hereinafter, the step (S500) of establishing a hysteresis relationship equation using the internal area of ​​the relationship curve between water level (H) and water surface slope (S), and the internal area of ​​the relationship curve between water level (H) and flow rate (Q) is explained in detail.

[0121] The water level (H) and flow rate (Q) are measured in real time at multiple measuring devices (100) (S510).

[0122] A plurality of measuring devices (100) installed at predetermined intervals along the length of a river or stream measure the water level (H) and flow rate (Q) of the river or stream in real time.

[0123] Next, the data calculation module (210) receives the water level (H) and flow rate (Q) measured in real time from the plurality of measuring devices (100), and calculates the real-time water surface slope (S) using the received water level (H) (S520).

[0124] The data calculation module (210) calculates the real-time water surface slope (S) using the water level (H) among the water level (H) and flow rate (Q) that are measured and input in real-time from a plurality of measuring devices (100).

[0125] As shown in FIG. 4, the water level slope (S) is calculated using the water level (H) measured at each measuring device (100) installed upstream and downstream and the distance between the measuring devices (100).

[0126] Next, the data selection module (220) selects the peak water level (H) among the calculated water level (H), water surface slope (S), and flow rate (Q). max ), peak flow rate (Q max) and peak surface slope (S max If ) is confirmed, select it (S530).

[0127] As shown in the upper part of FIGS. 22 and 23, the data selection module (220) draws a graph of the calculated water level (H) and water surface slope (S) to determine the peak water level (H max ), peak flow rate (Q max ) and peak surface slope (S max You can check ).

[0128] Next, the peak surface slope (S) selected by the relational equation operation module (230) max ), peak flow rate (Q max ) and peak water level (H max Using multiple values, the water level / water surface slope relationship curve and the water level / flow rate relationship curve are each set and nondimensionalized to calculate the internal area (S540).

[0129] As shown in the lower part of FIGS. 22 and 23, the relational calculation module (230) selects a plurality of peak surface slopes (S max ), peak flow rate (Q max ) and peak water level (H max Using ), the stage / surface slope relationship curve and the stage / discharge relationship curve are set, respectively.

[0130] And, as shown in Fig. 24, the water level / water surface slope relationship curve and the water level / flow rate relationship curve are nondimensionalized.

[0131] The relationship calculation module (230) calculates the water level / water surface slope relationship curve and the water level / flow rate relationship curve for each flood event, and converts each into a dimensionless graph.

[0132] The dimensionless process is the peak water level gradient (S) for each flood event. max ), peak flow rate (Q max ) and peak water level (H max Nondimensionalize based on ) so that the peak value becomes 1.

[0133] And, the interior area of ​​the dimensionless water level / flow rate relationship curve is defined as η, and the interior area of ​​the water level / water surface slope relationship curve is defined as η'.

[0134] Next, a linear relationship equation is established using multiple internal areas (η') of the calculated water level / water surface slope relationship curve and internal areas (η) of the water level / flow rate relationship curve, and a hysteresis relationship equation is established by finding variables (S550).

[0135] As shown in FIGS. 26 and 27, the relationship calculation module (230) establishes a hysteresis relationship equation through a graph using multiple internal areas (η') of the calculated water level / water surface slope relationship curve and internal areas (η) of the water level / flow rate relationship curve.

[0136] At this time, as shown in FIG. 25, a table can be prepared of the internal area (η') of the water level / water surface slope relationship curve and the internal area (η) of the water level / flow rate relationship curve for each flood event, and a graph can be plotted using the prepared table.

[0137] Next, the internal area (η) of the water level / flow rate relationship curve and the corresponding internal area (η') of the water level / water surface slope relationship curve in the hysteresis relationship equation are set as the hysteresis salience criterion (k) (S560).

[0138] The relationship equation calculation module (230) sets the inner area (η') of the water level / water surface slope relationship curve that corresponds one-to-one with the boundary of the inner area (η) of the water level / flow rate relationship curve as a criterion for diagnosing the salience of the hysteresis phenomenon in the hysteresis phenomenon relationship equation set in S550.

[0139] The value of the internal area (η') of the set water level / water surface slope relationship curve serves as the criterion value for the salience of the hysteresis phenomenon. Accordingly, in the S600 step described later, the salience of the hysteresis phenomenon can be determined by calculating only the internal area (η') of the water level / water surface slope relationship curve.

[0140] Below, a step (S600) for diagnosing the salience of a hysteresis phenomenon using a set hysteresis relationship equation is described in detail.

[0141] First, the water level (H) is measured in real time by a plurality of measuring devices (100) (S610).

[0142] In order to predict the hysteresis phenomenon using the above-mentioned hysteresis relationship, the water level (H) is measured in real time from a plurality of measuring devices (100).

[0143] At this time, the water level (H) can be selected from the water level (H) and flow rate (Q) measured by the measuring device (100).

[0144] Next, the data calculation module (210) receives the water level (H) measured in real time from the plurality of measuring devices (100) and calculates the real-time water surface slope (S) using the received water level (H) (S620).

[0145] The data calculation module (210) calculates the real-time water surface slope (S) from the water level (H) measured in real-time by a plurality of measuring devices (100), and this is calculated using the distance between each measuring device (100) and the water level (H) measured by each measuring device (100) installed upstream and downstream, as described above.

[0146] Next, the data selection module (220) selects the peak water level (H) among the calculated water level (H) and water surface slope (S). max) and peak surface slope (S max Check ) (S630).

[0147] The data selection module (220) checks the peak water slope (Smax), which is a variable to be substituted into the time difference relationship set in step S350, among the water slopes (S) of step S420.

[0148] At this time, as shown in the upper part of FIG. 23, a graph of the water level (H, stage) and surface slope (S, smoothed slope) is drawn to determine the peak surface slope (S max You can select ).

[0149] Next, the relational calculation module (230) calculates the selected peak water surface slope (S max ) and peak water level (H max A water level / water surface slope relationship curve is established using ) and nondimensionalized to calculate the internal area (S640).

[0150] As shown in the lower part of FIG. 23, the relational calculation module (230) selects the peak water surface slope (S max ) and peak water level (H max Set the water level / surface slope relationship curve using ) and nondimensionalize it to calculate the interior area (η') of the water level / surface slope relationship curve.

[0151] Next, the prediction / diagnosis module (240) diagnoses the salience of the hysteresis phenomenon by comparing the preset hysteresis phenomenon salience criterion (k) with the internal area (η') of the calculated water level / water surface slope relationship curve (S650).

[0152] As shown in FIG. 28, if the hysteresis phenomenon salience criterion is set to 0.07 in step S560, the prediction / diagnosis module (240) diagnoses the salience of the hysteresis phenomenon by comparing the internal area (η') of the water level / water surface slope relationship curve calculated in step S640 with the set hysteresis phenomenon salience criterion.

[0153] The prediction / diagnosis module (240) can diagnose that if the internal area (η') of the calculated water level / water surface slope relationship curve is less than 0.07, it is non-severe, and if it is 0.07 or more, it is severe.

[0154] Next, the prominence of the hysteresis phenomenon is output through the alarm device (300).

[0155] The prediction / diagnosis module (240) outputs the prominence of the hysteresis phenomenon diagnosed in step S650 through the alarm device (300).

[0157] The method according to the present invention has the effect of analyzing the relationship between the water level and the peak water level of a river to construct a graph, and through this, diagnosing the flood level, the time of flood occurrence, and the salience of the hysteresis phenomenon for a river that reflects the hysteresis phenomenon, thereby enabling preparation.

[0158] In addition, by measuring only the water level without utilizing the actual river flow velocity and using the resulting surface slope with pre-established graphs and equations to predict flood levels and flood occurrence times, as well as diagnose hysteresis phenomena, installation costs can be reduced.

[0160] For the time being, the present specification has been described with reference to embodiments illustrated in the drawings so that those skilled in the art can easily understand and reproduce the present invention; however, this is merely illustrative, and those skilled in the art will understand that various modifications and equivalent alternative embodiments are possible from the embodiments of the present invention. Accordingly, the scope of protection of the present invention should be determined by the claims. Explanation of the symbols

[0162] 100: Measuring device 200: Prediction / Diagnosis System 210: Data Operation Module 220: Data Selection Module 230: Relational expression operation module 240: Prediction / Diagnosis Module 300: Alarm device

Claims

Claim 1 (a) a step of establishing a hysteresis relationship equation using the internal area of ​​the relationship curve between water level (H) and water surface slope (S), and the internal area of ​​the relationship curve between water level (H) and flow rate (Q); and (b) a step of diagnosing the salience of the hysteresis phenomenon using the established hysteresis relationship equation; wherein step (a) comprises: (a1) a step of measuring the water level (H) and flow rate (Q) in real time at a plurality of measuring devices (100); (a2) a step in which a data calculation module (210) receives the water level (H) and flow rate (Q) measured in real time at a plurality of measuring devices (100) and calculates the real-time water surface slope (S) using the received water level (H); (a3) ​​a step in which a data selection module (220) selects the peak water level (H) among the calculated water level (H), water surface slope (S), and flow rate (Q). max ), peak flow rate (Q max) and peak surface slope (S max A step of selecting when ) is confirmed; (a4) the selected peak surface slope (S) in the relational expression operation module (230). max ), peak flow rate (Q max ) and peak water level (H max A step of setting and nondimensionalizing a water level / surface slope relationship curve and a water level / flow rate relationship curve using a plurality of methods, and calculating the internal area (η', η) of the nondimensionalized water level / surface slope relationship curve and the water level / flow rate relationship curve; (a5) a step of establishing a hysteresis relationship equation by establishing a linear relationship equation using multiple internal areas (η') of the calculated water level / water surface slope relationship curve and multiple internal areas (η) of the water level / flow rate relationship curve and by finding variables; (a6) a step of establishing the internal area (η') of the water level / water surface slope relationship curve corresponding to the internal area (η) of the pre-set water level / flow rate relationship curve in the established hysteresis relationship equation as a hysteresis salience criterion (k); wherein the above step (b) includes: (b1) a step of measuring the water level (H) in real time at the multiple measuring devices (100); (b2) a step in which the data calculation module (210) calculates the real-time water surface slope (S) using the water level (H) measured in real time at the multiple measuring devices (100); (b3) Among the water level (H) and water surface slope (S) calculated by the data calculation module (210), the data selection module (220) selects the peak water level (H max ) and peak surface slope (S max Step (b4) of verifying ) The peak surface slope (S) selected by the relationship calculation module (230) max ) and peak water level (H max A method for diagnosing hysteresis salience, comprising: a step (b5) of setting and nondimensionalizing a water level / water surface slope relationship curve using ), and calculating the internal area (η') of the nondimensionalized water level / water surface slope relationship curve; and a step (240) of diagnosing hysteresis salience by comparing the internal area (η') of the calculated water level / water surface slope relationship curve with the set hysteresis salience criterion (k). Claim 2 A method for diagnosing hysteresis phenomenon prominence according to claim 1, wherein step (b) further comprises, after step (b5), (b6) the prediction / diagnosis module (240) outputs the prominence of the diagnosed hysteresis phenomenon through an alarm device (300).

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