JP2020085441A - Program, method and information processor - Google Patents

Abstract

To predict properly the water-level of a river according to rain-fall conditions.SOLUTION: Provided is a program for causing a computer to execute the process of: acquiring first rainfall information measured by one or more radars linked to the river for water level prediction; determining the presence or absence of local rainfall that affects the water level of the river based on the first rainfall information acquired; and selecting third rainfall information used to predict the river water level from the first rainfall information by the radars and second rainfall information measured by rain gauges inside or near a specific area, depending on the result of the determination.SELECTED DRAWING: Figure 10

Description

The present disclosure relates to a program, a method, and an information processing device.

There is a method to predict the water level of a river based on the data measured by a rain gauge. For example, for a river having a length of more than about 100 km, a rain gauge is used to predict the water level of the river. However, it is difficult to accurately predict the water level of a river using a rain gauge for local heavy rain (hereinafter also referred to as "local heavy rain") in areas where the rain gauge density is not high. There is.

Therefore, by analyzing the rainfall amount, movement of rain areas, etc. by radar, the results of flood forecast, sediment disaster forecast, and flood simulation are reported to a wide range of related platforms so that the river can be responded quickly and accurately. A technique for constructing an information providing system is provided (for example, see Patent Document 1).

JP-A-2002-269656

However, Patent Document 1 does not describe how to specifically use various data such as rainfall by radar to perform flood forecasting, sediment disaster forecasting, and flood simulation. Therefore, the water level of the river may not be properly predicted.

Thus, in one aspect, the present disclosure provides an appropriate prediction of river water level according to rainfall conditions.

In one embodiment, the first rainfall amount information measured by one or a plurality of radars associated with the river targeted for water level prediction is acquired, and the river is acquired based on the acquired first rainfall amount information. The presence or absence of local rainfall that affects the water level of, the third rainfall information used to predict the water level of the river according to the result of the determination, the first rainfall amount by the radar. There is provided a program for causing a computer to execute a process of selecting from information and second rainfall information measured by a rain gauge inside or near a specific area.

In one aspect, the present disclosure can appropriately predict river water levels depending on rainfall conditions.

The figure showing an example of the functional composition of the information processor concerning one embodiment. The figure which shows an example of the water level database which concerns on one Embodiment. The figure which shows an example of the rainfall database which concerns on one Embodiment. The figure which shows an example of the radar database which concerns on one Embodiment. The figure which shows an example of the watershed river database which concerns on one Embodiment. The figure which shows an example of the water level observatory database which concerns on one Embodiment. The figure which shows an example of the rainfall observatory database which concerns on one Embodiment. The figure showing an example of the hardware constitutions of the information processor concerning one embodiment. The flowchart which shows an example of the prediction process which concerns on 1st Embodiment. The figure for demonstrating the prediction process which concerns on 1st Embodiment. The figure for demonstrating the prediction process which concerns on 1st Embodiment. The figure for demonstrating the prediction process which concerns on 1st Embodiment. The figure which shows the example of a screen which displays the result of the prediction process which concerns on 1st Embodiment. The flowchart which shows an example of the prediction process which concerns on 2nd Embodiment. The figure for demonstrating the prediction process which concerns on 2nd Embodiment.

An embodiment will be described below with reference to the accompanying drawings. In the present specification and the drawings, components having substantially the same functional configuration are designated by the same reference numerals, and duplicate description will be omitted.

[Functional configuration of information processing device]
First, an example of a functional configuration of the information processing apparatus 10 according to the embodiment will be described with reference to FIG. The information processing device 10 according to the embodiment includes a storage unit 11, an acquisition unit 12, a selection unit 13, a calculation unit 14, a determination unit 15, a prediction unit 17, a display unit 18, and a communication unit 19.

The storage unit 11 includes a water level database 111, a rainfall database 112, a radar database 113, a basin river database 114, a water level observatory database 115, and a rainfall observatory database 116.

FIG. 2 shows an example of the water level database 111 according to the embodiment. The water level database 111 accumulates the water level data of each water level observation station at each time. The water level data indicates the water level measured by each water level observatory.

FIG. 3 shows an example of the rainfall database 112 according to the embodiment. The rainfall database 112 accumulates rainfall data measured by each rainfall observation station at each time. The rainfall data corresponds to rainfall information measured by a rain gauge. The rainfall information measured by the rain gauge (hereinafter, also referred to as “rain gauge rainfall information”) is an example of second rainfall information used for predicting the water level of the river targeted for water level prediction.

FIG. 4A shows an example of the radar base 113 according to the embodiment. The radar base 113 stores geographical information (values at the end portions of latitude and longitude) defining pixels for each time and radar data (rainfall amount). Radar data corresponds to radar rainfall information. The radar rainfall amount information is an example of first rainfall amount information measured by one or a plurality of radars associated with a river targeted for water level prediction. As shown in FIG. 4B, the pixel indicates an area defined by geographical information indicating the end values of the north end latitude, the south end latitude, the west end longitude, and the east end longitude.

FIG. 5 shows an example of the basin river database 114 according to the embodiment. The basin river database 114 stores geographic information and data of basin rivers belonging to the area indicated by the geographic information.

FIG. 6 shows an example of the water level observatory database 115 according to one embodiment. The water level observatory database 115 stores position information (latitude information, longitude information) for each water level observatory and data of the river being observed.

FIG. 7 shows an example of the rainfall observatory database 116 according to one embodiment. The rainfall observatory database 116 stores position information (latitude information, longitude information) for each rainfall observatory, and data on the target river and basin division ratio. If the area of a specific target area (hereinafter, also referred to as “target basin”) is S and the area represented by the rainfall observatory i in the target basin is ai, the basin division ratio is indicated by “ai/S”. Be done.

The acquisition unit 12 acquires radar rainfall amount information measured by one or a plurality of radars associated with a river targeted for water level prediction and rainfall amount information of a rain gauge. Radar rainfall information is provided by the Meteorological Agency. The rainfall information of the rain gauge is provided by the local government and the like.

The selection unit 13 selects a target river in the target basin and selects a water level observation station of the target river. The calculation unit 14 calculates the number of pixels for each rainfall amount in the target basin and calculates the variance of the rainfall amount in the target basin based on the calculation result.

The determination unit 15 determines whether or not there is local rainfall that affects the water level of a specific target river (target river) based on the acquired radar rainfall amount information. As an example, the determination unit 15 may determine the presence or absence of local rainfall by using the distribution of rainfall based on the radar rainfall information.

The selection unit 13 uses radar rainfall information as rainfall amount information (hereinafter, also referred to as “third rainfall information”) used to predict the water level of the target river according to the determination result of the presence or absence of local rainfall. And rainfall information from a rain gauge inside or near a specific area. When it is determined that there is local rainfall, the selection unit 13 selects the first rainfall information as the third rainfall information, and when it is determined that there is no local rainfall, the third rainfall information. Alternatively, the second rainfall amount information may be selected.

The prediction unit 17 predicts the water level of the target river by using either the selected radar rainfall information or the rainfall gauge rainfall information as the third rainfall information.

The display unit 18 displays the predicted water level. The display unit 18 clearly indicates the information about whether the water level is predicted based on either the radar rainfall information or the rainfall gauge rainfall information. The display unit 18 displays the water level actually measured by the water level observing station and the water level predicted by the prediction unit 17.

The communication unit 19 receives various kinds of data relating to the water level of the river, such as water level data, rainfall data, radar data, and the like, from devices that distribute these data. The communication unit 19 acquires radar data from a device such as a distribution company that distributes weather data supplied from the Meteorological Agency, for example. The communication unit 19 also acquires the water level data and the rainfall amount data from a device such as a local government that distributes the water level data supplied from the water level observation station and the rainfall amount data supplied from the rainfall amount observation station.

[Hardware configuration]
Next, an example of the hardware configuration of the information processing device 10 according to the embodiment will be described with reference to FIG. The information processing device 10 includes an input device 101, a display device 102, and an external I/F 103. Further, the information processing device 10 includes a RAM 104, a ROM 105, a CPU 106, a communication I/F 107, and an HDD 108, which are connected to each other via a bus B.

The input device 101 includes a keyboard, a mouse, and the like, and is used to input each operation signal to the information processing device 10. The display device 102 includes a display such as an LCD (Liquid Crystal Display) monitor, a printer, a CRT (Cathode Ray Tube), and the like, and displays various processing results.

The external I/F 103 is an interface with an external device. The external device is, for example, a recording medium 103a. As a result, the information processing device 10 reads and/or writes to the recording medium 103a via the external I/F 103. The recording medium 103a may be a CD (Compact Disk) or a DVD (Digital Versatile Disk). The recording medium 103a may be an SD memory card, a USB memory (Universal Serial Bus memory), or the like.

The RAM 104 is a volatile semiconductor memory that temporarily holds data, and may temporarily hold the prediction program. The ROM 105 is a non-volatile semiconductor memory that can retain internal data even when the power is turned off. The ROM 105 stores basic programs and data.

The HDD 108 is a non-volatile storage device that stores programs and data. The programs and data stored in the HDD 108 include software that controls the entire information processing apparatus 10 and application software. Various databases may be stored in the HDD 108.

The CPU 106 is an arithmetic device that realizes control of the entire device and mounted functions by reading a prediction program and various data from the storage device such as the HDD 108 or the ROM 105 onto the RAM 104 and executing a predetermined process.

The communication I/F 107 is an interface that connects the information processing device 10 to a network. As a result, the information processing device 10 acquires the rainfall data from the rain gauge, the water level data from the water gauge, and the radar data from the radar via the communication I/F 107 every predetermined time. Each data is stored in the corresponding database of the storage unit 11.

FIG. 1 is a block diagram focusing on the functions of the information processing device 10. For example, the functions of the acquisition unit 12, the selection unit 13, the calculation unit 14, the determination unit 15, and the prediction unit 17 are executed by the prediction program executed by the CPU 106. It can be realized by a prediction process.

The forecast program acquires radar rainfall information measured by one or more radars associated with the river targeted for water level prediction, and based on the radar rainfall information, a local area that affects the water level of the river. The CPU 106 is caused to execute the process of determining the presence or absence of rainfall. Then, the prediction program, in accordance with the determination result, the third rainfall information used to predict the water level of the river, the first rainfall information by the radar and the rainfall in or near a specific area. The CPU 106 is caused to execute a process of selecting from the second rainfall amount information measured by the total.

The function of the communication unit 19 can be realized by the communication I/F 107, for example. The function of the storage unit 11 can be realized by, for example, the RAM 104, the ROM 105, and the HDD 108. The data in the various databases stored in the storage unit 11 may be stored in the RAM 104, the ROM 105, the HDD 108 in the information processing apparatus 10, or the storage device on the cloud connected to the information processing apparatus 10.

The information processing device 10 may be executed by a server device, a personal computer, or a cloud computer.

Further, the information processing device 10 may be a tablet terminal, a smartphone, a PDA (Personal Digital Assistants). The information processing device 10 may be an HMD (Head Mount Display), an FMD (Face Mount Display), or a wristwatch-type wearable device. The information processing device 10 may execute the prediction process by installing the application of the prediction program in a mobile device such as a smartphone. The data of various databases may be stored in a storage device on the cloud instead of being stored in the storage unit 11, and necessary information may be acquired from the storage device on the cloud.

<First Embodiment>
[Prediction processing]
Next, an example of the prediction process according to the first embodiment will be described with reference to FIG. FIG. 9 is a flowchart showing an example of the prediction process according to the first embodiment. The water level data, radar data, and rainfall data used in this process are acquired and updated at predetermined time intervals. Radar rainfall information and rain gauge rainfall information are data (mm/h) per unit time,
When this processing is started, in step S10, the selection unit 13 selects a target river based on the basin river database 114 (see FIG. 5). Further, the selection unit 13 selects the water level observation station of the target river based on the water level observation station database 115 (see FIG. 6). As a result, in the example of FIG. 10, it is assumed that the water level observing station “a water level observing station” corresponding to the position of the river “A river” is selected.

Returning to FIG. 9, next, in step S12, the selection unit 13 extracts the target watershed from the radar rainfall image. As a result, in the example of FIG. 10, it is assumed that the target watershed Ar is extracted.

Returning to FIG. 9, next, in step S14, the acquisition unit 12 acquires radar rainfall information and rain gauge rainfall information. For example, in the example of FIG. 10, the rainfall amount information measured by the rain gauges M1, M2, M3 provided at the three rainfall observation stations is acquired. However, the rain amount information of the rain gauge may be acquired before the process of step S14 is executed.

Returning to FIG. 9, next, in step S16, the calculation unit 14 calculates the number of pixels for each rainfall amount based on the radar rainfall amount information, and calculates the variance of the rainfall amount based on the calculated number of pixels for each rainfall amount. To do. For example, the calculation unit 14 calculates the number of pixels for each rainfall amount according to the radar rainfall image of the target catchment Ar shown in FIG. 10.

Specifically, the calculation unit 14 determines, based on the radar base 113 acquired at every predetermined time, each pixel in the basin (target basin Ar) of the target river at the predetermined time as geographical information (north end latitude, south end latitude). , West end longitude, east end longitude). Then, the calculation unit 14 acquires the rainfall amount corresponding to the cut-out pixels based on the radar rainfall amount information, and calculates the pixel number for each rainfall amount of the rainfall inside the basin.

In the example of the radar rainfall image of the target basin Ar in FIG. 11A, there is no region where the rainfall is locally large. An example of the distribution of the number of pixels for each rainfall calculated at this time is shown in the graph of (c) of FIG. 11.

On the other hand, in the example of the radar rainfall image of the target basin Ar in FIG. 11B, there is a region with a large amount of rainfall locally. An example of the distribution of the number of pixels for each rainfall calculated at this time is shown in the graph of (d) of FIG. 11.

The calculation unit 14 calculates the dispersion σ ^{2 of the} rainfall amount using the formula (1).

N in the equation (1) indicates the total number of pixels in the target basin Ar. x _i indicates the rainfall amount of each pixel.

は、各ピクセルの降雨量の平均値を示す。

Indicates the average amount of rainfall in each pixel.

As shown in the graph of (d) of FIG. 11, when the calculated variance σ ² is equal to or _larger than the predetermined first threshold σ ² _Limit , the local heavy rain mode can be determined. On the other hand, as shown in the graph of FIG. 11C, when the calculated variance σ ² is smaller than the first threshold σ ² _Limit , the state is not local heavy rainfall (hereinafter, referred to as “normal rainfall mode”). You can judge.

Therefore, in step S18 of FIG. 9, the determination unit 15 determines whether the variance σ ² is greater than or _{equal to} the first threshold σ ² _Limit . Then, when the variance σ ² is greater than or equal to the first threshold σ ² _Limit , the determination unit 15 determines the local heavy rain mode and executes the processes of steps S20 and S22. On the other hand, when the variance σ ² is smaller than the first threshold σ ² _Limit , the determination unit 15 determines the normal rainfall mode and executes the processes of steps S24 and S26.

In the local heavy rain mode, in step S20, the calculation unit 14 calculates the basin division ratio for each rainfall amount using the Thiessen method, and calculates the rainfall amount of each area divided based on the radar rainfall amount information using the basin division ratio. Calculate the average value (average rainfall).

For example, in FIG. 12A, the target flow area Ar is divided into areas Ar1, Ar2, and Ar3 as shown by a broken line D using the Thiessen method. Next, the calculation unit 14 calculates average values RA, RB, RC of the rainfall amounts of the areas Ar1, Ar2, Ar3 when the watershed Ar shown in (a) of FIG. 12 is divided into three, based on the radar rainfall amount information. calculate. FIG. 12B shows a graph of the number of pixels for each rainfall amount in the area Ar1, and the average value RA of rainfall amounts is indicated by an arrow. FIG. 12C shows a graph of the number of pixels for each rainfall in the area Ar2, and the average rainfall amount RB is indicated by an arrow. FIG. 12D shows a graph of the number of pixels for each rainfall amount in the area Ar3, and the average rainfall amount RC is indicated by an arrow.

Returning to FIG. 9, next, in step S22, the calculation unit 14 calculates the basin average rainfall R'. The basin division ratio is set for each rainfall observatory in the rainfall observatory database 116 (see FIG. 7). For example, assume that the watershed division ratios of the areas Ar1, Ar2, and Ar3 in FIG. 12 are s1, s2, and s3. The calculation unit 14 calculates the basin average rainfall R'using the equation (2) based on the calculated rainfall average values RA, RB, RC and the basin division ratios s1, s2, s3, and proceeds to step S28.

R′=RA×s1+RB×s2+RC×s3 (2)

On the other hand, when it is determined in step S18 that the mode is the normal rainfall mode, in step S24, the calculation unit 14 calculates the watershed division ratio for each rainfall amount using the Thiessen method. Then, the calculation unit 14 calculates the average value of the rainfall amount of each divided area based on the rainfall amount information (see FIG. 3) stored in the rainfall amount database 112 using the basin division ratio.

The calculation unit 14 calculates average values R1, R2, and R3 of rainfall amounts in the areas Ar1, Ar2, and Ar3 based on the rainfall amount information measured by the rain gauges M1, M2, and M3.

Next, in step S26, the calculation unit 14 calculates the basin average rainfall R. The calculation unit 14 calculates the basin average rainfall R by using the formula (3) based on the calculated rainfall average values RA, RB, RC and the basin division ratios s1, s2, s3, and proceeds to step S28.

R=R1×s1+R2×s2+R3×s3 (3)

In step S28, the prediction unit 17 calculates the water level data of the target river using the calculated basin average rainfall R′ or the calculated basin average rainfall R, and predicts the water level of the target river. A publicly known method such as the storage function method can be used to calculate the water level data. The calculated water level data is accumulated in the water level database 111.

Next, in step S30, the display unit 18 displays the predicted water level. For example, FIG. 13 shows an example of a screen displaying the predicted water level. In FIG. 13, the screen is switched and displayed for each mode, but the present invention is not limited to this, and the predicted water levels for each mode may be displayed side by side on one screen, or the line types of the predicted water level for each mode may be displayed. It may be changed and displayed in layers.

When the screen is switched for each mode, the display component 201 on the screen indicates the current mode to the user, as shown in FIG. As a result, it is possible to clearly indicate to the user which one of the normal rainfall mode and the local heavy rainfall mode is being displayed. The display component 202 shows the target river and the water level observation station. The display component 203 displays the water level for each hour of the target river. As a result, the water level 203a actually measured at the water level observing station and the water level 203b predicted by the prediction unit 17 in the past and the present (see broken line 204) of the target river are displayed. In order to separately display the actually measured water level 203a and the predicted water level 203b, it is preferable to change the color of the graph or the line type. Further, the graph may be moved and displayed according to the passage of time so that the current time indicated by the broken line 204 is displayed at a fixed position on the screen.

Further, as shown in FIG. 13, geographical information 205 including the target river may be displayed. In the geographical information 205, the position of the target river, the position of the rain gauge, the radar rainfall image, etc. are displayed. In addition, by displaying the watershed range that affects the water level of the target river, it is possible to visually show the prediction of the water level of the target river, and it is possible to more easily understand the effect of the increase in the water level of the target river.

Returning to FIG. 9, in step S32, the determination unit 15 determines whether a predetermined time has elapsed. The predetermined time may be preset and may be synchronized with the time when the rain gauge or radar data is updated. When the determination unit 15 determines that the predetermined time has elapsed, the determination unit 15 returns to step S14, acquires the updated radar rainfall information and the like, and repeats the processes thereafter.

As described above, according to the water level prediction method according to the first embodiment, the calculation unit 14 calculates the variance of rainfall in the target watershed based on the radar rainfall information. Then, the determination unit 15 determines the presence or absence of local rainfall in the target basin including the target river based on the calculated distribution of rainfall. Then, the selection unit 13 selects one of the normal rainfall mode and the local heavy rainfall mode according to the determination result, and uses the rainfall information used to predict the water level of the target river as radar rainfall information and a rain gauge. Select from the rainfall information in. As a result, the water level of the river can be appropriately predicted according to the rainfall situation.

That is, when it is determined that there is no local rainfall, that is, when there is no local heavy rainfall (in the case of normal rainfall mode), it is considered that the dispersion of rainfall in the target watershed is small. In this case, the rainfall information measured by the rain gauge is a value that approximates the total rainfall in the target watershed. Rain gauges measure accurate rainfall. Therefore, when the radar rainfall image is monitored and it is determined that local heavy rainfall has not occurred and it is a normal rainfall situation, the basin average rainfall R can be calculated based on the rainfall information measured by the rain gauge. , It becomes possible to accurately predict the water level of the river based on the calculated average rainfall R in the basin.

On the other hand, the rain gauges are installed at intervals of a dozen kilometers, and the density of rain gauges is not high. Therefore, when it is determined that there is local rainfall, that is, when there is local heavy rainfall (in the case of local heavy rain mode), it is difficult to accurately predict the water level of the river based on the rainfall information measured by the rain gauge. There is a case. In this case, by using radar rainfall information, it is possible to detect the occurrence of local heavy rainfall even in an area where the rain gauge density is not high. That is, when it is determined that the local rainfall mode is based on the radar rainfall information, by calculating the basin average rainfall R'based on the radar rainfall information as a means to replace the rain gauge, based on the calculated basin average rainfall R'. It is possible to predict the water level of the river according to the situation of local heavy rainfall.

As a result, in particular, by displaying in real time the danger of flooding of small and medium-sized rivers that flow in areas where the rain gauge density is not high, it is possible to encourage appropriate evacuation.

<Second Embodiment>
[Prediction processing]
Next, an example of the prediction process according to the second embodiment will be described with reference to FIG. FIG. 14 is a flowchart showing an example of the prediction process according to the second embodiment. The water level data, radar rainfall information, and rain gauge rainfall information used in this process are acquired and updated at predetermined time intervals. In addition, among the steps of FIG. 14, the step to which the same step number as that of the step (see FIG. 9) of the prediction process according to the first embodiment is given indicates that the same process is performed.

When this process is started, in step S10, the selection unit 13 selects the water level observation station of the target river. Next, in step S12, the selection unit 13 extracts the target watershed from the radar rainfall image. Next, in step S14, the acquisition unit 12 acquires radar rainfall information and rain gauge rainfall information.

Next, in step S40, the calculation unit 14 calculates the number of pixels for each rainfall amount in the target basin Ar based on the radar rainfall amount information, and determines the average value y1 of the radar rainfall amount of 10% from the higher rainfall amount. calculate. Further, the calculation unit 14 calculates the average value y2 of the rainfall amount information measured by the rain gauges M1, M2, M3 in the target watershed Ar. Then, the calculation unit 14 calculates the difference (=y1-y2) between the two average values y1 and y2.

Next, in step S42, the determination unit 15 determines whether or not the calculated difference is greater than or _equal to a preset second threshold y _Limit . When the determination unit 15 determines that the calculated difference is equal to or greater than the second threshold value y _Limit , the determination unit 15 determines the local heavy rain mode, and executes the processes of steps S20, S22, S28, S30, and S32. On the other hand, when it is determined in step S42 that the calculated difference is smaller than the second threshold value y _Limit , the determination unit 15 determines that the mode is the normal rain mode, and executes the processes of steps S24, S26, S28, S30, and S32. .. The processing of steps S20 to S32 is the same as the step of the same number in the prediction processing according to the first embodiment, and thus the description thereof is omitted.

For example, the calculation unit 14 calculates the average value y1 of the rainfall amounts of the top 10% from the number of pixels for each rainfall amount in (c) of FIG. 15 and calculates the difference from the average value y2 of the rainfall amount information. Then, when it is determined that the calculated difference (=y1-y2) is smaller than the second threshold value y _Limit , the determination unit 15 can determine that the normal rainfall mode is as shown in (a) of FIG.

Further, for example, the calculation unit 14 calculates the average value y1 of the rainfall amounts of the top 10% from the number of pixels for each rainfall amount in (d) of FIG. 15 and calculates the difference from the average value y2 of the rainfall amount information. Then, when it is determined that the difference (=y1-y2) is equal to or greater than the second threshold value y _Limit , the determination unit 15 can determine that the local heavy rain mode is as shown in (b) of FIG.

In the present embodiment, the top 10% of the number of pixels for each rainfall amount is extracted and used for comparison with the average value of the rainfall information, but the present invention is not limited to this, and the top z% from the number of pixels for each rainfall amount (z is arbitrary ) May be taken out and used for comparison with the average value of rainfall information.

As described above, according to the water level prediction method according to the second embodiment, the calculation unit 14 calculates the distribution of the number of pixels for each rainfall amount of the radar rainfall amount information based on the radar rainfall amount information. Then, the determination unit 15 determines the presence or absence of local rainfall based on the difference between the average value of the rainfall amount of the top z% and the average value of the rainfall amount of the rain gauge. Then, the selection unit 13 selects the third rainfall information used for predicting the water level of the river from the radar rainfall information and the rainfall gauge rainfall information according to the determination result. As a result, the water level of the river can be appropriately predicted according to the rainfall situation.

The method of predicting the water level of a river according to the first and second embodiments has been described above. According to the prediction method according to each embodiment, from the radar rainfall image, it is determined whether the normal rainfall mode or the local heavy rain mode, and the information for predicting the water level of the river according to the determination result is radar rainfall information and a rain gauge. Select from the rainfall information in. As a result, at the time of normal rainfall, it is possible to accurately predict the water level using the rainfall information of the rain gauge. In addition, during local heavy rainfall, the value calculated from radar rainfall can be input as the value of each rain gauge to predict the water level for local rainfall. This makes it possible to predict the river water level even in the case of local rainfall in areas where rain gauge density is not high. As a result, it is possible to predict changes in the water level rise in a river in real time and predict the risk of flooding, which is important from the viewpoint of disaster prevention, regardless of rainfall conditions.

In particular, even in small and medium-sized rivers where there are no rainfall stations in the river basin, the average rainfall in the basin can be ascertained, and even in areas where rain gauges for observing local heavy rainfall are not installed, there is a risk of flooding due to local heavy rainfall It can be predicted in real time and can be useful for disaster prevention.

Although the program, the method, and the information processing apparatus have been described above by the above embodiments, the program, the method, and the information processing apparatus according to the present disclosure are not limited to the above embodiments, and various modifications within the scope of the present disclosure. And improvements are possible. In addition, when there are a plurality of the above-described embodiments and modified examples, they can be combined within a range that does not conflict.

For example, in the prediction process of each of the above-described embodiments, the rain gauge in the target basin Ar is used, but the prediction process of each of the above-described embodiments is performed using the rain gauge outside the target basin Ar and in the vicinity of the target basin Ar. May be.

Regarding the above description, the following items will be further disclosed.
(Appendix 1)
Acquires the first rainfall information measured by one or more radars linked to the target river for water level prediction,
Based on the acquired first rainfall information, determine the presence or absence of local rainfall that affects the water level of the river,
Depending on the result of the determination, the third rainfall information used to predict the water level of the river was measured by the first rainfall information by the radar and a rain gauge inside or near a specific area. Select from the second rainfall information,
A program that causes a computer to perform processing.
(Appendix 2)
Using the distribution of rainfall based on the first rainfall information, determine the presence or absence of the local rainfall,
The program described in Appendix 1.
(Appendix 3)
Based on the comparison result of the first rainfall amount information and the second rainfall amount information, the presence or absence of the local rainfall is determined,
The program according to appendix 1 or 2.
(Appendix 4)
Either of the first rainfall information and the second rainfall information selected, using as the third rainfall information, to predict the water level of the river,
Display the result of the prediction of the water level and the result of the determination of the presence or absence of the local rainfall,
The program according to any one of appendices 1 to 3.
(Appendix 5)
If it is determined that there is local rainfall, as the third rainfall information, select the first rainfall information,
If it is determined that there is no local rainfall, as the third rainfall information, select the second rainfall information,
The program according to any one of appendices 1 to 4.
(Appendix 6)
Acquires the first rainfall information measured by one or more radars linked to the target river for water level prediction,
Based on the acquired first rainfall information, determine the presence or absence of local rainfall that affects the water level of the river,
Depending on the result of the determination, the third rainfall information used to predict the water level of the river was measured by the first rainfall information by the radar and a rain gauge inside or near a specific area. Select from the second rainfall information,
The way computers perform processing.
(Appendix 7)
Using the distribution of rainfall based on the first rainfall information, determine the presence or absence of the local rainfall,
The method according to appendix 6.
(Appendix 8)
Based on the comparison result of the first rainfall amount information and the second rainfall amount information, the presence or absence of the local rainfall is determined,
The method according to appendix 6 or 7.
(Appendix 9)
Either of the first rainfall information and the second rainfall information selected, using as the third rainfall information, to predict the water level of the river,
Display the result of the prediction of the water level and the result of the determination of the presence or absence of the local rainfall,
9. The method according to any one of appendices 6 to 8.
(Appendix 10)
If it is determined that there is local rainfall, as the third rainfall information, select the first rainfall information,
If it is determined that there is no local rainfall, as the third rainfall information, select the second rainfall information,
The method according to any one of appendices 6 to 9.
(Appendix 11)
An acquisition unit that acquires the first rainfall information measured by one or more radars linked to the river for which the water level is to be predicted,
Based on the acquired first rainfall information, a determination unit that determines the presence or absence of local rainfall that affects the water level of the river,
Depending on the result of the determination, the third rainfall information used to predict the water level of the river was measured by the first rainfall information by the radar and a rain gauge inside or near a specific area. A selection unit that selects from the second rainfall information,
Information processing device having a.
(Appendix 12)
The determining unit determines the presence or absence of the local rainfall by using the distribution of rainfall based on the first rainfall information.
The information processing device according to attachment 11.
(Appendix 13)
The determination unit determines the presence or absence of the local rainfall based on a comparison result between the first rainfall amount information and the second rainfall amount information,
The information processing device according to appendix 11 or 12.
(Appendix 14)
Any one of the first rainfall information and the second rainfall information selected, using the third rainfall information, a prediction unit that predicts the water level of the river,
A display unit that displays the result of the water level prediction and the result of the determination of the presence or absence of local rainfall,
The information processing device according to any one of appendices 11 to 13.
(Appendix 15)
When the selection unit determines that there is the local rainfall, as the third rainfall amount information, selects the first rainfall amount information,
If it is determined that there is no local rainfall, as the third rainfall information, select the second rainfall information,
The information processing device according to any one of appendices 11 to 14.

10 information processing device 11 storage unit 12 acquisition unit 13 selection unit 14 calculation unit 15 determination unit 17 prediction unit 18 display unit 19 communication unit 111 water level database 112 rainfall database 113 radar base 114 watershed river database 115 water level observation station database 116 rainfall observation station The database

Claims (7)

Acquires the first rainfall information measured by one or more radars linked to the target river for water level prediction,
Based on the acquired first rainfall information, determine the presence or absence of local rainfall that affects the water level of the river,
Depending on the result of the determination, the third rainfall information used to predict the water level of the river was measured by the first rainfall information by the radar and a rain gauge inside or near a specific area. Select from the second rainfall information,
A program that causes a computer to perform processing. Using the distribution of rainfall based on the first rainfall information, determine the presence or absence of the local rainfall,
The program according to claim 1. Based on the comparison result of the first rainfall amount information and the second rainfall amount information, the presence or absence of the local rainfall is determined,
The program according to claim 1. Any of the first rainfall information and the second rainfall information selected, as the third rainfall information, to predict the water level of the river,
Display the result of the prediction of the water level and the result of the determination of the presence or absence of the local rainfall,
The program according to any one of claims 1 to 3. If it is determined that there is local rainfall, as the third rainfall information, select the first rainfall information,
If it is determined that there is no local rainfall, as the third rainfall information, select the second rainfall information,
The program according to any one of claims 1 to 4. Acquires the first rainfall information measured by one or more radars linked to the target river for water level prediction,
Based on the acquired first rainfall information, determine the presence or absence of local rainfall that affects the water level of the river,
Depending on the result of the determination, the third rainfall information used to predict the water level of the river was measured by the first rainfall information by the radar and a rain gauge inside or near a specific area. Select from the second rainfall information,
The way computers perform processing. An acquisition unit that acquires the first rainfall information measured by one or more radars linked to the river for which the water level is to be predicted,
Based on the acquired first rainfall information, a determination unit that determines the presence or absence of local rainfall that affects the water level of the river,
Depending on the result of the determination, the third rainfall information used to predict the water level of the river was measured by the first rainfall information by the radar and a rain gauge inside or near a specific area. A selection unit that selects from the second rainfall information,
Information processing device having a.

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