JP2016122239A - Sediment disaster prediction system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To easily grasp a dangerous place and a time point high in a possibility of the sediment disaster.SOLUTION: A sediment disaster prediction system includes: a sediment disaster water level determination part for determining a sediment disaster water level showing that a possibility of the sediment disaster occurrence is high based on a rain amount, the water level being in the upper end part of a dangerous zone where the sediment disaster is expected to occur; a dangerous zone database for storing a position of the upper end part of the dangerous zone and the determined sediment disaster water level in association with the dangerous zone with respect of the respective dangerous zones; a water level prediction system for predicting the water level of a designated point; and an alert zone determination part for determining whether or not the dangerous zone stored in the dangerous zone database is an alert zone high in possibility of the sediment disaster occurrence.SELECTED DRAWING: Figure 2-5

Description

  The present invention relates to a sediment disaster prediction system.

In recent years, weather disasters caused by torrential rains and long rains have increased, causing a lot of damage. In addition to flooding from rivers, there are cases where people are caught in disasters shortly after evacuation due to sudden flooding from rivers where water is not normally flowing. In order to prevent the occurrence of a weather disaster due to such a rise in water level, various systems for estimating the risk of disaster by predicting the water level in a predetermined area have been proposed.
As such a system, in Patent Document 1 below, cameras are arranged at a plurality of observation points in a river basin, and the obtained image is transmitted to a computing device via a communication line. By comparing the image with a reference image acquired in advance, water level data representing the water level at each observation point is obtained, and processing of the obtained water level data makes it possible to determine a predetermined downstream basin where there is a possibility of flooding. A system has been proposed that can predict the water level at a monitoring point.

And conventionally, the area | region where the risk of a landslide disaster is high is specified based on the landslide disaster prevention law. In the Sediment-related Disaster Prevention Law, the area where there is a risk of landslide disaster is divided into two types, “Sediment-related disaster warning area” and “Sediment-related disaster special warning area”. Standards are established for each of the three types of disasters, “Debris flow” and “Landslide”, to determine the risk of disaster.
Furthermore, the risk of landslide disaster is determined based on precipitation and soil rainfall index, and when the risk increases, landslide disaster warning information is released for each municipality.

JP 2008-57994 A

In the landslide disaster prevention method described above, a location where there is a landslide disaster risk is determined from a static state such as a slope or a distance, and the rainfall situation at that time is not taken into consideration. For this reason, since it is impossible to grasp the change in the degree of danger in the precipitation state at that time, it cannot be used as information on “when to evacuate”. In addition, since this information cannot be obtained without browsing the ledger at a city hall or the like, few residents know whether or not their home is in the danger zone.
On the other hand, landslide disaster warning information is announced when it is determined that the risk of landslide disaster has increased based on precipitation and soil rainfall index as described above. However, it can only be seen with a mesh of 5km on each side, and it is rough compared to the area designation in the Sediment Disaster Prevention Law, and it is not enough as information on "Where is dangerous now?" In addition, since the unit of announcement is a municipality unit, even residents in the area are difficult to identify themselves and may lead to delays in evacuation. In addition, “Sediment disaster alert judgment mesh information” exists, but since it is a mesh of 5km per side, it is still difficult to grasp it as myself.
In addition, the soil rainfall index, which is the standard for judging sediment disasters, is a conceptual numerical value obtained by calculation, so it is difficult to intuitively understand the degree of danger.
Therefore, it is extremely difficult for the general population to grasp the dangerous place and time point where there is a high possibility of landslide disaster, and the object of the present invention is to solve the problems of the conventional examples as described above. It is.

  In order to achieve the above object, the present invention is a landslide disaster prediction system for predicting the possibility of a landslide disaster using a computer, and is a water level at the upper end of a dangerous area where a landslide disaster is expected to occur. For each of the dangerous areas, the position of the upper end of the dangerous area and the determined earth and sand are determined for each of the dangerous areas. A dangerous area database that stores disaster water levels in association with dangerous areas, a water level prediction unit that predicts the water level at a specified point, and a dangerous area that is stored in the dangerous area database can cause sediment disasters. A warning area determination unit that determines whether the area is a highly sensitive warning area, and obtains the position of the upper end of the dangerous area and the sediment disaster water level from the dangerous area database. The acquired position of the upper end is supplied to the water level prediction unit as a point where the water level should be predicted, the predicted water level of the position of the upper end is received from the water level prediction unit, and the received predicted water level is used as the sediment disaster. A warning area determination unit configured to determine that the dangerous area is a warning area when the predicted water level is higher than the sediment disaster water level compared to the water level. Provide a sediment disaster prediction system.

  In the landslide disaster prediction system according to the present invention described above, the landslide disaster water level determination unit may determine the landslide disaster water level based on a rainfall amount when a landslide disaster actually occurred in the past in the dangerous area. Good.

  In the earth and sand disaster prediction system according to the present invention described above, the earth and sand disaster water level determination unit determines the water level predicted by the water level prediction unit using the rainfall at the time of the occurrence of a past earth and sand disaster as the earth and sand disaster water level. It is good.

  In the landslide disaster prediction system according to the present invention described above, the landslide disaster water level determination unit may determine the landslide disaster water level by comparing a predicted water level calculated based on rainfall and a soil rainfall index. Good.

  In the landslide disaster prediction system according to the present invention described above, the landslide disaster water level determination unit, when the soil rainfall index obtained by the calculation exceeds the soil rainfall index reference value that is a criterion for occurrence of landslide disaster, The water level predicted by the water level prediction unit using the rainfall used for calculating the soil rainfall index may be determined as the sediment disaster water level.

  In the earth and sand disaster prediction system according to the present invention described above, the earth and sand disaster water level determination unit determines the earth and sand disaster water level for a nearby dangerous area similar in soil quality to the dangerous area as the earth and sand disaster water level of the dangerous area. Also good.

  In the landslide disaster prediction system according to the present invention described above, when the caution area determination unit determines that the danger area is a caution area, landslide disaster warning information indicating that a landslide disaster may occur is generated. A warning information generation unit may be further provided.

  In the earth and sand disaster prediction system according to the present invention described above, the water level prediction unit includes an altitude database that stores the altitude of each of a plurality of sections obtained by dividing the monitoring target area in association with the sections, and a predetermined level. In this interval, based on the rainfall and inflow of each of these sections, the storage height calculation means for calculating the estimated storage height, and the sum of the elevation and the estimated storage height for each section at the predetermined interval is calculated as a virtual water surface. An outflow direction determining means for determining an outflow direction of water, with a section having the lowest virtual water surface among the virtual water surfaces of a plurality of sections surrounding each of the sections as a direction in which water flows out from the center section, and a predetermined interval The outflow amount calculating means for calculating the outflow amount from each section in the outflow direction of the section based on the water storage amount and the rainfall amount of each section, and the determined outflow direction and outflow And based on the cross-sectional shape of the flow path, the water level calculating means for calculating the level of said path, may be provided with.

  As described above, the landslide disaster prediction system of the present invention determines the risk of landslide disaster based on the comparison between the landslide disaster water level and the predicted water level at the upper end of the danger zone. In other words, the danger of landslide disasters is expressed using an intuitively understandable concept (water level) regarding a place familiar to the residents (upper end of the danger zone). Therefore, the residents can specifically imagine where and how much danger of landslide disasters occur, which is extremely effective for guiding residents to evacuate. Moreover, according to the earth and sand disaster prediction system of the present invention, the earth and sand disaster water level, which is a judgment criterion for the risk of earth and sand disasters, can be set to an appropriate value, and the earth and sand disaster can be predicted accurately.

It is a block diagram which shows the structure of the earth and sand disaster prediction system which concerns on this invention. It is a flowchart which shows operation | movement of the earth and sand disaster prediction system shown in FIG. FIG. 2 is a flowchart showing a processing operation for one dangerous area in the flowchart shown in FIG. 2-1. It is a figure which shows an example of the screen produced | generated and displayed by the alert information generation part shown in FIG. It is a figure which shows another screen generated and displayed by the alert information generation part shown in FIG. It is a flowchart which shows the processing operation of the earth and sand disaster water level determination part. It is a block diagram which shows the structure of the water level prediction system proposed by the present applicant which can be applied as a water level prediction part of the earth and sand disaster prediction system shown in FIG. It is a block diagram which shows the structure of the vegetation registration part of the water level prediction system shown in FIG. FIG. 5 is a diagram for explaining a process of associating pixel coordinates of an oblique photograph with position coordinates on the ground surface in the vegetation registration unit shown in FIG. 4. FIG. 5 is a diagram for describing processing for setting a plurality of representative points in a vegetation region in the vegetation registration unit illustrated in FIG. 4. It is a figure which shows the example of the RGB value stored in the vegetation master produced | generated in the vegetation registration part shown in FIG. It is a figure for demonstrating the process which determines an outflow direction based on an altitude + water storage height in the outflow amount and direction calculating part of the water level prediction system shown in FIG. It is a figure for demonstrating the process which calculates the outflow amount depending on the flow velocity in the outflow amount and direction calculation part of the water level prediction part shown in FIG. It is a figure for demonstrating the process which determines the outflow direction when the river exists in the outflow amount and direction calculation part of the water level prediction system shown in FIG. It is a figure for demonstrating the known tank model for calculating the water which flows in the soil used in the outflow amount and direction calculation part of the water level prediction system shown in FIG. It is the figure which showed the relationship between the rainfall amount, runoff amount, and a direction calculated in the runoff amount and direction calculation part of the water level prediction system shown in FIG. It is a figure which shows the river model used for the water level calculation in the water level calculation part of the water level prediction system shown in FIG.

Before describing the configuration of the earth and sand disaster prediction system of the present invention in detail, the main points that the present inventors have focused on in constructing the system will be described.
1) Areas at risk of landslide disasters (dangerous areas) can be obtained based on existing surveys.
2) In the event of a landslide disaster, determine whether the water level of rivers and swamps has risen at the upper end of the area, and therefore the water level at the upper end of the dangerous area obtained based on survey records, etc. Therefore, it is possible to determine the risk of landslide disasters.
3) At the upper end of the danger zone, there is usually no flow of water, and it is often difficult to install measuring instruments. Furthermore, it is not practical to install a water level gauge when there are many target areas to be monitored. Therefore, instead of installing a water level gauge, a rise in water level can be predicted by using a water level prediction system that predicts the water level by a computer based on rainfall conditions and the like.
4) At the upper end of each hazardous area, the water level that is assumed to increase the risk of landslide disasters is set as the landslide disaster water level, and if the predicted water level reaches that level, the danger level As the risk of earth and sand disasters in the area is increasing, an alarm is generated with the dangerous area as a warning area.

  On the Internet, various administrative organizations such as the Ministry of Land, Infrastructure, Transport and Tourism, Japan Meteorological Agency, and prefectures publish live information obtained from water level gauges, rain gauges, cameras, and so on. Of these, information that can be used for water level prediction is obtained and used as “water level related public information”. In particular, the calculation of the water level prediction can be continued by using the rainfall at the rainfall observation point as it is, such as when the precipitation forecast data cannot be obtained. By confirming the difference between the actually measured water level by the water level gauge and the predicted water level by the water level prediction system, it is possible to verify the water level prediction system and make corrections based on it.

  FIG. 1 is a block diagram showing a configuration of a sediment disaster prediction system according to an embodiment of the present invention. The system includes a water level prediction unit 10, a warning area determination unit 20, and an alarm information generation unit configured by program units. 30, a water level related public information database (DB) 40, a dangerous area information DB 50, and a water level calculation result DB 60. The landslide disaster prediction system further includes a landslide disaster water level determination unit 70 and a soil rainfall index calculation unit 80 configured by program units, a landslide disaster history DB 90, and a soil rainfall index reference value DB 95. Although not shown, it goes without saying that an input unit to the sediment disaster prediction system and an output unit from the system are provided.

The water level related public information DB 40 stores information that can be used for water level prediction disclosed by various administrative agencies, that is, water level related public information (including actual measurement information and prediction information). The information is automatically obtained, for example, every 5 minutes from URLs provided by various administrative organizations or the like by appropriate means not shown, and stored in the DB 40.
The dangerous area information DB 50 includes information for identifying an area where there is a risk of occurrence of a landslide disaster, that is, a “dangerous area” obtained in advance by an existing survey, and a “upper end” that is the position of the “upper end” of each dangerous area. Information indicating the “position” and information specifying the “sediment disaster water level” at the upper end of each dangerous area determined by the sediment disaster water level determination unit 70 are stored. The method for determining the sediment-related disaster water level by the sediment-related disaster water level determining unit 70 will be described in detail below. The dangerous area information DB 50 further stores information indicating the soil quality at the upper end of each dangerous area. Soil information can be obtained from field surveys or data published by, for example, the Ministry of Land, Infrastructure, Transport and Tourism. Also, the dangerous area information DB 50 also stores information to that effect when a certain “dangerous area” is determined to be a “warning area” by the warning area determination unit 20.
The water level calculation result DB 60 stores the water level (predicted water level) predicted by the water level prediction unit 10. When the warning area determination unit 20 specifies the prediction of the water level at the upper end of the dangerous area, the predicted water level at the upper end is stored. The predicted water level at the upper end is referred to by the warning area determination unit 20.

  The earth and sand disaster history DB 90 is a database that stores information on past earth and sand disasters. Specifically, the earth and sand disaster history DB 90 stores information indicating a dangerous area where an earth and sand disaster has actually occurred in the past and rain data relating to precipitation that caused the earth and sand disaster in association with each other. When there are a plurality of dangerous areas where a sediment disaster has actually occurred in the past, the sediment disaster history DB 90 stores information indicating the dangerous area and rainfall data in association with each of the plurality of dangerous areas. If a landslide disaster has occurred multiple times in the same dangerous area, the landslide disaster history DB 90 stores information indicating the dangerous area and rainfall data in association with each of the multiple landslide disasters. May be. Rainfall data is data indicating the amount of rainfall that has fallen in an area upstream of the upper end of the dangerous area where the sediment disaster has occurred. The earth and sand disaster history DB 90 stores, as rain data, for example, data indicating the rainfall over time over a predetermined length of time before the time when the earth and sand disaster occurred. The period of the predetermined length may be, for example, a period from the start of getting down to the time when the earth and sand disaster occurs, or may be a predetermined period such as several days or weeks before the occurrence of the earth and sand disaster.

  The soil rainfall index reference value DB 95 is a database that stores a reference value of a soil rainfall index for each section obtained by dividing the ground surface into areas of a predetermined size (for example, 1 km square or 5 km square mesh). The soil rainfall index is a value obtained by indexing how much rainwater is accumulated in the soil of the area due to precipitation. The soil rainfall index reference value defines the value of the soil rainfall index that serves as a reference for determining whether or not the risk of occurrence of a sediment disaster has increased. That is, when the soil rainfall index exceeds the soil rainfall index reference value in a certain area, it is considered that there is a high possibility that a sediment disaster will occur in that area. For example, a soil rainfall index reference value for each area obtained by dividing the whole of Japan into a 1 km square mesh is provided by the Japan Meteorological Agency, and this data may be stored in the soil rain index reference value DB 95 and used.

  The water level prediction unit 10 has a function of predicting a water level with the passage of time of a designated area or point, and any of existing water level prediction systems can be applied. In addition, although this applicant applied for a patent of the water level prediction system as Japanese Patent Application No. 2014-001490, this water level prediction system can also be used as the water level prediction unit 10. This water level prediction system will be described in detail later with reference to FIGS.

FIGS. 2-1 and 2-2 are flowcharts showing the processing operation of the sediment disaster warning system shown in FIG. 1, and the system displays water level related public information including precipitation forecast data of all dangerous areas. Upon receipt (for example, every 5 minutes), the predicted water level of the upper end position of all the dangerous areas is sequentially calculated, and this is repeatedly executed until an end instruction is issued. More specifically, as shown in FIG. 2-1, when the process is started, the process proceeds from step S10 to determine whether or not an end instruction has been issued to step S20, and the distribution of precipitation forecast data is received. Store in the water level related public information DB 40. In step S30, the warning area determination unit 20 determines whether or not the landslide disaster determination process for all the dangerous areas has been completed, and then performs a landslide disaster determination process for one dangerous area in step S40. By repeatedly executing Steps S30 and S40, the landslide disaster determination process for all dangerous areas is executed. When the landslide disaster determination process is completed for all the dangerous areas, the process returns to step S10 to determine whether an end instruction has been issued. If no end instruction has been issued, steps S20 to S40 are executed again. If an end instruction is issued, the process ends.
In addition, when it is not necessary to predict the risk of landslide disasters in all the dangerous areas stored in the dangerous area DB 50, a plurality of dangerous areas stored in the dangerous area DB 50 are displayed on the monitor screen. It may be set as appropriate by the operator selecting a dangerous area on the screen.

  FIG. 2-2 shows in detail the processing by the warning area determination unit 20 for determining the sediment disaster for one dangerous area in step S40. As shown in FIG. 2-2, the warning area determination unit 20 searches the dangerous area DB 50 in step S41 to acquire the “upper end position” and “sediment disaster water level” of the area, and acquires in step S42. The “upper end position” is provided to the water level prediction unit 10. Thereby, the water level prediction unit 10 predicts and calculates the water level at the upper end position based on the information in the water level related public information DB 40. Of course, in order to calculate the predicted water level at the upper end point, the water level prediction unit 10 also predicts the predicted water level at an appropriate upstream point. The calculated predicted water level is stored in the water level calculation result DB 60 in association with each point.

  Next, in step S43, the warning area determination unit 20 receives the predicted water level of the upper end position calculated by the water level prediction unit 10, and in step S44, the predicted water level is acquired in the sediment disaster water level of the upper end (in step S41). ) Is exceeded. When the predicted water level exceeds the earth and sand disaster water level, in step S45, information (warning area information) indicating that the danger area may become a warning area is provided to the alarm generation unit 30. This information includes a predicted water level according to the time lapse predicted by the water level prediction unit 10 and information such as when the predicted water level reaches the sediment disaster water level. As a result, the warning generation unit 30 generates landslide disaster caution information including the area where the occurrence of a landslide disaster is predicted and the estimated time (the time when the predicted water level is predicted to reach the landslide water level). Become.

  As described above, in the earth and sand disaster prediction system of the present invention, the position of the upper end portion of the dangerous area is previously investigated and stored, and when the predicted water level of the upper end position exceeds the earth and sand disaster water level, It is configured to generate disaster alert information. FIG. 2-3 shows an example display screen in which earth and sand disaster caution information is displayed on a map. The display screen is a predicted water level and warning area determination stored in the water level calculation result DB 60 by the alarm generation unit 30. It is generated based on the warning area information from the unit 20 and displayed on a display (not shown).

  By the way, the predicted water level may be different from the actually measured water level, and the sediment-related disaster water level may also be different from the water level where the actual sediment-related disaster occurs. Dangerous when separated. Therefore, when using the simulation result, it is important to refer to the water level related public information and make a comprehensive judgment in consideration of the surrounding rainfall, water level, camera image, and the like. However, since the water level related public information has various providers as described above, and it is necessary to open and refer to the screen for each provider, there is a risk of inefficiency and oversight.

  Therefore, the warning information generating unit 30 in the landslide disaster prediction system of the present invention not only displays a warning about the area where the landslide is expected to occur and its estimated time, but also the occurrence of a landslide disaster. Is a water level related public information stored in the water level related public information DB 40, for example, rainfall, weather warning / warning (and its forecast text), and a link to the live camera on the map. It can be displayed. FIG. 2-4 shows a screen shot of a map displaying such water level related public information generated and displayed by the alert information generating unit 30. In the screen of FIG. 2-4, the camera mark is a link to the live camera, and regarding the rainfall, the actual rainfall (10 minutes, 1 hour, and cumulative) of the corresponding area is displayed within the three frames. . As for the rainfall, not only the actual value but also the predicted rainfall (for example, up to 6 hours ahead every hour) can be displayed based on the precipitation forecast data. By displaying them together on the map in this way, even if it is determined that the “dangerous area” is not the “warning area” by the warning area determination unit 20, it falls under the comprehensive judgment of the operator or the like. An alarm can be generated in the area. This can be applied not only as a prediction warning for sediment-related disasters but also as a prediction warning for floods.

  Next, the determination method of the earth and sand disaster water level by the earth and sand disaster water level determination part 70 is demonstrated in detail. FIG. 2-5 is a flowchart showing the processing operation of the sediment disaster water level determination unit 70. The determination process of the sediment disaster water level by the sediment disaster water level determination unit 70 is performed in advance prior to the sediment disaster prediction system performing the sediment disaster prediction process of FIGS. 2-1 and 2-2 in real time. Is. That is, the earth and sand disaster water level determination unit 70 determines the earth and sand disaster water level in advance in the following steps and stores it in the dangerous area information DB 50. Thereafter, the earth and sand disaster water level stored in the dangerous area information DB 50 is determined. As described above, the risk of landslide disaster is predicted in real time by being used for comparison with the predicted water level by the water level prediction unit 10.

  In step S110, the earth and sand disaster water level determination unit 70 selects one dangerous area from the dangerous area information DB 50, and determines whether or not the earth and sand disaster water level has already been set for the dangerous area. If the sediment-related disaster water level has already been set for the selected dangerous area, the sediment-related disaster water level determining unit 70 performs step S110 again for another dangerous area. If the landslide disaster water level has not yet been set for the selected danger area, the landslide disaster water level determination unit 70 advances the process to step S120.

  In step S120, the earth and sand disaster water level determination unit 70 refers to the earth and sand disaster history DB 90 and determines whether or not an earth and sand disaster has occurred in the past in the selected dangerous area. If there has been a landslide disaster in the risk area in the past, the landslide disaster water level determination unit 70 advances the process to step S130. If there has never been a landslide disaster in the dangerous area, the landslide disaster water level determination unit 70 advances the process to step S140.

  In step S130, the earth and sand disaster water level determination unit 70 determines the earth and sand disaster water level based on the amount of rainfall when an actual earth and sand disaster has occurred in the past in the dangerous area. Specifically, the sediment-related disaster water level determination unit 70 calculates the predicted water level at the upper end of the dangerous area, which is predicted when it is assumed that the same rainfall will occur in the past as the actual sediment disaster occurred. It is determined as the landslide disaster water level in the hazardous area. More specifically, the earth and sand disaster water level determination unit 70 reads out the rainfall data stored in the earth and sand disaster history DB 90 in association with the dangerous area, and provides the read rain data to the water level prediction unit 10. In response to this, the water level prediction unit 10 predicts the water level at the upper end of the dangerous area using the rainfall data at the time of the occurrence of a past sediment disaster. This predicted water level is adopted by the earth and sand disaster water level determination unit 70 as the earth and sand disaster water level. In this way, by determining the sediment-related disaster water level based on the amount of rainfall at the time of actual past sediment-related disasters, it is possible to accurately determine the risk of sediment-related disasters when there is similar precipitation in the future.

  In addition, when the landslide disaster has occurred several times in the past in the dangerous area, the landslide disaster water level determination unit 70, for example, for each predicted water level predicted based on the rainfall data corresponding to each landslide disaster. Of these, the predicted water level with the smallest value may be determined as the sediment disaster water level. By doing so, in the prediction of the occurrence of a landslide disaster, it is possible to make a prediction that anticipates more safety, for example, by issuing a warning early.

  In step S140, the earth and sand disaster water level determination unit 70 refers to the dangerous area information DB 50, and determines whether another dangerous area close to the soil exists in the vicinity of the selected dangerous area. Specifically, the earth and sand disaster water level determination unit 70 selects another dangerous area located within a predetermined distance range (for example, within 10 km) from the dangerous area from the dangerous area information DB 50 and stores it in the dangerous area information DB 50. The presence of dangerous areas with similar soils is determined by comparing the soil information for those two hazardous areas (the dangerous area and the selected other dangerous area) based on a predetermined evaluation criterion. . If there is another dangerous area with close soil quality, the sediment disaster water level determination unit 70 advances the process to step S150. If there is no other dangerous area with similar soil quality, the sediment disaster water level determination unit 70 advances the process to step S160.

  In step S150, if the sediment disaster water level has already been set in the dangerous area information DB 50 for another nearby dangerous area determined to be close to the soil quality, the sediment disaster water level determination unit 70 sets the sediment disaster water level to the dangerous area. It will be determined as the earth and sand disaster water level. In this way, by using the sediment disaster water level in an area with similar soil quality, it is possible to determine the risk of sediment disaster for a dangerous area whose soil condition is estimated to be similar. On the other hand, if the landslide disaster water level is not yet set in the dangerous area information DB 50 for another nearby dangerous area determined to be close to soil quality, the landslide disaster water level determination unit 70 proceeds to step S160, or Once the process of step S150 for the dangerous area is stopped, the process from step S110 may be performed on another dangerous area near the soil.

  In step S160, the sediment disaster water level determination unit 70 calculates a predicted water level and a soil rainfall index based on the rainfall, and determines a sediment disaster water level based on a comparison between the obtained predicted water level and the soil rain index. Specifically, if the soil rainfall index calculated from a certain rainfall exceeds the soil rainfall index reference value, the sediment disaster water level determination unit 70 determines the predicted water level calculated from the same rainfall as the sediment disaster water level. In this way, by obtaining the predicted water level corresponding to the soil rainfall index reference value and setting it as the sediment disaster water level, for example, the soil rainfall index reference value, which is the judgment standard for sediment disasters published by the Japan Meteorological Agency, is adequate. It is possible to judge the risk of a serious sediment disaster.

  The process of step S160 will be described in more detail. The earth and sand disaster water level determination unit 70 provides the rainfall data to the soil rainfall index calculation unit 80. The rainfall data may be data of rainfall actually observed in the past in the area upstream of the upper end of the dangerous area (hereinafter referred to as the upstream area), or may be virtually generated rainfall data. It may be data. In the case of actual rainfall data, it does not matter whether or not the landslide disaster actually occurred. The soil rainfall index calculation unit 80 calculates the soil rainfall index in the upstream area based on the rainfall data, and returns the result to the sediment disaster water level determination unit 70. A known method can be used to calculate the soil rainfall index. For example, the soil rainfall index calculation unit 80 models the upstream area by a tank model similar to FIG. 11 described later, and sets the sum S1 + S2 + S3 of the storage amounts of each tank as the soil rainfall index of the upstream area. The landslide disaster water level determination unit 70 refers to the soil rainfall index reference value DB 95 to determine whether or not the soil rainfall index obtained by the above calculation is equal to or greater than the soil rainfall index reference value corresponding to the upstream area. .

  If the soil rainfall index is smaller than the soil rainfall index reference value, the landslide disaster water level determination unit 70 and the soil rainfall index calculation unit 80 use the other rainfall data to calculate the soil rainfall index and the soil rainfall index in the same manner as described above. Repeat the comparison with the reference value. If the soil rainfall index is equal to or greater than the soil rainfall index reference value, the sediment disaster water level determination unit 70 provides the water level prediction unit 10 with the rainfall data used for the calculation of the soil rainfall index. In response to this, the water level prediction unit 10 predicts the water level at the upper end of the dangerous area using the given rainfall data (that is, rainfall data in which the soil rainfall index is equal to or higher than the soil rainfall index reference value). To do. This predicted water level is adopted by the earth and sand disaster water level determination unit 70 as the earth and sand disaster water level. In this way, the sediment disaster water level is determined as the water level corresponding to the soil rainfall index reference value.

  In step S170, the earth and sand disaster water level determination unit 70 stores the earth and sand disaster water level determined by the processing in step S130, S150, or S160 in the dangerous area information DB 50 in association with the dangerous area. Thereafter, as described above, the sediment disaster prediction process of FIGS. 2-1 and 2-2 is performed, and the risk of the sediment disaster is predicted based on the sediment disaster water level stored in the dangerous area information DB 50.

FIG. 3 is a water level prediction system applicable as the water level prediction unit 10 of the sediment disaster prediction system of the present invention, and is a schematic diagram showing the configuration of a water level prediction system already filed by the present applicant as Japanese Patent Application No. 2014-001490. It is. In FIG. 3, 1 is an arithmetic unit, 2 to 8 are databases (DB), 2 is an altitude DB that stores altitude data, 3 is a rain DB that stores rain data, and 4 is local information. The local information DB 5 is a calculation result DB for storing calculation results such as a predicted water level calculated by the calculation unit 1, and 6 is a shooting position / posture DB for storing shooting positions and postures of aerial photographs or satellite photographs taken obliquely. , 7 is an oblique photograph correspondence DB that stores coordinates associated with oblique photographs, and 8 is a DB that stores the correspondence between vegetation types and RGB pixel values. When the water level prediction system of FIG. 3 is used as the water level prediction unit 10, the water level calculation result DB 60 shown in FIG. 1 is not required because the predicted water level data stored in the calculation result DB 5 of FIG. 3 may be used. It becomes.
The calculation unit 1 includes an outflow / direction calculation unit 11, a stored water amount calculation unit 12, a water level calculation unit 13, a dangerous watershed estimation unit 14, and a vegetation registration unit 15.

  In this water level prediction system, the observation area is divided into a plurality of sections in a mesh shape (lattice form), and the elevation data stored in the elevation DB 2 represents the “elevation value” for each division and is stored in the rain DB 3. Rainfall data also represents “actual rainfall value” and “rainfall forecast value” for each section. The section varies depending on the size of the target area, the required accuracy of water level measurement, and the like, but is usually 5 to 5000 m square. The altitude data is a general data format representing the topography, and is measured or obtained in advance and stored in the altitude DB2. In one embodiment, a mesh that can be obtained from the Geographical Survey Institute or the like, that is, the height of the center point of each section is adopted as the elevation data. The actual measurement value and the forecast value of the rainfall amount stored in the rainfall DB 3 are data that can be obtained from existing organizations such as the Japan Meteorological Agency, and are stored in the water level related DB 40 provided in the sediment disaster prediction system of FIG. Use rain information.

  The “local information” stored in the local information DB 4 investigates in advance the “river shape”, “river bed state”, and “land state” (that is, “vegetation state”) in a plurality of predetermined observation areas. In addition to the data obtained from this, the data of the local “dangerous water level” is stored. “The shape of the river” and “the condition of the river bed” are obtained in advance by various materials or field surveys, and the “the shape of the river” is the “lower bottom”, “upper bottom”, “normal” Data such as “water level (height of water from the bottom of each season)” is included. The detailed attributes of “river shape” and “river bed state” are the same as the data used in the conventional simulation system for predicting the river water level. In addition, the “vegetation state” is determined by the vegetation registration unit 15 based on a photograph taken directly from above by an aircraft or the like (a photograph directly above) and a photograph taken from a diagonal direction (an oblique photograph). It is stored in advance in the information DB 4.

Here, with reference to FIG. 4, acquisition of “vegetation state” data processed by the vegetation registration unit 15 and registration in the local information DB 4 will be described in detail. First, a plurality of predetermined monitoring areas are photographed from directly above and obliquely to obtain a top-up photograph and a diagonal photograph in advance, and the photographing position of these photographs and the photographing posture (photographing angle) of the oblique photographs are determined according to the photographing position / It memorize | stores in attitude | position DB6. Since the position of the top photograph is distorted due to the height of the terrain, it is converted into an ortho image in which the position is correctly arranged in step S1. Note that conversion to an ortho image can be performed by a known method, and if there is an existing ortho image that approximates the shooting time, it may be used, so it is necessary to take a photograph directly above. Absent.
On the other hand, in step S2, the local coordinates corresponding to each pixel of the oblique photograph are calculated with reference to the altitude DB 2 in the same principle as the creation of the ortho image, and the oblique photograph stored in the photographing position / posture DB 6 is taken. Based on the position and orientation, the local coordinates are associated with each pixel coordinate on the oblique photograph and stored in the oblique photograph correspondence DB 7.

  The correspondence between the oblique photograph and the local coordinates will be described in more detail with reference to FIG. In the case of an oblique photograph in which an area including the point P on the ground surface is photographed with a predetermined posture (ie, tilt), if the point on the oblique photograph corresponding to the point P on the ground surface is P ′, the photographing center O and the point Since the point P exists on the line L1 connecting the point O and the point P ′ from P ′, the elevation of the position coordinate on the horizontal plane of the line L1 is obtained with reference to the elevation DB2, respectively. A longitudinal sectional view of the passing position of the line L1 as shown in the lower part is generated. Further, if the point at which the photographing center O is projected on the reference plane (elevation zero) is C, ∠POC can be obtained based on the posture at the time of photographing, and this is determined as the inclination of the line L1 on the vertical plane. . Next, by comparing the obtained longitudinal sectional view and the ∠POC, the position coordinates of the point P are determined, and the position on the oblique photograph can be associated with the two-dimensional position coordinates on the ground surface in this way. Each oblique photograph is stored in the oblique photograph correspondence DB 7 together with the two-dimensional position coordinates of a plurality of positions (including contours) in the photographing range of the photograph. Instead of associating local position coordinates with each pixel coordinate on the oblique photograph, only predetermined position coordinates that are referred to for calculation may be stored. Thereby, the amount of data can be reduced. In addition, the shooting range of oblique photographs is actually complicated due to the undulations of the land, etc., but since it is intended to narrow down the image to be investigated, it is calculated by plane calculation based on the shooting position and viewing angle. What is necessary is just to determine a two-dimensional position coordinate.

Next, in step S3, a representative point of a vegetation area in a predetermined monitoring area is created. As shown in FIG. 6, the representative point is created by inputting a vegetation area determined and determined by the operator on an ortho image obtained from a photograph directly above and a rectangular section circumscribing the vegetation area. For example, one point in the vegetation region is set as a representative point for each of the obtained subsections. The representative point is the average coordinate of the vegetation area in the small section. When the average coordinate is outside the vegetation area (such as when the vegetation area is complicated), the position is shifted to the vegetation area. If there is no vegetation area in the divided subsection, “no representative point” is set.
Although it is important to accurately grasp the vegetation area from the ortho image, for example, when the vegetation area is smaller than the size of the mesh-like section, and when the vegetation area is larger than the section size, If the bedrock is sparsely present and the vegetation is dense, the operator inputs the percentage of vegetation in the vegetation area (%).

And in step S4, the kind of vegetation is determined with reference to the oblique photograph and the vegetation master 8 which were matched with the local coordinate stored in DB7 with diagonal photograph correspondence. The vegetation master 8 stores a correspondence relationship between vegetation states and standard RGB pixel values. The vegetation master is based on oblique photographs of various past locations for each vegetation and its standard vegetation. It can be obtained by acquiring the relationship with the RGB pixel value. For example, as shown in FIG. 7, the vegetation master is “coniferous forest”, “broadleaved forest”, or “turf / grassland” according to a combination of standard RGB values. Etc. are determined and registered in advance. In addition, when it is determined that the stored data of the vegetation master 8 does not match the current situation, as shown in FIG. 4, the standard RGB pixel values are determined using the latest oblique photograph, and the vegetation master 8 is based on the determined values. Can be updated.
In step S4, an oblique photograph in which the coordinates of the representative point are reflected is specified with reference to the oblique photograph correspondence DB, and the RGB pixel value of the representative point on the oblique photograph is measured. When a plurality of oblique photographs are applicable, all are subject to subsequent processing. Then, the RGB pixel value of the representative point is compared with the standard RGB pixel value stored in the vegetation master 8, and the vegetation having the highest similarity is determined to be the vegetation of the representative point.

The similarity of RGB pixel values is obtained by the following calculation.
P = | (MR-R) | + | (MG-G) | + | MB-B) |
Where P: similarity
MR, MG, MB: RGB value of vegetation master
R, G, B: RGB values of representative points In this way, the vegetation of all representative points is determined, and the vegetation having the largest number of hits is determined as the vegetation of the vegetation region. However, if the difference between the first and second similarities is less than 20%, for example, the operator determines the vegetation by inputting an oblique photograph and inputs it.
Subdivisions without representative points are set as “turf / grassland”.
As a result, it is determined whether the “vegetation state” of the vegetation region is a “conifer”, “hardwood”, or “turf / grassland” registered as a vegetation master, and used in subsequent water level estimation processing .

  Further, when the vegetation area is larger than the size of the section and straddles a plurality of sections, the ratio of the area occupied by the vegetation is determined for each section in step S4, and along with the determined vegetation type, the section Is stored in the local information DB 4 in association with When multiple types of vegetation exist in one section, the ratio is also stored for each type. By obtaining such types of vegetation and their proportions for each section, it is possible to calculate an outflow coefficient and a roughness coefficient indicating the ease of water flow, as will be described later.

  The outflow amount / direction calculation unit 11 calculates the outflow amount and direction from each section to the eight sections surrounding the section. The outflow amount includes the outflow amount flowing on the surface (river and soil surface) and the outflow amount flowing through the soil. In order to determine the outflow direction, the outflow amount / direction calculation unit 11 calculates the estimated storage height based on the elevation stored in the elevation DB 2 and the storage amount stored in the calculation result DB 5. ”Is identified as the source section, and the elevations of the eight sections surrounding the source section plus the estimated reservoir height are compared, and water from the source section to the lowest elevation + estimated reservoir height of the eight sections is identified. The second section is identified as the one that flows. If there are multiple sections with the lowest elevation + estimated water storage, spilled water is divided equally. Similarly, the direction of water flow is determined by specifying the third, fourth,... Compartments. As will be described later, the water storage amount is calculated for each section by the stored water amount calculation unit 12 and stored in the local information DB 4. By dividing the water storage amount for each section by the area of the section, Estimated water storage is required.

  FIG. 8 shows such a direction of water flow. In FIG. 8, the numerical value in the section represents the altitude of the section + estimated reservoir height, and was determined based on the elevation + estimated reservoir height. The direction of water flow from each compartment is indicated by arrows. As a result, it is possible to estimate the water flow path, and it is possible to estimate the water flow gradient based on the difference between the elevation viewed in the direction of water flow and the estimated water storage height. The determined outflow direction is stored in the calculation result DB 5 in association with the section.

The outflow amount / direction calculation unit 11 further calculates the outflow amount flowing in the determined direction and stores the result in the calculation result DB 5. As a method for calculating the outflow amount, for example, a rational expression can be used as described below. In addition, several methods are known for calculating the outflow amount, and the calculation of the outflow amount in the present invention is not limited to the method using a rational formula, and the actual observation result in the field is compared with the calculation result, Select an appropriate one from known calculation methods. Further, in the following calculation method, the unit of the outflow amount is m ³ / sec. On the other hand, in the present invention, the outflow amount at a time such as 30 seconds is calculated, so it is necessary to multiply the time.

The rational formula is as follows.
Q = 1 / 3.6 ・ f ・ r ・ A
Q: Outflow (m ³ / sec)
f: Runoff coefficient
r: Rainfall (mm / hour)
A: Basin area (km ² )
The rainfall r is based on actual measurement data and prediction data from the Japan Meteorological Agency and the like stored in the rainfall DB 3.
The runoff coefficient f is a coefficient representing the ease of flow taking into account the characteristics of the land.
For steep mountains, f = 0.75 to 0.9
For rough land and forest, f = 0.5 to 0.75
For flat arable land, f = 0.45 to 0.6
For mountain rivers, f = 0.75 to 0.85
It is.

In one embodiment, the runoff coefficient for hardwood and coniferous forests is set to 0.60, the runoff coefficient for rock mass is set to 0.80, and the runoff coefficient for turf and pasture is set to 0.50. Is corrected based on the simulation result. And in a certain section, for example, when the broad-leaved forest is 20%, the coniferous forest is 5%, the bedrock is 15%, and the turf and pasture is 60%, the outflow coefficient f of the section is the above-mentioned outflow coefficient. When used, it is calculated as follows:
f = 0.60 (broad-leaved forest) × 0.2 + 0.60 (coniferous forest) × 0.05
+0.80 (bedrock) x 0.15 +0.50 (turf, pasture) x 0.6
= 0.57

In addition, depending on the flow velocity, water may flow into a section ahead of the adjacent section within 30 seconds, so the flow rate is also taken into account in determining the outflow amount Q. For example, as shown in FIG. 9, it is conceivable that the water flowing out from the section 1 within 30 seconds passes through the sections 2 to 4 and reaches the section 5, but the equivalent inflow amount to these sections 2 to 5 (That is, the inflow amount for 30 seconds minus the outflow amount) is obtained by the following procedure.
First, the flow velocity of each section is calculated according to Manning's formula, and the time required to pass through each section is calculated from the section size (m) / flow velocity.

Manning's formula is as follows. As understood from the formula, the flow rate depends on the amount of water stored in each section, the width of the route to the adjacent section (river width), and the vegetation condition. It is determined based on the difficulty of flow.
v = 1 / n ・ R ^2/3・ I ^1/2
However, v: Flow velocity (m / sec)
n: Roughness coefficient
R: Diameter depth (flow / junbe)
I: Gradient “Roughness coefficient” represents the ease of water flow, that is, the condition of the river bed and the surface of the ground. In one embodiment of the present invention, the roughness coefficient of the broadleaf forest is 0.60, Set the roughness coefficient of coniferous forest to 0.80, the roughness coefficient of rock mass to 0.02, and the roughness coefficient of turf and pasture to 0.20, and correct these roughness coefficients for each monitoring area based on the simulation results. . And, for example, as illustrated above, in the case of a section of broad-leaved forest 20%, coniferous forest 5%, bedrock 15%, turf / pasture 60%, the roughness coefficient n of the section is When the roughness coefficient of is used, it is calculated as follows.
n = 0.60 (broadleaf forest) x 0.2 + 0.80 (coniferous forest) x 0.05
+ 0.02 (bedrock) x 0.15 + 0.20 (turf, pasture) x 0.6
= 0.283
The “flow product” is a cross-sectional area, and the “junsen” is a length in which water is in contact with the surrounding wall and bottom in the cross section of the water channel. As the gradient used for calculating the flow velocity, the gradient of the virtual water surface at the start of outflow is used.

Then, using the obtained flow velocity, the outflow destination section where the water flowing out from the outflow section 1 arrives in 30 seconds is obtained. In the example of FIG. The amount of water that has flowed out for 30 seconds is calculated as being distributed according to the time required for passage. That is,
Outflow water from section 1 to section 2 = 60m ³ × 8s / 30s = 16m ³
Outflow water from section 1 to section 3 = 60m ³ × 5s / 30s = 10m ³
Outflow water from section 1 to section 4 = 60m ³ × 10s / 30s = 20m ³
Outflow water from section 1 to section 5 = 60m ³ × 7s / 30s = 14m ³
However, the outflow amount to the section 5 is calculated using 7 seconds instead of 8 seconds because the flow time within 30 seconds is only 7 seconds. The outflow amount to the section 5 that is the last outflow destination may be calculated by subtracting the outflow amount to the sections 2 to 4 from the outflow amount 60 m ³ from the section 1.

A section through which a registered river passes is handled as a “river section”. In the “river section”, the runoff coefficient used for calculating the runoff amount is changed to a value indicating the ease of flow, and in the flow velocity calculation, the cross section and roughness coefficient registered as river data are used. In addition, water from sections other than the river section, that is, the “normal section”, flows out to the river section when the river section exists. Therefore, the waterless river is registered until it flows into the river section. Rivers do not overlap with Minakawa.
For example, as shown in FIG. 10, when there are river sections and normal sections, the direction of water outflow is determined according to the altitude + water storage height, and after flowing into the river section, everything flows through the river. To do.

In addition, the rain that has fallen may flow through the soil, in which case it is modeled using a tank model as shown in FIG. The parameters of the tank model are based on the numerical values shown in FIG. 11, but the standard parameters are modified and used by comparing the calculated outflow amount described below with the observed values on site.
The storage height (Si) at time t + Δt of each tank model is obtained from the following calculation formula.
S1 (t + Δt) = (1-β1Δt) ・ S1 (t) −q1 (t) ・ Δt + R
S2 (t + Δt) = (1-β2Δt) ・ S2 (t) −q2 (t) ・ Δt + β1 ・ S1 (t) ・ Δt
S3 (t + Δt) = (1-β3Δt) ・ S3 (t) −q3 (t) ・ Δt + β2 ・ S2 (t) ・ Δt
However, S1 (t), S2 (t), S3 (t): Reservoir height of each tank at time t
β1, β2, β3: Permeation coefficients of permeate outflow holes of each tank
q1 (t), q2 (t), q3 (t): Outflow from the side holes of each tank at time t

Moreover, the outflow amount from the side hole of each tank model is obtained from the following calculation formula.
q1 (t) = α1 {S1 (t) −L1} + α2 {S1 (t) −L2}
q2 (t) = α3 {S2 (t) −L3}
q3 (t) = α4 {S3 (t) −L4}
However, α1, α2, α3, α4: Outflow coefficient of each outflow hole
L1, L2, L3, L4: Height of each outflow hole The amount of outflow obtained by the tank model in this way, that is, the outflow flowing out to the adjacent compartment through the soil, is also used to estimate the river water level of each compartment. Used.

The stored water amount calculation unit 12 performs a simulation calculation on the stored water amount, which is the amount of water remaining in each section, based on the following formula, and stores the result in the calculation result DB 5. In the present invention, the amount of water stored is the amount of water remaining in each section, the amount of inflow is the amount of water flowing into each section from the other sections, and the amount of outflow is the amount of water flowing from each section to the other sections, and the amount of loss. This is the amount of water that does not flow out due to underground penetration or evaporation.
Amount of water stored (the amount of water remaining in each section)
= The amount of stored water at the end of the previous treatment + Increase in the amount of stored water per hour Increase in the amount of stored water per hour = Rainfall per hour + Inflow per hour-Outflow per hour-Loss per hour Unit time is For example, 30 seconds.

The rainfall amount is converted into, for example, a rainfall amount per 30 seconds based on actual measurement data and prediction data from the Japan Meteorological Agency and the like stored in the rainfall DB 3. As described above, the outflow amount from the section to the other section is calculated every 30 seconds by the outflow amount / direction calculation unit 11, and the stored water amount calculation unit 12 uses the outflow amount obtained thereby. . On the other hand, the obtained water storage amount is used in the outflow amount / direction calculation unit 11 to calculate the estimated water storage amount as described above, and is used to calculate the water storage amount of the tank model in the calculation of the outflow amount. . Moreover, it uses in the water level calculating part 13 so that it may demonstrate below.
The calculation of the amount of stored water is not limited to the above, and various known methods can be employed.

FIG. 12 shows the amount of water storage and runoff in each section in relation to precipitation in the section of elevation + estimated storage height shown in FIG. 8 at time points t _i , t _{i + 1} (= It is a schematic explanatory diagram shown every t _i +30 seconds) and t _{i + 2} (= t _{i + 1} +30 seconds). (In Fig. 12, t _i = 10: 00: 00) The section shown in the upper row shows the amount of precipitation (mm / 30 seconds) that has fallen in 30 seconds up to each point in time. The result of adding precipitation to the amount of stored water (10 ⁻³ m ³ ) (= reserved amount) up to the previous time is shown for each compartment, and the amount of runoff (10 ^{− 3} m ³ ) is shown together with the arrow of the outflow direction shown in FIG. However, for the sake of simplicity, it is assumed that all precipitation flows to the surface without penetrating underground.

  The water level calculation unit 13 calculates the water level at an observation point such as a river and stores the result in the calculation result DB 5. In the simulation calculation, the water level calculation unit 13 uses the elevation data stored in advance in the elevation DB 2, the rainfall DB 3, and the local information DB 4, the rainfall data and the local information, and the outflow amount calculated by the outflow amount / direction calculation unit 11. Based on this, the river level is calculated. As local information, the crossing shape of the river and the state of the river are used. The river crossing shape and river condition are measured and registered in advance through field surveys. If the field survey is not possible in advance or if there is usually no water flow, the calculation is performed assuming a rectangular channel with a width of Nm.

When the river shape is registered, it is assumed that the shape of the river does not change in one section. For example, assuming that the size of one section is 10 m, a continuous channel with a constant shape of 10 m is assumed, and the amount of runoff To calculate the water level. For example, in the case of the shape shown in FIG. 13, the cross-sectional area of the water flow is trapezoidal,
Bottom bottom = 5m
Top bottom = 5 + 2H / 10 + 2H / 10 = 5 + 2H / 5
Because
Nagareseki = H ^2/5 + 5H
It becomes. Therefore, the volume of water in a 10m channel is
Volume = Nagareseki × running water distance ^{= (H 2/5 + 5H} ) × 10
= 2H ² + 50H
The water level (H) can be obtained by solving an equation that matches the volume of the obtained water channel with the amount of stored water.

  If the river shape is not registered, a rectangular channel with a width of Nm is assumed as described above. Then, similarly to the case where the river shape is registered, the water level can be calculated by solving an equation that makes the volume expression equal to the flow rate. Then, the channel width is adjusted by comparing the calculated value of the water level with the observation result.

  The dangerous basin estimation unit 14 identifies an area where flooding may occur based on the water level. Thereby, an alarm can be issued to such an area. More specifically, the risk basin estimation unit 14 monitors whether the predicted water level calculated every 30 seconds in the monitoring area calculated by the water level calculation unit 13 reaches the dangerous water level of the area stored in the local information DB 4. When the predicted water level reaches the dangerous water level, the time is displayed in the vicinity of the monitored area on the map, and the area is highlighted and displayed in red or the like.

  Since the water level prediction system described in relation to FIGS. 3 to 13 is configured as described above, it is usually a waterless river where water does not flow, and a river from which water suddenly flows due to torrential rain. As described with reference to FIG. 10, the water level can be estimated by estimating the amount of outflow and the direction thereof to other sections for each section obtained by dividing a predetermined area into a mesh shape. In other words, there is no river because the outflow direction and the outflow amount can be estimated by estimating the outflow amount from each section to the adjacent section based on the precipitation and water storage amount of each section. Even in such a case, it is possible to estimate the direction and amount of water flowing, and thus it is possible to estimate whether or not sudden water flow occurs.

Claims (8)

A sediment disaster prediction system that predicts the possibility of a sediment disaster using a computer,
A landslide disaster water level determination unit that determines a landslide disaster water level based on the amount of rain, which is the water level at the upper end of the danger zone where the occurrence of a landslide disaster is expected and indicates that the possibility of a landslide disaster occurring is high;
For each of the dangerous areas, a dangerous area database storing the position of the upper end of the dangerous area and the determined sediment disaster water level in association with the dangerous area;
A water level prediction unit for predicting the water level at a specified point;
A dangerous area stored in the dangerous area database is a warning area determination unit for determining whether or not a dangerous area is likely to cause a sediment disaster;
Obtain the position of the upper end of the dangerous area and the sediment disaster water level from the dangerous area database,
Supplying the position of the acquired upper end to the water level prediction unit as a point where the water level should be predicted;
The predicted water level of the position of the upper end is received from the water level prediction unit, and the received predicted water level is compared with the landslide disaster water level, and when the predicted water level is higher than the landslide disaster water level, the dangerous area is a warning area To determine,
A warning area determination unit configured as follows,
Sediment disaster prediction system characterized by comprising.   The landslide disaster prediction system according to claim 1, wherein the landslide disaster water level determination unit determines the landslide disaster water level based on a rainfall amount when a landslide disaster has actually occurred in the past in the dangerous area. .   The landslide disaster level determination unit according to claim 2, wherein the landslide disaster water level determination unit determines the water level predicted by the water level prediction unit using the rainfall at the time of the occurrence of a past landslide disaster as the landslide disaster water level. Prediction system.   4. The sediment-related disaster water level determination unit determines the sediment-related disaster water level by comparing a predicted water level calculated based on rainfall and a soil rainfall index. 5. Sediment disaster prediction system described in 1.   When the soil rainfall index obtained by the calculation exceeds the soil rainfall index reference value that is a judgment standard for occurrence of a sediment disaster, the sediment disaster water level determination unit uses the rainfall used for calculating the soil rainfall index. 5. The sediment disaster prediction system according to claim 4, wherein the water level predicted by the water level prediction unit is determined as the sediment disaster water level.   6. The sediment-related disaster water level determination unit determines a sediment-related disaster water level for a nearby dangerous area whose soil quality is similar to that of the dangerous area as a sediment-related disaster water level of the dangerous area. The earth and sand disaster prediction system described in the section.   When the warning area determination unit determines that the dangerous area is a warning area, the warning area determination unit further includes a warning information generation unit that generates landslide disaster caution information indicating that a landslide disaster may occur. The earth and sand disaster prediction system according to any one of claims 1 to 6. The water level prediction unit
An altitude database that stores the altitude of each of a plurality of sections obtained by dividing the monitoring target area in association with the sections;
Reservoir height calculating means for calculating the estimated reservoir height based on the rainfall amount and the inflow amount of each of these sections at a predetermined interval;
At a predetermined interval, the sum of the altitude and the estimated reservoir level is calculated for each compartment as a virtual water surface, and the compartment having the lowest virtual water surface among the virtual water surfaces of the plurality of compartments surrounding each compartment is extracted from the central compartment. Outflow direction determining means for determining the outflow direction of water,
An outflow amount calculating means for calculating an outflow amount from each section in the outflow direction of the section based on the water storage amount and the rainfall amount of each section at a predetermined interval;
Water level calculation means for calculating the water level of the path based on the determined outflow direction and amount, and the cross-sectional shape of the water flow path;
The earth and sand disaster prediction system according to any one of claims 1 to 7, characterized by comprising: