JP2004347339A - Landslide prediction apparatus and computer program for landslide prediction - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To relatively easily evaluate the degree of risk of a cliffs (slopes) over a wide area. <P>SOLUTION: This landslide prediction apparatus is provided with an estimated cliff characteristics data acquisition means 4 for acquiring both cliff characteristics data on cliffs which have experienced landslides and estimated cliff characteristics data; a landslide risk evaluation point computation means 5 for computing numerical values to be evaluation criteria for evaluating the risk of landslides by performing multivariate analysis on each geological pattern classified by the geological structures of cliffs; an estimated rainfall condition acquisition means 6 for acquiring rainfall conditions to which it is estimated that the cliffs which have experienced landslides have been exposed; a landslide relational expression deriving means 7 for deriving a landslide relational expression corresponding to each data constellation by performing statistic processing on data constellations having the numerical values to be the evaluation criteria as boundaries for every geological pattern; and a landslide prediction means 8 for predicting landslides by applying each landslide relational expression to each cliff accumulated in a cliff registry database. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

[0001]
TECHNICAL FIELD OF THE INVENTION
The present invention predicts the danger of cliff collapse using a cliff ledger database based on a field survey, a collapse history database including cliff collapse history data, and a rainfall database including precipitation data per unit time at observation points. The present invention relates to a cliff collapse prediction device and a cliff collapse prediction program.
[0002]
[Prior art]
Conventional risk assessment and factor analysis of steep slopes (cliffs) can be broadly divided into analytical methods using slope stability analysis models in the field of geotechnical engineering and empirical methods based on past collapse cases. . The former analytical method can perform accurate evaluation when narrowing down the target slope and performing the analysis.However, when performing evaluation in a relatively large area such as that targeted by administrative agencies, In practice, it is impossible to implement this because it takes a great deal of effort to prepare the ground data and examine the model. On the other hand, the latter empirical method is a method of extracting a cliff (slope) having the same attribute (cliff characteristics) as the past collapse case and judging the degree of danger. Although the risk can be easily evaluated, it is necessary to conduct a site survey on a wide area to be evaluated and fully understand the cliff characteristics of the cliff (slope) in the target area .
[0003]
As a conventional prediction device and method for predicting a cliff (slope) collapse, an AE (acoustic emission) sensor is arranged in the ground to analyze the number and magnitude of AE signals generated along with the precursor of the slope failure. (Refer to JP-A-6-88744, JP-A-9-138288, JP-A-10-307128, etc.), and the concentration of specific ions such as sodium ions and sulfate ions contained in groundwater are periodically measured. When a change in the measured value is observed continuously and it is recognized that there is a sudden rise, it is a precursor to the occurrence of ground displacement due to slope failure such as landslide or surface collapse. (Japanese Patent Application Laid-Open No. 2002-339373), an underground sound from a vibration sensor attached to a tree or the like on a slope (for example, groundwater estimation sound, tree Data from a digital camera, etc., and the slope image (ground surface image) from a digital camera, etc., and predicts the collapse of the slope based on changes in ground sound and changes in the ground surface image. (Japanese Patent Application Laid-Open No. 2002-4294).
[0004]
[Problems to be solved by the invention]
However, in these collapse prediction devices and methods, equipment such as an AE sensor, a groundwater sampling well, a vibration sensor, a camera, and the like must be installed on each of the cliffs (slope) where the collapse prediction is to be performed. Since it is practically impossible to install these devices for all the cliffs (slope) inside, it is practically difficult to predict the collapse of the cliff (slope) in a wide area. .
[0005]
The present invention has been made in view of such circumstances, and there is no need to install equipment such as sensors for each cliff (slope) for which a prediction of collapse is to be performed. Of a cliff collapse prediction device and a cliff collapse prediction program that can relatively easily evaluate the danger of cliff collapse on a wide area of cliffs (slope) without the need for data and ground model maintenance and model review The purpose is to do.
[0006]
[Means for Solving the Problems]
The cliff failure prediction device according to claim 1 of the present invention stores a cliff ledger database in which cliff characteristic data of a cliff investigated by a field survey is accumulated and history data of a cliff collapse in which a cliff collapse actually occurs. And a rainfall database that stores rainfall data per unit time at the observation point.
Estimated cliff characteristic data acquiring means for acquiring, from the data of the cliff ledger database and the data of the collapse history database, cliff characteristic data estimated as cliff characteristic data of a cliff having a cliff collapse accumulated in the collapse history database. And, from the cliff characteristic data of the cliffs in which these cliff collapses have occurred and the data of the cliff ledger database, perform a multivariate analysis on each of the geological patterns classified according to the geological structure of the cliff to reduce the danger of the cliff collapse. Collapse risk evaluation score calculating means for calculating a numerical value serving as a criterion for determining for each quality pattern,
Estimated rainfall condition obtaining means for obtaining rainfall conditions that are estimated to have been exposed to a cliff where a cliff collapse has occurred from the data of the collapse history database and the data of the rainfall database, and the determination criteria for each of the geological patterns. Decay relation formula deriving means for performing statistical processing on each of the data groups divided by the numerical value to derive a decay relation formula corresponding to each of the data groups,
From the data of the cliff ledger database and the data of the rainfall database sequentially updated according to rainfall conditions, for each cliff accumulated in the cliff ledger database, the geological pattern of the cliff and the numerical value serving as the judgment criterion Means for implementing a collapse cliff prediction step of predicting the collapse of each cliff by applying each of the collapse relational expressions according to the above.
[0007]
According to the cliff collapse predicting device of the present invention, it is estimated from the data of the cliff ledger database and the data of the collapse history database that the cliff characteristic data of the cliff in which the cliff collapse accumulated is accumulated in the collapse history database. Obtain cliff characteristic data and perform multivariate analysis on each of the geological patterns classified by the geological structure of the cliff from the cliff characteristic data of the cliff where the cliff collapse occurred and the data of the cliff ledger database. Calculate numerical values that are criteria for judging the danger of cliff collapse for each quality pattern,
Obtain the rainfall conditions estimated that the cliff where the cliff collapse occurred was exposed from the data of the collapse history database and the data of the rainfall database, and with each numerical value serving as the criterion for each geological pattern. Statistical processing is performed on each of the classified data groups to derive a collapse relation formula corresponding to each of the data groups, and from the data of the cliff ledger database and the data of the rainfall database sequentially updated according to the rainfall situation. For each of the cliffs stored in the cliff ledger database, the collapse relation of each cliff is predicted by applying each of the collapse relational expressions according to the geological pattern of the cliff and the numerical value serving as the criterion.
[0008]
Further, when a new cliff collapse occurs and the collapse history database is updated, a so-called self-growth system can be constructed in which numerical values and collapse relational expressions serving as judgment criteria are updated.
[0009]
Further, it is possible to construct a so-called real-time system that outputs prediction and warning of a cliff collapse in real time using rainfall data that is sequentially updated at the time of rainfall.
[0010]
The cliff failure prediction device according to claim 2 of the present invention stores a cliff ledger database in which cliff characteristic data of a cliff investigated by a field survey is accumulated, and history data of a cliff collapse in which a cliff collapse actually occurs. And a rainfall database that stores rainfall data per unit time at the observation point.
Estimated cliff characteristic data acquisition in which data of the cliff ledger database and data of the collapse history database are matched to obtain data estimated as cliff characteristic data of a cliff having a cliff collapse accumulated in the collapse history database. Means, the cliff characteristic data of the cliff in which these cliff collapses have occurred and the data of the cliff ledger database are divided for each geological pattern classified by the geological structure of the cliff, and a multivariate analysis is performed for each data group. A collapse risk evaluation score calculating means for calculating a numerical value serving as a criterion for judging a danger of a cliff collapse for each quality pattern,
Estimated rainfall condition obtaining means for obtaining rainfall conditions estimated to be exposed to a cliff having a cliff collapse by matching the data of the collapse history database with the data of the rainfall database; The rainfall conditions to which the cliffs were exposed and the data of the collapse history database were divided for each of the geological patterns and divided into data groups with the numerical value serving as the criterion as a boundary. Collapse relational expression deriving means for deriving a collapse relational expression corresponding to each data group divided by the numerical value serving as the criterion for each quality pattern,
The data of the cliff ledger database was matched with the data of the rainfall database that is sequentially updated according to the rainfall conditions, and the matched data was classified for each of the geological patterns with the numerical value serving as the criterion as a boundary. The system is characterized by comprising: a collapse cliff predicting means for predicting a collapse of each cliff by applying the collapse relation formula corresponding to the data group to each data group.
[0011]
According to the cliff collapse prediction device of the present invention, by matching the data of the cliff ledger database and the data of the collapse history database, the cliff characteristic data of the cliff where the cliff collapse accumulated in the collapse history database. Estimated data is obtained, and the cliff characteristic data of the cliff in which these cliff collapses have occurred and the data of the cliff ledger database are divided for each geological pattern classified according to the geological structure of the cliff, and each data group is obtained. Calculate the numerical values that are the criteria for judging the danger of cliff collapse by performing multivariate analysis on
By matching the data of the collapse history database with the data of the rainfall database, it is possible to obtain rainfall conditions estimated to have exposed the cliff in which the cliff collapse has occurred, and to obtain the rainfall in which the cliff in which these cliff collapse has occurred has been exposed. The conditions and the data of the collapse history database are divided for each of the geological patterns and divided into respective data groups with the numerical value serving as the criterion as a boundary. Deriving a collapse relation formula corresponding to each of the data groups divided by the numerical value serving as the determination criterion,
The data of the cliff ledger database was matched with the data of the rainfall database that is sequentially updated according to the rainfall conditions, and the matched data was classified for each of the geological patterns with the numerical value serving as the criterion as a boundary. The data is divided into data groups, and the collapse prediction of each cliff is performed by applying each of the collapse relation formulas corresponding to the data groups to each data group.
[0012]
Further, when a new cliff collapse occurs and the collapse history database is updated, a so-called self-growth system can be constructed in which numerical values and collapse relational expressions serving as judgment criteria are updated.
[0013]
Further, it is possible to construct a so-called real-time system that outputs prediction and warning of a cliff collapse in real time using rainfall data that is sequentially updated at the time of rainfall.
[0014]
According to a third aspect of the present invention, there is provided a cliff collapse prediction device, further comprising collapse danger cliff display means for displaying, on a map, a collapse danger cliff predicted to have a danger of collapse by the collapse prediction means.
[0015]
According to the cliff collapse prediction device of the present invention, a collapse danger cliff predicted to have a danger of collapse by the collapse prediction means is displayed on a map. As a result, the collapse danger cliff can be recognized on the map, and a warning or an evacuation recommendation can be made.
[0016]
The cliff collapse predicting device according to claim 4 of the present invention displays a disaster-risk area, which is a range in which it is estimated that earth and sand will flow out of the collapse-risk cliff displayed on the map by the collapse-risk cliff display means. Characterized by the following:
[0017]
According to the cliff failure prediction device of the present invention, a disaster risky area, which is a range in which earth and sand are estimated to flow due to the collapse at the collapse risk cliff displayed on the map by the collapse risk cliff display means, is displayed. Thereby, the stricken area can be recognized on the map, and a warning or an evacuation recommendation can be made.
[0018]
The cliff collapse prediction device according to claim 5 of the present invention includes a disaster-risk building extraction means for extracting a structure such as a building overlapping the disaster-risk area displayed by the disaster-risk area display means as a disaster-risk building. It is characterized by the following.
[0019]
According to the cliff collapse prediction device of the present invention, a structure such as a building overlapping the disaster-risk area displayed by the disaster-risk area display means is extracted as a disaster-risk building. If the extracted disaster-risk buildings are displayed on a map, the disaster-risk buildings can be recognized on the map, and a warning or evacuation recommendation can be made.
[0020]
The computer program for predicting a cliff collapse according to claim 6 of the present invention comprises: a cliff ledger database in which cliff characteristic data of a cliff investigated by a field survey are accumulated; and a history data of a cliff collapse in which a cliff collapse actually occurs. It has an accumulated collapse history database and a rainfall database in which rainfall data per unit time at each observation point is accumulated,
An estimated cliff characteristic data acquiring step of acquiring cliff characteristic data estimated as cliff characteristic data of a cliff having a cliff collapse accumulated in the collapse history database from the data of the cliff ledger database and the data of the collapse history database And, from the cliff characteristic data of the cliffs in which these cliff collapses have occurred and the data of the cliff ledger database, perform a multivariate analysis on each of the geological patterns classified according to the geological structure of the cliff to reduce the danger of the cliff collapse. A collapse risk evaluation score calculating step of calculating a numerical value serving as a criterion for determining for each quality pattern,
An estimated rainfall condition obtaining step of obtaining a rainfall condition estimated to have been exposed to a cliff in which a cliff collapse has occurred from the data of the collapse history database and the data of the rainfall database; and A decay relation formula deriving step of performing a statistical process on each of the data groups divided by the numerical value to derive a decay relation formula corresponding to each of the data groups,
From the data of the cliff ledger database and the data of the rainfall database sequentially updated according to rainfall conditions, for each cliff accumulated in the cliff ledger database, the geological pattern of the cliff and the numerical value serving as the judgment criterion And a collapse-cliff prediction step of predicting the collapse of each cliff by applying each of the above-mentioned collapse-relation relational expressions.
[0021]
According to the computer program for predicting a cliff collapse according to the present invention, it is estimated from the data of the cliff ledger database and the data of the collapse history database to be cliff characteristic data of the cliff in which the cliff collapse accumulated in the collapse history database. From the cliff characteristic data of the cliff where the cliff collapse occurred and the data of the cliff ledger database, multivariate analysis was performed for each geological pattern classified according to the geological structure of the cliff. Calculate the numerical value that becomes the criterion for judging the danger of cliff collapse for each quality pattern,
Obtain the rainfall conditions estimated that the cliff where the cliff collapse occurred was exposed from the data of the collapse history database and the data of the rainfall database, and with each numerical value serving as the criterion for each geological pattern. Statistical processing is performed on each of the classified data groups to derive a collapse relation formula corresponding to each of the data groups, and from the data of the cliff ledger database and the data of the rainfall database sequentially updated according to the rainfall situation. For each cliff accumulated in the cliff ledger database, the collapse relation of each cliff is predicted by applying each of the collapse relational expressions according to the geological pattern of the cliff and the numerical value serving as the criterion.
[0022]
Further, when a new cliff collapse occurs and the collapse history database is updated, a so-called self-growth system can be constructed in which numerical values and collapse relational expressions serving as judgment criteria are updated.
[0023]
Further, it is possible to construct a so-called real-time system that outputs prediction and warning of a cliff collapse in real time using rainfall data that is sequentially updated at the time of rainfall.
[0024]
The computer program for predicting a cliff collapse according to claim 7 of the present invention comprises: a cliff ledger database in which cliff characteristic data of a cliff investigated by a field survey are accumulated; and historical data of a cliff collapse in which a cliff collapse actually occurs. It has an accumulated collapse history database and a rainfall database in which rainfall data per unit time at the observation point is accumulated,
Estimated cliff characteristic data acquisition in which data of the cliff ledger database and data of the collapse history database are matched to obtain data estimated as cliff characteristic data of a cliff having a cliff collapse accumulated in the collapse history database. The process and the cliff characteristic data of the cliff where the cliff collapse occurred and the data of the cliff ledger database are divided into each geological pattern classified by the geological structure of the cliff, and a multivariate analysis is performed for each data group. A collapse risk evaluation score calculation step of calculating a numerical value serving as a criterion for judging a danger of a cliff collapse for each quality pattern,
Estimated rainfall condition obtaining step of obtaining rainfall conditions estimated to have been exposed to the cliff where the cliff collapse has occurred by matching the data of the collapse history database and the data of the rainfall database, and these cliff collapses have occurred The rainfall conditions to which the cliffs were exposed and the data of the collapse history database were divided into the respective geological patterns and divided into data groups with the numerical value serving as the judgment criterion as a boundary. Collapse relational expression deriving step of deriving a collapse relational expression corresponding to each data group divided by the numerical value serving as the criterion for each quality pattern,
The data of the cliff ledger database and the data of the rainfall database that are sequentially updated according to the rainfall conditions are matched, and the matched data is converted into a data group bordered by the numerical value serving as the criterion for each geological pattern. Each of the data groups is subjected to a collapse cliff prediction step of performing a collapse prediction of each cliff by applying each of the collapse relation formulas corresponding to the data group.
[0025]
According to the computer program for predicting a cliff collapse according to the present invention, the cliff characteristics of the cliff in which the cliff collapse accumulated in the collapse history database are obtained by matching the data of the cliff ledger database with the data of the collapse history database. The data estimated as data is obtained, and the cliff characteristic data of the cliff in which these cliff collapses have occurred and the data of the cliff ledger database are divided for each geological pattern classified according to the geological structure of the cliff, and A multi-variate analysis is performed on the data group to calculate a numerical value that is a criterion for judging the danger of a cliff collapse for each quality pattern,
By matching the data of the collapse history database with the data of the rainfall database, it is possible to obtain rainfall conditions estimated to have exposed the cliff in which the cliff collapse has occurred, and to obtain the rainfall in which the cliff in which these cliff collapse has occurred has been exposed. The conditions and the data of the collapse history database are divided for each of the geological patterns and divided into data groups with the numerical value serving as the criterion as a boundary. Deriving a decay relation formula corresponding to each of the data groups divided by the numerical value serving as the criterion,
The data of the cliff ledger database and the data of the rainfall database that are sequentially updated according to the rainfall conditions are matched, and the matched data is converted into a data group bordered by the numerical value serving as the criterion for each geological pattern. For each of the data groups, the collapse relation of each cliff is predicted by applying each of the collapse relation formulas corresponding to the data group.
[0026]
Further, when a new cliff collapse occurs and the collapse history database is updated, a so-called self-growth system can be constructed in which numerical values and collapse relational expressions serving as judgment criteria are updated.
[0027]
Further, it is possible to construct a so-called real-time system that outputs prediction and warning of a cliff collapse in real time using rainfall data that is sequentially updated at the time of rainfall.
[0028]
A computer program for predicting a cliff collapse according to claim 8 of the present invention executes a collapse danger cliff display step of displaying a collapse danger cliff predicted to have a danger of collapse in the collapse prediction step on a map.
[0029]
According to the computer program for predicting a cliff collapse according to the present invention, the danger of collapse is predicted on the map by the collapse prediction step. As a result, the collapse danger cliff can be recognized on the map, and a warning or an evacuation recommendation can be made.
[0030]
The computer program for predicting a cliff collapse according to claim 9 of the present invention is a disaster-risk area that is a range in which earth and sand are estimated to flow out of the collapse-risk cliff displayed on a map in the collapse-risk cliff display step due to collapse. Is displayed, and a warning or an evacuation recommendation can be made.
[0031]
According to the computer program for predicting a cliff collapse according to the claims of the present invention, the disaster risk is a range in which the earth and sand are estimated to flow out due to the collapse to the collapse danger cliff displayed on the map in the collapse danger cliff display step. Display the area. Thereby, the disaster risk area can be recognized on the map, and a warning or an evacuation recommendation can be made.
[0032]
The computer program for predicting a cliff collapse according to claim 10 of the present invention includes a disaster-risk building extraction step of extracting, as a disaster-risk building, a structure such as a building overlapping the disaster-risk area displayed in the disaster-risk area display step. Execute.
[0033]
According to the computer program for predicting a cliff collapse according to the present invention, a structure such as a building overlapping the disaster-risk area displayed in the disaster-risk area display step is extracted as a disaster-risk building. If the extracted disaster-risk buildings are displayed on a map, the disaster-risk buildings can be recognized on the map.
[0034]
BEST MODE FOR CARRYING OUT THE INVENTION
Hereinafter, a cliff collapse prediction device and a cliff collapse prediction computer program stored in the cliff collapse prediction device according to an embodiment of the present invention will be described with reference to the drawings.
[0035]
The cliff collapse prediction device according to the present embodiment includes a computer, a storage device for storing programs and data, and an input / output device such as a CRT for displaying information, a keyboard for inputting data, and the like. Have.
[0036]
In the storage device, a cliff ledger database (cliff ledger DB) 1 in which cliff characteristic data of a cliff investigated by a field survey are accumulated, and history data of a cliff collapse in which a cliff collapse actually occurred are accumulated. A collapse history database (collapse history DB) 2 and a rainfall database (rainfall DB) 3 in which rainfall data per unit time at observation points are accumulated.
[0037]
As shown in FIG. 1A, the cliff ledger database 1 includes a unique number (ID) assigned to each of the cliffs surveyed in the field in a relatively wide area, and a position of the cliff assigned the ID. A large set of data consisting of information, a geological pattern in which the cliff is classified according to the geological structure, and a collapse factor composed of a plurality of (10 in this example, shown in FIG. 1B) characteristic values causing the collapse. It is a set. Specific items of the characteristic values are shown in FIG. Each item is classified into levels (categories) as shown in FIG. 7, and parameters corresponding to these levels are input to the cliff ledger database 1. In the geological pattern item, for example, parameters paired with four geological patterns as shown in FIG. 4 are input.
[0038]
Incidentally, the geological pattern shown in FIG. 4 is disclosed by the “Yokohama City Cliff Risk Assessment Committee” as a representative geological structure of Yokohama City, Kanagawa Prefecture. Pattern 1 is formed of soft soil, and the collapse mode is a so-called Slump type (arc slide) in which the entire slope collapses, and pattern 2 has soft soil on the upper portion of the hard base, and only the upper portion collapses in the collapse mode. The pattern 3 has a so-called Slide type (surface slip) form, the pattern 3 is entirely formed of a hard base, and the collapse form has a so-called Fall type (weathering fall) form in which the surface falls due to cracks or weathering. Has a so-called Slide type (surface layer slip) in which the topsoil covers the surface of the hard base as a whole and the topsoil slides down.
[0039]
The original data of the cliff ledger database 1 is a "cliff actual situation survey table" collected over many years by a field survey of the City of Yokohama, and this is paper-based data, but GIS (Geographic Information System) And is linked to map information by position information.
[0040]
As shown in FIG. 2, the collapse history database 2 includes a serial number (ID) of the cliff where the collapse actually occurred, position information of the cliff to which the serial number (ID) is assigned, and the serial number (ID). It is a collection of many sets of data consisting of the collapse date of the cliff and the collapse time. The original material of the collapse history database is paper-based data, but is digitized in accordance with GIS and is linked to map information by position information. Each time a cliff collapse occurs, the data is additionally input in a GIS-compliant format using the data addition interface. The date of the collapse is input separately for the year, month, and day for convenience of data processing.
[0041]
As shown in FIG. 3, the rainfall database 3 includes an observation point number (ID) having an observation facility (for example, a fire station or a fire branch office) for continuously observing rainfall per hour, and the observation point (ID). ), A large number of sets of data including the position information of the observation facility marked with), the current latest one-hour rainfall, and the current date and time. The rainfall database 3 is automatically created based on the data sent from the observation equipment, but may be created by manual input. In addition, the rainfall database is stored as history data at every date and time (every hour). It should be noted that the current date is input by dividing it into year, month, and day for the convenience of data processing. The database may store additional information such as the observatory name of the observation point (ID).
[0042]
In the cliff collapse prediction device and the cliff collapse prediction computer program according to the present embodiment, processing of three stages (three phases (PHASE)) is roughly performed. The first stage is a stage of analyzing and analyzing the relationship between the predisposition of the cliff and the collapse, and the second stage is a stage of analyzing and analyzing the relationship between the trigger for the collapse of the cliff (rainfall in this example) and the collapse. The third stage is the stage of actually predicting the collapse of a cliff during rainfall.
[0043]
In order to execute the first step, the cliff collapse prediction device accumulates in the collapse history database by matching the data of the cliff ledger database 1 and the data of the collapse history database 2 as shown in FIG. Estimated cliff characteristic data acquiring means 4 for acquiring data estimated as cliff characteristic data of a cliff in which a cliff collapse has occurred, and cliff characteristic data of the cliff in which a cliff collapse has occurred and data of the cliff ledger database, Divide each geological pattern classified according to the geological structure of cliffs, perform multivariate analysis on each data group, and calculate numerical values that are criteria for judging the danger of cliff collapse for each geological pattern And a collapse risk evaluation score calculating means 5.
[0044]
In order to execute the second step, as shown in FIG. 5, the collapse prediction device matches the data of the collapse history database 2 with the data of the rainfall database 3 to expose the cliff where the cliff collapse has occurred. Estimated rainfall condition acquiring means 6 for acquiring the rainfall condition estimated to have been performed, the rainfall condition to which these cliffs in which the cliff collapse has been exposed and the data of the collapse history database are divided for each of the geological patterns, and the judgment is made. Divided into data groups with reference numerical values as boundaries, subjected to statistical processing on each data group, and for each quality pattern for each quality pattern, And a collapse relation deriving means 7 for deriving a corresponding collapse relation equation.
[0045]
In order to execute the third step, as shown in FIG. 5, the collapse prediction device matches the data of the cliff ledger database 1 with the data of the rainfall database 3 which is sequentially updated according to the rainfall condition. The divided data is divided into data groups for each of the geological patterns and divided by the numerical value serving as the criterion, and the respective collapse relation formulas corresponding to the data groups are applied to each data group. And a collapse cliff predicting means 8 for predicting the collapse of each cliff.
[0046]
In order to further enhance the utilization of the result of the third step, the collapse prediction device displays the collapse danger cliff on which the danger of collapse is predicted by the collapse prediction means on a map as shown in FIG. Danger cliff display means 9 and a danger area display means for displaying a danger area which is a range in which earth and sand are estimated to flow out of the collapse danger cliff displayed on the map by the collapse danger display means 9 due to the collapse. And a disaster-risk building extraction means 11 for extracting structures such as buildings overlapping the disaster-risk area displayed by the disaster-risk area display means 10 as disaster-risk buildings.
[0047]
Hereinafter, the operation of the cliff collapse prediction device according to the present embodiment will be described with reference to flowcharts or explanatory diagrams shown in FIGS. The operation of the cliff collapse prediction device is mainly based on the operation of a cliff collapse prediction computer program stored in a storage device attached to the cliff collapse prediction device.
[0048]
The flowchart shown in FIG. 6 shows a processing procedure in the first stage (PHASE1). The cliff ledger database 1 and the collapse history database 2 are matched (Step 1 (described as S1 in the figure; the same applies hereinafter)). In the matching in step 1, the data of the cliff ledger database 1 within a predetermined range (distance) with respect to the cliff collapse position as cliff characteristic data of the cliff in which the cliff collapse occurred in the collapse history database 2 is performed. This is performed by estimating the characteristic data.
[0049]
Next, the database after the matching is divided for each geological pattern stored in the cliff database 1 (step 2). A multivariate analysis is performed on each data group divided for each geological pattern as described above (step 3). The multivariate analysis performed here is performed as follows.
[0050]
That is, the tendency of the items of the database matched by the quantification theory II (class 2) of the multivariate analysis was analyzed. The attributes of the cliff database include attributes that are difficult to express quantitatively. Numerical theory (for example, see "Quantification-Theory and Method", published by Asakura Shoten) by Tomio Hayashi for multivariate analysis including attributes for which such quantitative representation is difficult. The class II (category 2) model of the numerical theory is a type of multiple regression analysis that analyzes a qualitative value “objective variable” from a qualitative value “explanatory variable”. Qualitative explanation items ("explanatory variables") that affect qualitative external criteria ("objective variables") are quantitatively clarified as weighting factors ("category quantities"). In addition, the classification of the external criterion is determined from the sum of the weighting factors (“sample score”) of the individual (“sample”), and the accuracy rate (“discriminative predictive value”) of the determination result indicates the accuracy of the analysis. Used as a guide. In the case of the cliff collapse prediction device and the cliff collapse prediction computer program according to the present embodiment, the external standard (“objective variable”) is used as the collapse history of the collapse history database 2 (including the matched cliff ledger database 1). Are classified into two categories of collapse and non-collapse, and the explanatory item (“explanatory variable”) is a factor of the ten items (see FIG. 1 (b)) shown in the cliff ledger database 1.
[0051]
FIG. 7 shows the analysis result based on the quantification theory II (Category 2) in the form of a table. Note that the table displayed here is for the pattern 1 of the four geological patterns shown in FIG. 4, and for the other geological patterns 2 to 4, the numerical values are different, but a table of the configuration of similar items is shown. Is obtained as an analysis result, but its display is omitted.
[0052]
In the table, "cause name (item)" indicates the ten collapse factors recorded in the cliff database 1, and "level (category)" indicates a qualitative level that is an "explanatory variable". Is displayed, the number of cliffs included in each level is displayed in “cliff number”, the “category quantity” of each level is displayed in “weight coefficient (category quantity)”, and the “range (range)” Indicates the range from the upper limit to the lower limit of the "category quantity" of each level, and the "partial correlation coefficient" indicates the partial correlation coefficient, which is the contribution ratio for decay / non-decay, which is the "objective variable" Is displayed.
[0053]
The “weighting factor (“ category quantity ”)” of each factor indicates that the larger the value is on the positive side, the more contribution to the occurrence of cliff collapse. “Range” indicates the influence of each factor on an external standard (“objective variable”), and indicates that a factor with a larger range contributes to collapse. The "partial correlation coefficient" indicates that a factor having a larger value contributes to collapse.
[0054]
FIG. 8 shows a frequency distribution diagram in which “sample scores” formed of the sum of weighting factors (“category quantity”) are totaled for each of the external criteria (“objective variable”), the presence or absence of collapse. A graph of the cumulative relative frequency was created based on FIG. 8, and a discriminative midpoint was obtained at the intersection of the cumulative graph (step 4). This discriminative midpoint represents the “numerical value used as a criterion for determining the danger of cliff collapse” in the present invention. In the example shown in FIG. 8, the discriminative midpoint was -0.06. A relatively high accuracy of 85.1% was obtained in the discriminant predictive value using this discriminative midpoint. That is, at the cliff of the geological pattern 1, the “sample score” composed of the sum of the “category quantities” obtained using the above-described 10 factors exceeds 8% with a probability of collapse of 85.1% with respect to the one that exceeds −0.06. It means that the presence or absence can be determined.
[0055]
The flowchart shown in FIG. 9 shows the processing procedure in the second stage (PHASE2). By matching the collapse history database 2 and the rainfall database 3, a rainfall condition that is presumed to have been exposed to the cliff where the cliff collapse has occurred is acquired (step 5). In this matching, the rainfall observation point very close to the collapse point stored in the cliff collapse history database 2 is extracted from the rainfall database 3 with the hourly rainfall three days before the collapse time (72 hours before) from the collapse time, and FIG. The intermediate database 14 as shown in FIG. 1 is created, and the total of the hourly rainfall at the collapse time and the rainfall during the three days before the collapse is calculated for each observation point number (ID) based on the database 14. This is performed by creating a database 15 as shown in FIG.
[0056]
The cliff ledger database 1 is matched with the database 15 thus matched (step 6). The matching is based on the position information of the observation point number (ID) in the database 15, estimating the data of the cliff ledger database 1 within a predetermined range as the cliff characteristics of the observation point and merging the data with the database 15. It is performed by
[0057]
The database matched in this step is divided for each geological pattern, and further divided above and below a numerical value serving as a criterion (step 7). Statistical processing is performed on each of the divided data groups to derive a collapse relation formula for predicting the risk of collapse for each data group (step 8). In this example, an approximate expression composed of a linear expression is calculated as a collapse relation expression.
[0058]
FIG. 12 plots the data of the database in the geological pattern 1, divides the data into two data groups above and below the reference value, and performs statistical processing on each of the data groups. 7 shows a graph in which the following collapse relational expressions a and b are written. In the case of FIG. 12, an approximate expression composed of a linear expression is calculated from the plotted points, and the linear expression is translated, and the linear expression that becomes the lowest line so as to include all the plotted points is included. Is calculated to derive the collapse relational expressions a and b.
[0059]
Collapse relational expression a is a collapse relational expression derived from data in which a cliff greater than or equal to a criterion in geological pattern 1 has collapsed, and collapse relational expression b is equal to or less than a numerical criterion in geological pattern 1. The following shows the collapse relations derived from the data of the cliff collapse. Incidentally, the collapse relational expression a is used when predicting the danger of collapse at a cliff which is considered to be easily collapsed, and the collapse relational expression b is used when predicting the danger of collapse at a cliff which is considered difficult to collapse.
[0060]
The flowchart shown in FIG. 13 shows the processing procedure in the third stage (PHASE3). The third stage shows a stage of predicting the collapse of a cliff recorded in the cliff ledger database 1 when the rainfall state is actually reached. The cliff ledger database 1 is matched with the database 15 shown in FIG. 11 obtained from the rainfall database 3 which is sequentially updated in the rainy state (step 9). The matching is performed by combining the data of the cliff ledger database 1 within a predetermined range with respect to the observation point (ID) of the database 15 with the database 15.
[0061]
The data of the matched database is divided into data groups for each of the geological patterns and bounded by the numerical value serving as the criterion (step 10). The collapse prediction is performed for each cliff by applying the collapse relation formula derived in the second stage to each divided data group (step 11). Collapse prediction is performed for each cliff recorded in the cliff ledger database 1 based on whether there is a danger of collapse.
[0062]
For example, as shown in FIG. 14A, a cliff (collapse dangerous cliff) K (thick line in the figure) determined to be at risk of collapse is extracted and displayed on a map by GIS (step 12). When the items extracted in each step are displayed on a map by GIS, it is preferable to display a collapse danger cliff in units of polygons of the cliff.
[0063]
As shown in FIG. 14B, a disaster risk area is calculated for the collapse risk cliff K, and the damage risk area H (solid thin line in the figure) is displayed on a map by GIS (step 13). The “disaster danger zone” refers to the area (area) where sediment flows below the cliff when the cliff collapses, and the size (distance below the cliff) depends on the geological pattern of the cliff and the type of collapse (slip type, Although it changes depending on characteristics such as a falling type, if a geological pattern or the like is specified, it can be recognized as a range obtained by multiplying the height of the cliff by a predetermined magnification (distance below the cliff). Therefore, by referring to the cliff characteristics and the like recorded in the cliff ledger database 1 and the like for the extracted collapse danger cliff, the distance under the cliff, which is the disaster risk zone H, can be calculated for each of the collapse danger cliffs K. , Can be displayed on a map.
[0064]
As shown in FIGS. 14A and 14B, among the buildings T (rectangular figures in the figure) displayed on the map, a building overlapping with the disaster-risk area H displayed on the map is referred to as a dangerous building U ( It is extracted as a hatched rectangular figure in the figure (step 14). In the GIS, since information such as the building T is stored as a part of the skeletal map data, if the collapse danger cliff K and the disaster danger area H are specified, the dangerous building U can be easily extracted. .
[0065]
Based on the map illustrated in FIG. 14 created by the procedures of Steps 9 to 14, the administrative organization or the like gives evacuation advice to the residents in the stricken area H of the collapse danger cliff K (especially in the vicinity of the dangerous building U). And implement administrative services such as implementing and proposing cliff (slope) repair work from a long-term perspective.
[0066]
The above operation is executed by the cliff collapse prediction computer program stored in the present apparatus, as described above, and the cliff collapse prediction computer program includes instructions for executing the respective steps, and the like. It is used by being recorded on a storage device such as a hard disk attached to the computer. Before storage in the computer, the data is stored in a storage medium such as a CD-ROM and provided to the user.
[0067]
In addition, the estimated cliff characteristic data obtaining means includes, based on the data of the cliff ledger database and the data of the collapse history database, estimation of cliff characteristic data of a cliff having a cliff collapse accumulated in the collapse history database. It may acquire cliff characteristic data.
[0068]
The collapse risk evaluation score calculating means includes a multivariate analysis for each geological pattern classified by the geological structure of the cliff from the cliff characteristic data of the cliff in which the cliff collapse has occurred and the data of the cliff ledger database. And a numerical value serving as a criterion for determining the danger of cliff collapse may be calculated for each quality pattern.
[0069]
The estimated rainfall condition acquiring means may acquire a rainfall condition from which it is estimated that a cliff having a cliff collapse has been exposed from the data of the collapse history database and the data of the rainfall database.
[0070]
As the collapse relation formula deriving means, for each of the geological patterns, statistical processing is performed on each of the data groups divided by the numerical value serving as the criterion, and a collapse relation formula corresponding to each of the data groups is obtained. It may be derived.
[0071]
As the cliff predicting means, based on the data of the cliff ledger database and the data of the rainfall database that is sequentially updated in accordance with the rainfall condition, for each cliff accumulated in the cliff ledger database, In addition, each of the collapse relation formulas may be applied in accordance with the numerical value serving as the determination criterion to predict the collapse of each cliff.
[0072]
In addition, as a cliff collapse predicting means, an operation of obtaining a geological pattern of a specific cliff and a numerical value serving as a criterion from a database without performing database matching and applying each of the above-described collapse relational expressions to predict a danger of collapse. May be repeated to identify (extract) a collapse danger cliff at risk of collapse.
[0073]
In the present cliff failure prediction device, the four geological patterns are divided into one numerical value as a criterion, and the processing is performed on eight data groups. The number can be appropriately selected. When the data group is increased by increasing the number of numerical values serving as judgment criteria, it is expected that the accuracy of the collapse relational expression is improved and the prediction accuracy of the risk of collapse is improved.
[0074]
【The invention's effect】
As described above, according to the cliff collapse prediction device and the cliff collapse prediction computer program of the present invention, the following effects can be obtained.
[0075]
(1) A cliff ledger database in which cliff characteristic data of cliffs surveyed by field surveys are accumulated, a collapse history database in which historical data of cliff collapses in which a cliff collapse actually occurred, and a unit at each observation point Since the rainfall database in which rainfall data per hour is accumulated is used, there is no need to install any equipment or equipment such as sensors for each cliff (slope) for which the prediction of collapse is to be performed. There is no need to maintain a variety of terrain and ground data or study models.
[0076]
(2) Estimated cliff characteristic data estimated as cliff characteristic data of a cliff having a cliff collapse accumulated in the collapse history database is acquired from the data of the cliff ledger database and the data of the collapse history database. From the cliff characteristic data of the cliff in which the cliff collapse has occurred and the data of the cliff ledger database, multivariate analysis is performed on each of the geological patterns classified by the geological structure of the cliff to determine the danger of the cliff collapse. A numerical value serving as a criterion can be calculated for each quality pattern, and a rainfall condition estimated to have been exposed to a cliff where a cliff collapse has occurred is obtained from the data of the collapse history database and the data of the rainfall database, Each of the geological patterns is subjected to statistical processing for each data group divided by the numerical value serving as the criterion, It is possible to derive a collapse relation formula corresponding to each of the above, from the data of the cliff ledger database and the data of the rainfall database sequentially updated according to the rainfall situation, for each cliff accumulated in the cliff ledger database In addition, it is possible to predict the collapse of each cliff by applying each of the collapse relational expressions according to the geological pattern of the cliff and the numerical value serving as the judgment criterion, and thus it is relatively simple for a wide area cliff (slope). The risk can be evaluated.
[0077]
(3) Matching the data of the cliff ledger database with the data of the collapse history database to obtain data estimated as cliff characteristic data of the cliff in which the cliff collapse has occurred, accumulated in the collapse history database, The cliff characteristic data of the cliff in which the cliff collapse occurred and the data of the cliff ledger database were divided into each geological pattern classified according to the geological structure of the cliff, and each data group was subjected to multivariate analysis to perform the cliff collapse. It is possible to calculate a numerical value serving as a criterion for judging the danger of each quality pattern for each region, and to estimate that a cliff in which a cliff collapse has occurred has been exposed by matching the data of the collapse history database with the data of the rainfall database. Rainfall conditions are obtained, and the rainfall conditions to which the cliffs where these cliff collapses have been exposed and the data of the collapse history database are obtained. Are divided into data groups with the numerical value serving as the criterion as a boundary, and statistical processing is performed on each data group, and the numerical value serving as the criterion is determined for each geological pattern. A destruction relational expression corresponding to each of the data groups divided in the above manner can be derived, and the data of the cliff ledger database and the data of the rainfall database that is sequentially updated according to the rainfall situation are matched. Each of the geological patterns is divided into data groups divided by the numerical value serving as the criterion, and the respective collapse relation formulas corresponding to the data groups are applied to the respective data groups, and each cliff relation is applied. Collapse prediction can be performed, and the risk can be relatively easily evaluated for wide cliffs (slope).
[0078]
(4) When a new cliff collapse occurs and the collapse history database is updated, it is possible to construct a so-called self-growth system in which numerical values and collapse relational expressions serving as criteria are updated.
[0079]
(5) It is possible to construct a so-called real-time system that outputs prediction and warning of a cliff collapse in real time using rainfall data that is sequentially updated at the time of rainfall.
[0080]
(6) Cliffs predicted to be in danger of collapse can be displayed on a map.
[0081]
(7) It is possible to display a disaster-risk area where it is estimated that earth and sand will flow out of the collapse danger cliff displayed on the map due to the collapse.
[0082]
(8) A building overlapping with the disaster risk area can be extracted as a disaster risk building.
[Brief description of the drawings]
FIG. 1 is a diagram showing an example of a cliff ledger database used in a cliff collapse prediction device according to an embodiment of the present invention and a list of causes of collapse.
FIG. 2 is a diagram showing an example of a collapse history database used in the cliff collapse prediction device according to the embodiment of the present invention.
FIG. 3 is a diagram showing an example of a rainfall database used in the cliff collapse prediction device according to the embodiment of the present invention.
FIG. 4 is a diagram showing an example of a geological pattern stored in a cliff ledger database used in the cliff collapse prediction device according to the embodiment of the present invention.
FIG. 5 is a block diagram illustrating an overall configuration of a cliff collapse prediction device according to an embodiment of the present invention.
FIG. 6 is a flowchart showing a processing procedure by the cliff collapse prediction device according to the embodiment of the present invention.
FIG. 7 is a diagram showing a result of a multivariate analysis performed by the cliff collapse prediction device according to the embodiment of the present invention.
FIG. 8 is a diagram showing a result of a multivariate analysis performed by the cliff collapse prediction device according to the embodiment of the present invention.
FIG. 9 is a flowchart showing a processing procedure by the cliff collapse prediction device according to the embodiment of the present invention.
FIG. 10 is a diagram showing an example of a database created by the cliff collapse prediction device according to the embodiment of the present invention.
FIG. 11 is a diagram showing an example of a database created by the cliff collapse prediction device according to the embodiment of the present invention.
FIG. 12 is a diagram showing an example of a collapse relational expression obtained by the cliff collapse prediction device according to the embodiment of the present invention.
FIG. 13 is a flowchart illustrating a processing procedure performed by the cliff collapse prediction device according to the embodiment of the present invention.
FIG. 14 is a diagram showing an example of a map obtained by the cliff collapse prediction device according to the embodiment of the present invention.
[Explanation of symbols]
1 cliff account database
2 Collapse history database
3 rainfall database
4 Estimated cliff characteristic data acquisition means
5 Collapse risk evaluation score calculation means
6 Estimated rainfall condition acquisition means
7 Collapse relation formula derivation means
8 Collapse cliff prediction means
9 Collapse danger cliff display means
10 Damaged area display means
11 Dangerous building extraction means

Claims (10)

A cliff ledger database that stores cliff characteristic data of cliffs surveyed by field surveys, a collapse history database that stores historical data of cliff collapse that actually occurred, and rainfall per unit time at observation points With a rainfall database in which amount data is accumulated,
Estimated cliff characteristic data acquiring means for acquiring, from the data of the cliff ledger database and the data of the collapse history database, cliff characteristic data estimated as cliff characteristic data of a cliff having a cliff collapse accumulated in the collapse history database. And, from the cliff characteristic data of the cliffs in which these cliff collapses have occurred and the data of the cliff ledger database, perform a multivariate analysis on each of the geological patterns classified according to the geological structure of the cliff to reduce the danger of the cliff collapse. Collapse risk evaluation score calculating means for calculating a numerical value serving as a criterion for determining for each quality pattern,
Estimated rainfall condition obtaining means for obtaining rainfall conditions that are estimated to have been exposed to a cliff where a cliff collapse has occurred from the data of the collapse history database and the data of the rainfall database, and the determination criteria for each of the geological patterns. Decay relation formula deriving means for performing statistical processing on each of the data groups divided by the numerical value to derive a decay relation formula corresponding to each of the data groups,
From the data of the cliff ledger database and the data of the rainfall database sequentially updated according to rainfall conditions, for each cliff accumulated in the cliff ledger database, the geological pattern of the cliff and the numerical value serving as the judgment criterion Collapse cliff prediction means for predicting the collapse of each cliff by applying each of the collapse relations according to
A cliff collapse prediction device comprising: A cliff ledger database that stores cliff characteristic data of cliffs surveyed by field surveys, a collapse history database that stores historical data of cliff collapse that actually occurred, and rainfall per unit time at observation points With a rainfall database in which amount data is accumulated,
Estimated cliff characteristic data acquisition in which data of the cliff ledger database and data of the collapse history database are matched to obtain data estimated as cliff characteristic data of a cliff having a cliff collapse accumulated in the collapse history database. Means, the cliff characteristic data of the cliff in which these cliff collapses have occurred and the data of the cliff ledger database are divided for each geological pattern classified by the geological structure of the cliff, and a multivariate analysis is performed for each data group. A collapse risk evaluation score calculating means for calculating a numerical value serving as a criterion for judging a danger of a cliff collapse for each quality pattern,
Estimated rainfall condition obtaining means for obtaining rainfall conditions estimated to be exposed to a cliff having a cliff collapse by matching the data of the collapse history database with the data of the rainfall database; The rainfall conditions to which the cliffs were exposed and the data of the collapse history database were divided for each of the geological patterns and divided into data groups with the numerical value serving as the criterion as a boundary. Collapse relational expression deriving means for deriving a collapse relational expression corresponding to each data group divided by the numerical value serving as the criterion for each quality pattern,
The data of the cliff ledger database was matched with the data of the rainfall database that is sequentially updated according to the rainfall conditions, and the matched data was classified for each of the geological patterns with the numerical value serving as the criterion as a boundary. Dividing into data groups, applying each of the collapse relations corresponding to the data group to each data group, collapse cliff prediction means for predicting the collapse of each cliff,
A cliff collapse prediction device comprising: The cliff collapse prediction device according to claim 1 or 2, further comprising collapse danger cliff display means for displaying, on a map, a collapse danger cliff whose danger of collapse is predicted by the collapse prediction means. Disaster danger area display means for displaying a disaster danger area, which is a range in which earth and sand are estimated to flow out due to collapse with respect to the collapse danger cliff displayed on the map by the collapse danger display means, The cliff collapse prediction device according to claim 3. 5. The cliff collapse prediction device according to claim 4, further comprising a disaster risk building extraction means for extracting a structure such as a building overlapping the disaster risk area displayed by the disaster risk area display means as a disaster risk building. . A cliff ledger database that stores cliff characteristic data of cliffs surveyed by field surveys, a collapse history database that stores historical data of cliff collapse that actually occurred, and rainfall per unit time at observation points With a rainfall database in which amount data is accumulated,
An estimated cliff characteristic data acquiring step of acquiring cliff characteristic data estimated as cliff characteristic data of a cliff having a cliff collapse accumulated in the collapse history database from the data of the cliff ledger database and the data of the collapse history database And, from the cliff characteristic data of the cliffs in which these cliff collapses have occurred and the data of the cliff ledger database, perform a multivariate analysis on each of the geological patterns classified according to the geological structure of the cliff to reduce the danger of the cliff collapse. A collapse risk evaluation score calculating step of calculating a numerical value serving as a criterion for determining for each quality pattern,
An estimated rainfall condition obtaining step of obtaining a rainfall condition estimated to have been exposed to a cliff in which a cliff collapse has occurred from the data of the collapse history database and the data of the rainfall database; and A decay relation formula deriving step of performing a statistical process on each of the data groups divided by the numerical value to derive a decay relation formula corresponding to each of the data groups,
From the data of the cliff ledger database and the data of the rainfall database sequentially updated according to rainfall conditions, for each cliff accumulated in the cliff ledger database, the geological pattern of the cliff and the numerical value serving as the judgment criterion A collapse cliff prediction step of performing a collapse prediction of each cliff by applying each of the collapse relations according to
A computer program for predicting cliff collapse for executing each process consisting of: A cliff ledger database that stores cliff characteristic data of cliffs surveyed by field surveys, a collapse history database that stores historical data of cliff collapse that actually occurred, and rainfall per unit time at observation points With a rainfall database in which amount data is accumulated,
Estimated cliff characteristic data acquisition in which data of the cliff ledger database and data of the collapse history database are matched to obtain data estimated as cliff characteristic data of a cliff having a cliff collapse accumulated in the collapse history database. The process and the cliff characteristic data of the cliff where the cliff collapse occurred and the data of the cliff ledger database are divided into each geological pattern classified by the geological structure of the cliff, and a multivariate analysis is performed for each data group. A collapse risk evaluation score calculation step of calculating a numerical value serving as a criterion for judging a danger of a cliff collapse for each quality pattern,
Estimated rainfall condition obtaining step of obtaining rainfall conditions estimated to have been exposed to the cliff where the cliff collapse has occurred by matching the data of the collapse history database and the data of the rainfall database, and these cliff collapses have occurred The rainfall conditions to which the cliffs were exposed and the data of the collapse history database were divided for each of the geological patterns and divided into data groups with the numerical value serving as the criterion as a boundary. Collapse relational expression deriving step of deriving a collapse relational expression corresponding to each data group divided by the numerical value serving as the criterion for each quality pattern,
The data of the cliff ledger database and the data of the rainfall database that are sequentially updated according to the rainfall conditions are matched, and the matched data is converted into a data group bordered by the numerical value serving as the criterion for each geological pattern. Dividing, applying a respective collapse relation formula corresponding to the data group to each data group, a collapse cliff prediction step of predicting the collapse of each cliff,
A computer program for predicting cliff collapse for executing each process consisting of: The computer program for predicting a cliff collapse according to claim 6 or 7 for executing a collapse danger cliff display step of displaying on a map a collapse danger cliff predicted to have a danger of collapse in the collapse prediction step. 9. A disaster-risk area display step of displaying a disaster-risk area which is a range in which earth and sand are estimated to flow out of the collapse-risk cliff displayed on the map in the collapse-risk cliff display step. The described cliff collapse prediction computer program. 10. The computer program for predicting a cliff collapse according to claim 9, for executing a disaster-risk building extraction step of extracting, as a disaster-risk building, a structure such as a building overlapping the disaster-risk area displayed in the disaster-risk area display step.

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