Information processing device, information processing method, and program

Abstract

To provide an information processing apparatus configured to implement highly accurate uncertainty assessment in determining a water balance quantified with a distributed water circulation model, for a target area.SOLUTION: An information processing apparatus 1 includes: a sample generation unit 143 which generates two or more sets of sample value groups including sample values of parameters; a sample calculation unit 145 which acquires a sample calculation value on a balance of a water resource by applying each of the sample value groups to a distributed water circulation model configured with a parameter group and formed by modeling a circulation state of the water resource in a predetermined area; and an uncertainty assessment unit 149 which acquires information on uncertainty of a water valance calculation value obtained using the distributed water circulation model on the basis of the sample calculation value. The information processing apparatus can implement uncertainty assessment with high accuracy in determining a water balance quantified using a distributed water circulation model, for a target area.SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] The present invention relates to an information processing device, an information processing method, and a program for acquiring quantities related to the balance of water resources using a distributed water circulation model that reproduces the circulation state of water resources in a spatial unit having a certain spatial extent. [Background technology]

[0002] In recent years, when operating facilities such as factories, it is necessary to be aware of the amount of water resources in spatial units of a certain area, such as each facility or a river basin that includes the facility. For example, the amount of water resources in a target area is monitored, the monitoring results are analyzed, and the analysis results are used in production activities, water resource operation and management, water resource conservation activities, etc. For such activities, it is desirable to make future predictions that take into account the time scale so that effective future plans can be formulated and appropriate operation and management can be carried out.

[0003] That is, conventionally, when production activities are carried out in a factory or the like, regardless of the type of product being produced, large amounts of water resources are used for purposes such as boilers, raw materials, product processing, equipment cleaning, equipment cooling, temperature control equipment cleaning water, etc. Facilities that use water resources include facilities that own wells and draw water from them, and facilities that use tap water or industrial water purchased from the national or local government. In any water intake method, water resources in the river basin surrounding the facility or water resources in the vicinity are used.

[0004] Whether a facility can continue to operate depends on the sustainability of water resources in the basin around the facility. In order to increase the sustainability of water resources, it is important not only to improve the efficiency of water resource use at the facility, but also to develop an operation plan for the facility that minimizes the impact on water resources in the basin around the facility. Specifically, for example, when formulating an operation plan for a facility, it is necessary to pay close attention to various water-related indicators such as river water levels, groundwater levels, and spring water volume. In addition, if the sustainability of water resources in the basin around the facility is estimated to be low, it is necessary to develop an implementation plan for activities such as rice paddy cultivation and forest conservation activities to protect water resources.

[0005] However, it is not easy to accurately grasp the behavior of water in a certain geosphere environment with a certain extent. Water repeats various behaviors such as evaporation, precipitation, river flow, infiltration, groundwater flow, and spring water between various systems such as the atmosphere, the earth's surface, and even underground, and is recharged and stored as groundwater or flows out from the area.

[0006] Therefore, in order to confirm the sustainability of water resource security at the facility level and at the basin level including the facility, it is necessary to monitor the water resource balance within the basin. It is also necessary to properly manage the flow, storage, and fluctuation of water resources at the facility level and at the basin level including the facility (hereinafter referred to as the water balance of the target area).

[0007] However, it is not realistic to grasp all the parameters related to the water balance of the target area at all points in the basin. In particular, when managing quantities that are difficult to directly observe, such as recharge quantities at points that are difficult to access, such as mountainous regions, or groundwater flow, it is difficult to manage them appropriately because it is not possible to obtain observed values ​​for these quantities.

[0008] In light of these problems, water cycle models have traditionally been used to manage water resources. By using water cycle models, it is possible to quantify the water balance in places where direct observation is difficult, making it possible to manage water resources at any location.

[0009] There are two types of water cycle models: centralized models and distributed models (see, for example, Non-Patent Document 1).

[0010] The centralized model has the advantage that it requires a small amount of rainfall data for calculation, is relatively easy to calculate, and is easy to systematize. However, it is difficult to reproduce or predict the water cycle with high accuracy using the centralized model. Therefore, the centralized model is not suitable for the purpose of managing the production capacity of a facility.

[0011] On the other hand, in a distributed model, the target space is divided into fine meshes, and the water circulation can be reproduced according to the physical laws of water circulation in each mesh. Therefore, it is possible to reproduce phenomena close to the real water circulation with a relatively high degree of accuracy. In other words, a distributed model is suitable for the purpose of managing the production capacity of a facility (see Non-Patent Documents 2 and 3). Examples of such distributed water circulation models include those described in Patent Document 1 and Non-Patent Document 4. [Prior art documents] [Patent documents]

[0012] [Patent Document 1] JP 2011-13753 A [Non-patent literature]

[0013] [Non-Patent Document 1] Nissui Consulting Co., Ltd., "Distributed Flood Forecasting Model | Nissui Consulting Co., Ltd. - Towards a Prosperous Future," [online], [Retrieved June 12, 2020], Internet<URL:http: / / www.nissuicon.co.jp / jigyou / kasen / kouzui-yosoku / > [Non-Patent Document 2] Hiroyuki Tosaka, Keiji Kojima, Akio Miki, Takeshi Chino, Development of a 3D Land-Water Simulation Method Coupling Surface and Groundwater Flows, Journal of the Japanese Society of Groundwater Hydrology, 1996, Vol. 38, No. 4, pp. 253-267 [Non-Patent Document 3] Koji Mori, Kazuhiro Tada, Takeshi Sato, Nobuko Kakizawa, Yoshimi Uchiyama, Naohide Yokoyama, Masanobu Yamane, Three-dimensional water circulation model of the Kanagawa Prefecture water source area, Kanagawa Prefectural Environmental Protection Society Report 10 (2013) 215-223. [Non-Patent Document 4] McDonald, Michael G. and Arlen W. Harbaugh. “A modular three-dimensional finite-difference ground-water flow model.” Techniques of water-resources investigations(1984) Summary of the Invention [Problem to be solved by the invention]

[0014] In the distributed water cycle model described above, the amount of data required for calculation is large due to the characteristic of applying parameters to each mesh. In other words, the mesh size is determined by a trade-off with the amount of calculation. In addition, for meshes at points where elements that cannot be directly observed, such as the internal structure of the underground or the spatial distribution of precipitation, estimated values ​​must be applied as the values ​​of the applied parameters. In other words, in a distributed water cycle model, it is impossible to apply values ​​based on actual observation values ​​as the values ​​of all parameters for all meshes. Due to these problems, the water balance of the target area that can be quantified in a distributed water cycle model has uncertainty with respect to the actual water balance.

[0015] By performing calculations using a distributed water cycle model, it is possible to know the quantified water balance at any location. However, in order for business operators to make decisions based on the quantified water balance in this way, it is necessary to perform a highly accurate evaluation of the uncertainty of the quantified water balance (hereinafter sometimes referred to as uncertainty evaluation).

[0016] An object of the present invention is to provide an information processing device, an information processing method, and a program capable of performing uncertainty assessment with high accuracy when obtaining a quantified water balance using a distributed water circulation model. [Means for solving the problem]

[0017] The information processing device of the first invention is an information processing device used to obtain information regarding the uncertainty of a water balance calculation value regarding the balance of water resources obtained based on the values ​​applied to each parameter using a distributed water circulation model configured using a parameter group including two or more parameters and in which the circulation state of water resources in a specified area is modeled, and the information processing device is equipped with a sample creation unit that creates two or more sets of sample value groups including sample values ​​within the range that each parameter can take, such that the sample value of at least one parameter is different from each other, a sample calculation unit that applies each of the two or more sets of sample value groups created by the sample creation unit to the distributed water circulation model to obtain a sample calculation value regarding the balance of water resources, and an uncertainty assessment unit that obtains information regarding the uncertainty of the water balance calculation value obtained using the distributed water circulation model based on the sample calculation values ​​corresponding to each sample value group acquired by the sample calculation unit.

[0018] With this configuration, it is possible to perform a highly accurate uncertainty assessment when obtaining a quantified water balance for a target region using a distributed water cycle model.

[0019] In addition, the information processing device of the second invention, compared to the first invention, further includes an extraction unit that performs a sensitivity analysis on the influence of each parameter on the calculated water balance value and extracts high-influence parameters that have a large influence on the calculated water balance value, and the sample creation unit creates two or more sets of sample values ​​such that the sample values ​​of at least one of the high-influence parameters extracted by the extraction unit are different from each other.

[0020] With this configuration, the uncertainty can be easily evaluated.

[0021] In addition, the information processing device of the third invention is an information processing device in which, compared to the second invention, the extraction unit acquires observation data regarding one or more parameters and the balance of water resources, and sets the value of each parameter corresponding to the acquired observation data and the values ​​of other parameters so that the water balance calculated value corresponds to the acquired observation data regarding the balance of water resources, and a post-change calculation unit changes the value of each parameter set by the parameter setting unit to acquire a post-change calculated value regarding the balance of water resources, and extracts highly impacted parameters based on the post-change calculated value calculated by the post-change calculation unit for each parameter.

[0022] With this configuration, it is possible to reliably extract parameters that are highly sensitive to the calculated water balance value.

[0023] Furthermore, the information processing device of the fourth invention is an information processing device in which, compared to the third invention, the post-change calculation unit changes the value of each parameter set by the parameter setting unit by a predetermined rate to obtain a post-change calculated value, and the extraction unit extracts, for each parameter, if the difference or ratio between the water balance calculated value obtained using the value set by the parameter setting unit and the post-change calculated value is equal to or greater than a predetermined threshold, the parameter is extracted as a high-influence parameter.

[0024] With this configuration, it is possible to reliably extract parameters that are highly sensitive to the calculated water balance value.

[0025] Furthermore, the information processing device of the fifth invention is an information processing device in which, compared to the third invention, the post-change calculation unit changes the value of each parameter set by the parameter setting unit to the maximum and minimum values ​​of the possible range to obtain two post-change calculated values, and the extraction unit extracts each parameter as a high-influence parameter when the difference or ratio between the two post-change calculated values ​​is equal to or greater than a predetermined threshold value.

[0026] With this configuration, it is possible to reliably extract parameters that are highly sensitive to the calculated water balance value.

[0027] In addition, the information processing device of the sixth invention is an information processing device according to any one of the first to fifth inventions, in which the uncertainty assessment unit acquires observation data relating to one or more parameters and the balance of water resources, compares a group of sample values ​​and their corresponding sample estimated values ​​with the observation data to confirm that reproducibility is ensured, and acquires a value relating to the variance of the sample estimated values ​​obtained by performing statistical processing using the sample estimated values ​​for which it has been confirmed that reproducibility is ensured, as information regarding uncertainty.

[0028] With this configuration, uncertainty assessment can be performed with higher accuracy.

[0029] In addition, the information processing device of the seventh invention is an information processing device based on any of the first to sixth inventions, further comprising a calculation unit that uses a distributed water cycle model to obtain a water balance calculation value based on values ​​applied to each parameter, and a component that configures output data by correlating the water balance calculation value obtained by the calculation unit with information regarding uncertainty obtained by the uncertainty assessment unit.

[0030] With this configuration, it is possible to output a water balance calculation value in which the uncertainty is evaluated and quantified.

[0031] In addition, the information processing device of the eighth invention is an information processing device relative to any of the first to seventh inventions, in which the water balance calculation value relates to the flow volume, storage volume, or fluctuations of water resources in a specified area.

[0032] With this configuration, it is possible to obtain the flow volume, storage volume, or fluctuations thereof of a water resource, with uncertainties evaluated and quantified.

[0033] In addition, the information processing device of the ninth invention is an information processing device relative to any of the first to seventh inventions, in which the water balance calculated value relates to the groundwater flow volume, storage volume, or fluctuations thereof in a specified area.

[0034] With this configuration, it is possible to obtain the groundwater flow volume, storage volume, or fluctuations therein, with uncertainties evaluated and quantified.

[0035] Further, an information processing device of the tenth invention is an information processing device according to any one of the first to seventh inventions, wherein the water balance calculation value relates to an amount of groundwater recharge in a predetermined area.

[0036] With this configuration, it is possible to obtain a quantified amount of groundwater recharge with an evaluation of uncertainty. Effect of the Invention

[0037] According to the information processing device of the present invention, when obtaining a water balance quantified using a distributed water circulation model for a target area, it is possible to perform uncertainty assessment with high accuracy. [Brief description of the drawings]

[0038] [Figure 1] FIG. 1 is a diagram for explaining an example of a distributed water circulation model according to the present embodiment. [Diagram 2] Flowchart explaining the use of the distributed water cycle model [Diagram 3] Block diagram of an information processing device according to the present embodiment. [Figure 4] A flowchart showing the flow of uncertainty evaluation of the information processing device. [Diagram 5] A flowchart for explaining a screening evaluation performed by the information processing device [Figure 6] A flowchart for explaining the quantitative evaluation performed by the information processing device [Figure 7] Overview of the computer system according to the above embodiment. [Figure 8] Block diagram of the computer system [Figure 9] 1 is a table showing parameters that were the subject of sensitivity analysis in one embodiment of the present invention and the possible ranges of each parameter. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0039] Hereinafter, an embodiment of an information processing device and the like will be described with reference to the drawings. Note that, in the embodiments, components with the same reference numerals perform similar operations, and therefore repeated description may be omitted.

[0040] The terms used below are generally defined as follows. The meanings of these terms should not always be interpreted as shown here, but rather, when they are explained individually below, they should be interpreted in light of that explanation.

[0041] Water resources include, for example, groundwater, river water, stored water such as lake water, spring water, etc., but are not limited to these.

[0042] An identifier for an item is a character or code that uniquely identifies the item. Codes are, for example, alphanumeric characters or other symbols, but are not limited to these. An identifier is, for example, a code string that does not indicate a specific meaning in itself, but any type of information is acceptable as long as it can identify the corresponding item. In other words, an identifier may be the name of the thing it indicates, or a combination of codes that uniquely correspond to the thing. A combination of two or more pieces of information (for example, attribute values ​​of records recorded in a database) may be used as an identifier.

[0043] The term "acquisition" may include acquiring items input by a user or the like, or may include acquiring information stored in one's own device or another device (which may be information stored in advance or information generated by information processing performed in the device). Acquiring information stored in another device may include acquiring information stored in the other device via an API or the like, or may include acquiring the contents of a document file provided by another device (including the contents of a web page, etc.).

[0044] Also, a so-called machine learning technique may be used to acquire information. The machine learning technique can be used, for example, as follows. That is, a classifier that receives a specific type of input information as an input and outputs a type of output information to be acquired is configured using the machine learning technique. For example, two or more sets of input information and output information are prepared in advance, and the two or more sets of information are provided to a module for configuring a machine learning classifier to configure the classifier, and the configured classifier is stored in a storage unit. The classifier can also be called a learning device. Note that the machine learning technique may be, for example, deep learning, random forest, SVR, or the like. For machine learning, functions in various machine learning frameworks such as fastText, tinySVM, random forest, and TensorFlow, and various existing libraries can be used.

[0045] The classifier is not limited to one obtained by machine learning. The classifier may be, for example, a table showing the correspondence between an input vector based on input information and output information. In this case, output information corresponding to a feature vector based on input information may be obtained from the table, or a vector approximating a feature vector based on input information may be generated using two or more input vectors in the table and parameters for weighting each input vector, and final output information may be obtained using output information and parameters corresponding to each input vector used in the generation. The classifier may also be, for example, a function expressing the relationship between an input vector based on input information and information for generating output information. In this case, for example, information corresponding to a feature vector based on input information may be obtained by a function, and output information may be obtained using the obtained information.

[0046] Outputting information is a concept that includes displaying on a display, projecting using a projector, printing on a printer, outputting sound, transmitting to an external device, storing on a recording medium, passing the processing results to another processing device or another program, etc. Specifically, it includes, for example, enabling information to be displayed on a web page, transmitting as e-mail, etc., and outputting information to be printed.

[0047] The concept of accepting information includes the acceptance of information input from input devices such as a keyboard, mouse, or touch panel, the reception of information transmitted from other devices via wired or wireless communication lines, and the acceptance of information read from recording media such as optical disks, magnetic disks, and semiconductor memories.

[0048] (Embodiment)

[0049] In this embodiment, the information processing device has a function of calculating the flow rate and storage volume fluctuation of water resources using a distributed water circulation model that reproduces the circulation state of water resources in a spatial unit having a certain spatial range. The information processing device extracts only the values ​​of parameters that have a high influence on the flow rate and storage volume fluctuation of water resources from the values ​​of each parameter constituting the distributed water circulation model, and quantitatively calculates the uncertainty that the flow rate of water resources can take by combining variables randomly sampled from the possible ranges of the extracted multiple parameters. Here, when extracting parameters, all parameters may be varied, centering on the parameter setting values ​​of the water circulation model whose reproducibility has been confirmed by comparing the observation data with the calculated values ​​of the water circulation model corresponding to the observation data, and then the flow rate and storage volume fluctuation of water resources may be calculated, and the parameters may be extracted based on the results. For example, if the calculated value has changed by more than a certain ratio, the parameter may be extracted. Also, for example, when the flow rate or storage volume fluctuation of water resources calculated using a water circulation model whose reproducibility has been confirmed using observation data is greater than or equal to a predetermined value (for example, greater than or equal to 1 percent) in the range in which a certain parameter value can be taken, the parameter may be extracted. The information processing device 1 configured in this way will be described below.

[0050] FIG. 1 is a diagram illustrating an example of a distributed water circulation model according to the present embodiment.

[0051] As shown in FIG. 1, in this embodiment, the distributed water circulation model is a model of the circulation state of water resources in a predetermined area including forests, rivers, and other types of land. The two or more predetermined areas can be set, for example, in units of factories or in units of river basins including factories. In other words, the predetermined area can be set in spatial units having a certain spatial extent. In this embodiment, the distributed water circulation model can be, for example, a known model such as "GETFLOWS (registered trademark)" or "MODFLOW", and in the present invention, "GETFLOWS (registered trademark)" is more preferable.

[0052] The distributed water circulation model is configured using a parameter group including two or more parameters. The parameter values ​​are set for each mesh of the distributed water circulation model, making it possible to simulate the circulation state of water resources in the model. The parameters may be set to, for example, those related to elements such as weather, the ground surface, the shallow layer, and the deep layer (for example, precipitation, temperature, topsoil, sediment, permeable layer, etc.), or those related to properties specific to the site. In this embodiment, the parameter group constituting the distributed water circulation model includes parameters such as precipitation, evapotranspiration, temperature, wind speed, sunshine hours, relative humidity, canopy coverage, canopy storage, litter coverage, litter storage, snow accumulation / melt temperature, albedo, bulk transport coefficient, soil evaporation efficiency, groundwater flow, equivalent roughness coefficient, groundwater flow, hydraulic conductivity, effective porosity, solid compressibility, relative permeability, capillary pressure, fluid properties, fluid density, air density, fluid viscosity coefficient, and air viscosity coefficient. The parameters are not limited to these, and may be set in various ways.

[0053] FIG. 2 is a flowchart explaining the use of the distributed water cycle model.

[0054] Using such a distributed water cycle model, users such as businesses can grasp information on the water balance for a target area. A distributed water cycle model can be used, for example, in the following manner. Note that the following is just one example, and various modifications are possible depending on the configuration and use of the distributed water cycle model.

[0055] (Step S1) That is, first, a target area for which information on water balance should be grasped is determined. Specifically, for example, when grasping the water balance around a factory that uses water resources, a recharge area such as a forest upstream of the factory, an area downstream from which the factory takes in groundwater or river water, and an area further downstream thereof can be selected as the target area.

[0056] (Step S2) A survey is carried out on the target area to collect information on the topography, geology, vegetation, etc.

[0057] (Step S3) Using the information collected through the survey, the target area is modeled as a distributed water cycle model.

[0058] (Step S4) The reproducibility of the created distributed water circulation model is confirmed using observational data.

[0059] (Step S5) Repeated surveys and model adjustments are performed to improve reproducibility, resulting in the completion of a usable distributed water cycle model.

[0060] (Step S6) Generate a groundwater table in the distributed water circulation model and calculate the water balance as defined. This calculation can be performed using a computer by a known method depending on the configuration of the distributed water circulation model to be adopted.

[0061] (Step S7) Parameter values ​​such as climate for a predetermined period are input into the distributed water cycle model, and the water balance for that period is calculated.

[0062] (Step S8) This allows production activities and the like for a predetermined period in the target area to be planned using the calculated water balance information (calculated water balance value).

[0063] As described below, in this embodiment, the information processing device 1 can be used to quantify the uncertainty due to the uncertainty of each parameter constituting the distributed water cycle model for the calculated water balance value of the target area obtained by the distributed water cycle model. Such quantification of uncertainty can be performed roughly in two steps, namely, screening evaluation and quantitative evaluation. Then, the user can use the quantified water balance uncertainty to plan the use of water resources so as to reliably ensure the sustainability of the environment, business, etc. Specifically, for example, the risk caused by calculation errors in the water balance can be reduced by using the lowest value of the possible range of the water balance calculation result obtained by quantification.

[0064] In this embodiment, the water balance calculation value relates to the flow rate, storage volume, or fluctuations of water resources in a specified area. More specifically, the water balance calculation value relates to the flow rate, storage volume, or fluctuations of groundwater in a specified area. In other words, the water balance is a concept that refers to, for example, the flow rate, storage volume, and fluctuations of water resources in a target area.

[0065] The flow rate of water resources may include, for example, the recharge rate. Here, recharge mainly refers to the supply of water from the ground surface to the groundwater table, and the recharge rate refers to the amount of water supplied. In other words, in this embodiment, the water balance calculated value relates to the recharge rate of groundwater in a specified area. The water balance calculated value is not limited to this, and the amount of water resources can be defined as appropriate depending on the purpose of grasping the water balance, etc.

[0066] In this embodiment, the calculation of the water balance calculated value can be performed by the calculation unit 151 of the processing unit 140 of the information processing device 1 as described below, but is not limited to this. While the quantification of the uncertainty of the water balance calculated value is performed by the information processing device 1, the calculation of the water balance calculated value may be performed by a device different from the information processing device 1.

[0067] First, the configuration of the information processing device 1 will be described.

[0068] FIG. 3 is a block diagram of information processing device 1 in the present embodiment.

[0069] As shown in Fig. 3, the information processing device 1 includes a storage unit 110, a reception unit 130, a processing unit 140, and an output unit 160. The information processing device 1 is, for example, a personal computer, but is not limited thereto, and may be a server device, a mobile information terminal, or the like. The information processing device 1 may be configured by one server, may be configured by multiple servers operating in cooperation with each other, or may be a computer or the like built into other equipment. It goes without saying that the server may be a so-called cloud server, an ASP server, or the like, and the type is not limited.

[0070] The storage unit 110 includes a model information storage unit 111, an observation data storage unit 113, and an extraction information storage unit 115. The storage unit 110 is preferably a non-volatile recording medium, but may also be a volatile recording medium. Each unit of the storage unit 110 stores, for example, information received by the reception unit 130 and information acquired by the processing unit 140, but the information stored in each unit of the storage unit 110 and the process by which the information is stored are not limited to this. For example, information may be stored in the storage unit 110 via a recording medium, information transmitted via a communication line or the like may be stored in the storage unit 110, or information inputted via an input device may be stored in the storage unit 110.

[0071] The model information storage unit 111 stores information about the distributed water circulation model. The information about the distributed water circulation model includes, for example, information about each mesh that constitutes the model. The values ​​of each parameter applied to each mesh are stored in the model information storage unit 111. In addition, the model information storage unit 111 stores information that can identify the position of each mesh.

[0072] The observation data storage unit 113 stores observation data on various indices of the target area. The observation data is, for example, data obtained by reconnaissance, data obtained from an observation organization, etc., but is not limited to these. Each observation data is stored, for example, in association with an identifier that identifies the corresponding parameter. For example, observation data on precipitation is stored in association with an identifier of a parameter indicating precipitation. Note that observation data on the balance of water resources may be stored in association with an identifier that identifies that it is about the balance of water resources. The observation data is, for example, associated with location information corresponding to an observation point, but is not limited to this. The location information may be information indicating a position, or may be information that can identify a corresponding mesh. In addition, the observation data is, for example, associated with time information indicating the timing of observation, but is not limited to this. The observation data is, for example, not limited to each parameter, such as groundwater level and river flow rate. For example, geospatial information, such as the spatial distribution of spring points, may also be included.

[0073] The extraction information storage unit 115 stores extraction information that identifies parameters extracted by screening evaluation. The extraction information is, for example, but not limited to, an identifier that identifies the extracted parameter (high impact parameter) and an identifier that identifies the distributed water circulation model in association with each other.

[0074] The reception unit 130 receives, for example, information input using an input means connected to the information processing device 1, or information input by an input operation (including, for example, information read by a device) performed using a reading device (for example, a code reader, etc.) connected to the information processing device 1. The received information is stored, for example, in the storage unit 110. The input means that can be used to input information that can be received by the reception unit 130 may be any of a numeric keypad, keyboard, mouse, and menu screen. The reception unit 130 may be realized by a device driver for an input means such as a numeric keypad or keyboard, control software for a menu screen, and the like. The reception unit 130 may receive, for example, information such as voice input by a microphone.

[0075] The processing unit 140 can usually be realized by an MPU, a memory, etc. The processing procedure of the processing unit 140 is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, it may also be realized by hardware (dedicated circuit). The processing unit 140 performs various types of processing. The various types of processing are, for example, processing performed by each part of the processing unit 140 as follows.

[0076] In this embodiment, the processing unit 140 includes an extraction unit 141 , a sample creation unit 143 , a sample calculation unit 145 , an uncertainty assessment unit 149 , a calculation unit 151 , and a configuration unit 155 .

[0077] The extraction unit 141 performs a screening evaluation to extract high influence parameters that have a large influence on the water balance calculation value. The high influence parameters are extracted from a group of parameters used in the distributed water cycle model. The high influence parameters may be one or two or more. The extraction unit 141 stores extraction information capable of identifying the extracted high influence parameters in the extraction information storage unit 115.

[0078] In this embodiment, the screening evaluation is performed by performing a sensitivity analysis on the influence of each parameter on the calculated water balance value. The extraction unit 141 includes a parameter setting unit 1411 and a post-change calculation unit 1412.

[0079] The parameter setting unit 1411 acquires observation data relating to one or more parameters from the observation data storage unit 113. The parameter setting unit 1411 also acquires observation data relating to the balance of water resources from the observation data storage unit 113. The parameter setting unit 1411 can acquire corresponding observation data by using an identifier that identifies a parameter or a water balance.

[0080] The parameter setting unit 1411 sets the value of each parameter so that the water balance calculation value calculated by the processing unit 140 using the value of each parameter corresponding to the acquired observation data and the values ​​of other parameters corresponds to the acquired observation data regarding the balance of water resources. That is, the parameter setting unit 1411 sets the value of each parameter so that it is a reasonable value in light of the observation data. For example, for a parameter with corresponding observation data, a value corresponding to the observation data may be set, and the values ​​of the other parameters may be set within the range of possible values ​​of the parameter using an analysis method such as Newton's method so that the difference between the observation data of the water balance and the water balance calculation value is within a predetermined value.

[0081] The post-change calculation unit 1412 calculates the water balance by changing the parameter value set by the parameter setting unit 1411 for each parameter. The calculated value of the water balance calculated in this way is called a post-change calculated value. In other words, the post-change calculation unit 1412 acquires the post-change calculated value.

[0082] In this embodiment, the post-change calculation unit 1412 acquires a post-change calculated value by changing the parameter value set by the parameter setting unit 1411 by a predetermined rate for each parameter. Specifically, for example, the set value for one parameter is increased or decreased by 1 percent, and the post-change calculated value is calculated using the changed value. Note that the rate of change is not limited to this. Also, a value used for calculating the post-change calculated value may be obtained by adding or subtracting a change amount set in advance for each parameter to or from the set value.

[0083] The extraction unit 141 extracts a highly influential parameter based on the post-change calculated value calculated by the post-change calculation unit 1412 for each parameter. In this embodiment, the extraction unit 141 acquires a water balance calculated value for each parameter using a parameter group set by the parameter setting unit 1411. Then, the extraction unit 141 calculates the difference between the acquired water balance calculated value and the post-change calculated value calculated by the post-change calculation unit 1412. If the calculated difference is equal to or greater than a predetermined threshold, the extraction unit 141 extracts the parameter as a highly influential parameter. Note that the extraction unit 141 may extract the parameter as a highly influential parameter if the ratio between the water balance calculated value and the post-change calculated value is equal to or greater than a predetermined threshold.

[0084] The extraction unit 141 performs sensitivity analysis by varying such values ​​for each parameter, thereby extracting highly influential parameters that have a large effect on the calculated water balance value, that is, parameters that are highly responsive to the water balance. The sensitivity analysis is performed using values ​​that are varied from the values ​​set by the parameter setting unit 1411, so that it is possible to confirm the sensitivity of the parameter in the vicinity of the values ​​that are considered to be likely to be actually taken by the parameter.

[0085] Note that parameters (parameters to be subjected to sensitivity analysis) that are candidates for extraction by the extraction unit 141 may be limited. For example, parameters for which corresponding observation data can be obtained may not be subjected to sensitivity analysis.

[0086] For example, the sensitivity analysis may be performed by assuming an equilibrium state (steady state) in which the effective precipitation obtained from average weather conditions is given, so as to quickly extract parameters that have a large effect on the water balance of the target area. Note that an example of a parameter involved in a state in which weather conditions, which are real phenomena, change (unsteady state) is the effective porosity. The sensitivity of a parameter such as the effective porosity can be evaluated by performing a sensitivity analysis in the unsteady state after the sensitivity of other parameters has been evaluated in the steady state.

[0087] The extraction unit 141 may extract the high-influence parameters, for example, as follows. That is, the post-change calculation unit 1412 may change the value of each parameter set by the parameter setting unit 1411 to the maximum value and the minimum value of the possible range, and obtain two post-change calculated values. In this case, the extraction unit 141 may extract the parameter as a high-influence parameter when the difference or ratio between the two post-change calculated values ​​is equal to or greater than a predetermined threshold value for each parameter. Specifically, for example, when the ratio between the two post-change calculated values ​​is equal to or greater than 1 percent, the parameter can be extracted as a high-influence parameter, but the method of determining using the threshold value or the two post-change calculated values ​​is not limited to this. In addition, for example, a sensitivity analysis may be performed based on the post-change calculated value calculated using either the maximum value or the minimum value that the parameter can take, and the water balance calculated value obtained using the parameter group set by the parameter setting unit 1411.

[0088] The sample creation unit 143 creates two or more sets of sample value groups including sample values ​​within the range that each parameter can take, with the sample value of at least one parameter being different from each other. In this embodiment, the sample creation unit 143 creates two or more sets of sample value groups by making the sample values ​​of at least one of the high influence parameters extracted by the extraction unit 141 different from each other. For example, the sample value groups may be created using a method such as the Monte Carlo method or the Latin Hypercube Sampling method, which are quantitative evaluation methods. More preferably, the Latin Hypercube Sampling method is used to create a sample value group for efficient quantification. The sample value groups may be created by creating a sample value group while changing the value of each parameter, which is set to a different value, for each predetermined difference from the minimum value to the maximum value of the range that the value can take, or various methods may be used.

[0089] The sample calculation unit 145 applies each of the two or more sets of sample values ​​created by the sample creation unit 143 to the distributed water circulation model to calculate the water balance. The calculated value of the water balance calculated in this way is called a sample calculated value. In other words, the sample calculation unit 145 obtains a sample calculated value corresponding to each sample value group. The obtained sample calculated values ​​are stored in the storage unit 110, for example.

[0090] The uncertainty assessment unit 149 acquires information about the uncertainty of the water balance calculated value obtained using the distributed water cycle model, based on the sample calculated value corresponding to each sample value group acquired by the sample calculation unit 145. In other words, the uncertainty assessment unit 149 performs an uncertainty assessment of the water balance calculated value using the sample calculated value. For example, the uncertainty assessment unit 149 calculates a value related to the variance of the sample calculated value by performing statistical processing on two or more sample calculated values ​​acquired using two or more sets of sample value groups, and acquires this as information about the uncertainty of the water balance calculated value. Specifically, for example, the value of the standard deviation calculated by the statistical processing, and the value and confidence interval calculated using the standard deviation may be acquired as information about the uncertainty, but is not limited thereto.

[0091] Here, in this embodiment, the uncertainty assessment unit 149 acquires, for example, one or more parameters and observation data related to the balance of water resources from the observation data storage unit 113. The acquisition of the observation data can be performed in the same manner as the method by the parameter setting unit 1411 described above, but is not limited to this. Then, the uncertainty assessment unit 149 collates the sample value group and the corresponding sample calculated value with the observation data to confirm that the reproducibility with the observation data is ensured for the calculation of the sample calculated value from the sample value group. The determination that the reproducibility is ensured can be performed, for example, by determining that the difference between each observation data and the sample value and the sample calculated value is within a predetermined value, but is not limited to this. Then, the uncertainty assessment unit 149 acquires, as information on uncertainty, a value related to the variation of the sample calculated value obtained by performing statistical processing using the sample calculated value confirmed to have the reproducibility ensured. In this way, by using the sample calculated value confirmed to be reliable by collating it with the observation data, information on the uncertainty of the water balance calculated value can be acquired with higher accuracy.

[0092] When performing uncertainty assessment of the water balance calculation value of the target area, if there are insufficient sample calculation values ​​for the target area using a water cycle model with confirmed reproducibility, it is preferable to generate a group of sample values, i.e., increase the number of samplings, so that a sufficient number of sample calculation values ​​with confirmed reproducibility can be obtained. This makes it possible to obtain highly accurate information on the uncertainty.

[0093] The calculation unit 151 uses a distributed water circulation model to obtain a water balance calculation value based on the values ​​of the applied parameter set.

[0094] The configuration unit 155 configures output data by associating the water balance calculation value acquired by the calculation unit 151 with the information on uncertainty acquired by the uncertainty assessment unit 149. For example, when a 95% confidence interval is acquired by the uncertainty assessment unit 149 as information on uncertainty, the output data configured by the configuration unit 155 provides the calculated water balance calculation value and its 95% confidence interval. The output data is output by the output unit 160 or the like, so that the user of the information processing device 1 can obtain the quantified water balance calculation value including the confidence interval.

[0095] In this embodiment, the output unit 160 outputs information by, for example, displaying the information on a display device provided in the information processing device 1. Note that the output unit 160 may be configured to output information by transmitting the information to another device via a network or the like using, for example, a transmission unit (not shown).

[0096] The output unit 160 may or may not include an output device such as a display, a speaker, etc. The output unit 160 may be realized by driver software for an output device, or a combination of driver software for an output device and the output device, etc.

[0097] Next, the flow of operations for uncertainty evaluation performed by the information processing device 1 will be described.

[0098] FIG. 4 is a flowchart showing the flow of uncertainty evaluation by the information processing device 1.

[0099] As shown in FIG. 4, the information processing device 1 performs a screening evaluation of parameters (step S11), and performs a quantitative evaluation of the uncertainty using the result (step S12), thereby performing an uncertainty evaluation.

[0100] FIG. 5 is a flowchart illustrating the screening evaluation performed by the information processing device 1.

[0101] (Step S21) The processing unit 140 sets a counter i to zero.

[0102] (Step S22) The processing unit 140 increments the counter i.

[0103] (Step S23) The processing unit 140 selects the i-th parameter for which a sensitivity analysis is to be performed.

[0104] (Step S24) The processing unit 140 acquires the range of possible values ​​of the selected parameter. (Step S25) The processing unit 140 performs a sensitivity analysis by varying the values ​​of the selected parameters. (Step S26) The processor 140 judges whether the result of the sensitivity analysis satisfies a predetermined extraction condition. As the extraction condition, for example, as described above, it is possible to set that the ratio of the calculated values ​​of the water balance before and after the fluctuation is equal to or greater than a predetermined threshold. If the extraction condition is satisfied, the process proceeds to step S27, and if not, the process proceeds to step S28.

[0105] (Step S27) The processing unit 140 sets the selected parameter as an extraction target. That is, the processing unit 140 causes the selected parameter to be extracted as a highly influential parameter.

[0106] (Step S28) The processing unit 140 judges whether the counter i is equal to the total number of parameters for which the sensitivity analysis is performed. If it is equal, the screening evaluation ends. If it is not equal, the process returns to step S22.

[0107] FIG. 6 is a flowchart illustrating the quantitative evaluation performed by the information processing device 1.

[0108] (Step S31) The processing unit 140 creates two or more sets of sample values ​​by using, for example, the Latin Hypercube sampling method.

[0109] (Step S32) The processing unit 140 performs an analysis using a distributed water circulation model for each of the created sample value groups, and obtains sample calculated values.

[0110] (Step S33) Processing unit 140 checks whether or not each combination of sample value group and sample estimated value matches the repeatability index based on the observation data.

[0111] (Step S34) The processing unit 140 narrows down the number of sample estimates used for the uncertainty evaluation based on the confirmation result regarding the reproducibility.

[0112] (Step S35) The processor 140 uses the sample estimates that have been confirmed to match the repeatability index to calculate the uncertainty of the water balance estimate, that is, the range that the water balance estimate can take. Then, the quantitative evaluation ends.

[0113] By performing uncertainty assessment in this way, it becomes possible to add information representing uncertainty to the calculated water balance value and output it.

[0114] As described above, in this embodiment, the information processing device 1 can execute an information processing method for acquiring information on the uncertainty of a water balance calculation value regarding the balance of water resources calculated using a distributed water circulation model that is configured using a parameter group including two or more parameters and that models the circulation state of water resources in a predetermined area. That is, the information processing device 1 can execute the steps of: extracting a high-influence parameter having a high influence on the calculation value among the parameters; creating two or more sets of sample value groups including sample values ​​within a range that the parameter can take as the value of each parameter, such that the sample value of at least one parameter is different from each other; applying each of the two or more sets of sample value groups to the distributed water circulation model to acquire a sample calculation value regarding the balance of water resources; and acquiring information on the uncertainty of the water balance calculation value obtained using the distributed water circulation model based on the sample calculation values ​​corresponding to each acquired sample value group.

[0115] Since the information processing device 1 is configured as described above, it is possible to perform uncertainty assessment with high accuracy when obtaining a water balance quantified using a distributed water circulation model for a target area.

[0116] In a distributed water circulation model, the method of performing calculations to evaluate the uncertainty of each of the multiple parameters in each mesh assigned to reproduce phenomena close to the real water circulation would require an enormous amount of calculations. Since large-scale calculations are required, it is necessary to use a computer with high calculation performance at the supercomputer level and operate the computer for a long time.

[0117] In contrast, in this embodiment, a sensitivity analysis is performed on the influence on the water balance calculation value, and the parameters used in the calculation are screened, and the parameters extracted by varying the values ​​of the highly influential parameters are used to perform the uncertainty assessment. Therefore, the amount of calculation required for the uncertainty assessment is small, and the uncertainty assessment can be easily performed. Specifically, the uncertainty assessment can be performed by performing calculation processing for a relatively short time using a device with a simple configuration such as a single personal computer or a small-scale computer network as the information processing device 1, without using a computer such as a so-called supercomputer.

[0118] As described above, the water balance calculation value can be quantified by using the information processing device 1, so that each business operator can obtain the monitoring index (water balance of the target area) at any time and make necessary decisions at the appropriate time to realize water resource management. Specifically, it becomes possible to evaluate the uncertainty in a distributed water circulation model with a large number of variables, so that each business operator can obtain information in which the uncertainty evaluation result is added to the monitoring index (water balance of the target area) in order to make necessary decisions. This allows each business operator to, for example, formulate and adjust production activity plans in consideration of the obtained information on the monitoring index, their own water usage amount, and the water usage situation of the area.

[0119] The processing in this embodiment may be realized by software. This software may be distributed by software download or the like. This software may also be recorded on a recording medium such as a CD-ROM and distributed. The software that realizes the information processing device 1 in this embodiment is a program such as the following. In other words, this program is a program executed on the computer of an information processing device 1 used to obtain information regarding the uncertainty of a water balance calculation value regarding the balance of water resources obtained based on the values ​​applied to each parameter using a distributed water circulation model constructed using a parameter group including two or more parameters and in which the circulation state of water resources in a specified area is modeled, and causes the computer of the information processing device 1 to function as a sample creation unit that creates two or more sets of sample value groups including sample values ​​within the range that each parameter can take, with the sample value of at least one parameter being different from each other, a sample calculation unit that applies each of the two or more sets of sample value groups created by the sample creation unit to the distributed water circulation model to obtain a sample calculation value regarding the balance of water resources, and an uncertainty assessment unit that obtains information regarding the uncertainty of the water balance calculation value obtained using the distributed water circulation model based on the sample calculation values ​​corresponding to each sample value group acquired by the sample calculation unit.

[0120] (others)

[0121] Fig. 7 is a schematic diagram of a computer system 800 according to the above embodiment, and Fig. 8 is a block diagram of the computer system 800.

[0122] These figures show the configuration of a computer that executes the programs described in this specification to realize the information processing device 1 of the above-mentioned embodiment, etc. The above-mentioned embodiment can be realized by computer hardware and a computer program executed thereon.

[0123] Computer system 800 includes a computer 801 including a CD-ROM drive, a keyboard 802, a mouse 803, and a monitor 804.

[0124] In addition to the CD-ROM drive 8012, the computer 801 includes an MPU 8013, a bus 8014 connected to the CD-ROM drive 8012 etc., a ROM 8015 for storing programs such as a boot-up program, a RAM 8016 connected to the MPU 8013 for temporarily storing instructions of application programs and providing a temporary storage space, and a hard disk 8017 for storing application programs, system programs, and data. Although not shown here, the computer 801 may further include a network card for providing a connection to a LAN.

[0125] A program for causing computer system 800 to execute the functions of the information processing device or the like of the above-mentioned embodiments may be stored on CD-ROM 8101, inserted into CD-ROM drive 8012, and further transferred to hard disk 8017. Alternatively, the program may be transmitted to computer 801 via a network (not shown) and stored on hard disk 8017. The program is loaded into RAM 8016 at the time of execution. The program may be loaded directly from CD-ROM 8101 or the network.

[0126] The program does not necessarily include an operating system (OS) or a third party program that causes the computer 801 to execute the functions of the information processing device of the above-mentioned embodiment. The program only needs to include an instruction portion that calls appropriate functions (modules) in a controlled manner to obtain a desired result. How the computer system 800 operates is well known, and a detailed description will be omitted.

[0127] In addition, in the above program, the transmitting step of transmitting information and the receiving step of receiving information do not include processing performed by hardware, such as processing performed by a modem or interface card in the transmitting step (processing that is performed only by hardware).

[0128] The program may be executed by a single computer or a plurality of computers. That is, the program may be executed by a centralized processing or a distributed processing.

[0129] In the above-described embodiments, two or more components present in one device may be physically realized on one medium.

[0130] Furthermore, in the above embodiments, each process (each function) may be realized by centralized processing by a single device (system), or may be realized by distributed processing by multiple devices (in this case, the entire system made up of multiple devices performing distributed processing can be considered as a single "device").

[0131] In addition, in the above embodiments, the transfer of information between each component may be performed, for example, by one component outputting information and the other component receiving information if the two components transferring the information are physically different, or, if the two components transferring the information are physically the same, by shifting from a processing phase corresponding to one component to a processing phase corresponding to the other component.

[0132] In the above embodiment, information related to the processing performed by each component, such as information accepted, acquired, selected, generated, transmitted, or received by each component, and information such as thresholds, formulas, and addresses used by each component in processing, may be temporarily or long-term stored in a recording medium (not shown) even if not specified in the above description. Furthermore, each component or a storage unit (not shown) may store information in the recording medium (not shown). Furthermore, each component or a reading unit (not shown) may read information from the recording medium (not shown).

[0133] In the above embodiment, if the information used by each component, such as the thresholds, addresses, and various setting values ​​used by each component in processing, may be changed by the user, the user may or may not be able to change the information as appropriate, even if not specified in the above description. If the information is changeable by the user, the change may be realized, for example, by a reception unit (not shown) that receives a change instruction from the user, and a change unit (not shown) that changes the information in response to the change instruction. The reception unit (not shown) may receive the change instruction from an input device, may receive information transmitted via a communication line, or may receive information read from a predetermined recording medium.

[0134] The present invention is not limited to the above-described embodiment, and various modifications are possible, and are also included within the scope of the present invention. In addition, some of the components and functions of the above-described embodiment may be omitted.

[0135] The present invention is not limited to applications related to the evaluation of the uncertainty of calculated values ​​related to water resources as in the above embodiment, but can be used to evaluate the uncertainty of various quantities, such as resources, pollutants, or heat quantities. EXAMPLES

[0136] An embodiment of the present invention will be described in detail below by way of an experimental example, but the present invention is not limited thereto.

[0137] In this example, it is shown that by performing parameter screening and quantitative evaluation of the groundwater recharge volume calculated from a water circulation model that reproduces the circulation state of water resources in a spatial unit having a certain range of spatial extent, it is possible to obtain results that evaluate the uncertainty in the groundwater recharge volume.

[0138] (Experimental Method)

[0139] A watershed model of Hakushu Town, Hokuto City, Yamanashi Prefecture (hereinafter referred to as the Southern Alps Water Circulation Model) was used as a water circulation model that reproduces the circulation state of water resources in a spatial unit with a certain spatial extent.

[0140] The parameters that make up the Southern Alps water circulation model are precipitation, evapotranspiration, temperature, wind speed, sunshine duration, relative humidity, albedo, bulk transport coefficient, equivalent roughness coefficient, capillary pressure, relative permeability, permeability coefficient of developed black soil, permeability coefficient of topsoil (Masa), permeability coefficient of mountain slope deposits, permeability coefficient of alluvial gravel, permeability coefficient of debris flow deposits, permeability coefficient of low terrace deposits (tributaries such as Jingu River), permeability coefficient of low terrace deposits (main stream of Kamanashi River), permeability coefficient of alluvial fan deposits, permeability coefficient of mid-terrace deposits, permeability coefficient of Nirasaki mudflow deposits, permeability coefficient of the Momonoki Formation, permeability coefficient of Houou-type granite (weathered / loosening zone), The 35 items are: permeability coefficient of Houou-type granite (fresh part, low permeability part), permeability coefficient of Houou-type granite (fresh part, impermeable part), permeability coefficient of Kaikoma-type granite (weathered and loosened zone), permeability coefficient of Kaikoma-type granite (fresh part), permeability coefficient of Houou-type and Kaikoma-type granite alteration part (weathered and loosened zone), permeability coefficient of Houou-type and Kaikoma-type granite alteration part (fresh part), permeability coefficient of Kaikoma-type granite (weathered and loosened zone), permeability coefficient of Kaikoma-type granite (fresh part), permeability coefficient of Akaishi Group (weathered and loosened zone), permeability coefficient of Akaishi Group (fresh part), permeability coefficient of lineament (shallow part), and permeability coefficient of lineament (deep part). When performing a quantitative evaluation using this model, even if the number of variations in one parameter is assumed to be 10, calculations as many as 10 to the power of 35 would be required. Therefore, it is not realistic to perform a quantitative evaluation.

[0141] Therefore, a sensitivity analysis was performed to screen the many parameters in the Southern Alps water circulation model. The possible ranges of each parameter were set, and calculations were performed using the maximum and minimum values ​​of the possible ranges to evaluate the change in recharge volume.

[0142] FIG. 9 is a table showing parameters that were the subject of sensitivity analysis in one example of this embodiment and the possible ranges of each parameter.

[0143] As shown in Figure 9, if the range between the recharge amount calculated using the Southern Alps water circulation model and the maximum and minimum values ​​of each parameter is 1 percent or more, the parameter is considered to be sensitive (highly influential).

[0144] Then, for parameters that were found to be sensitive by the sensitivity analysis, we performed calculations in which values ​​were randomly assigned approximately 100 times using the Latin hypercube sampling method, and examined the range of possible values ​​that would reproduce the observed values.

[0145] (Experimental Results)

[0146] In the Southern Alps water circulation model, sensitivity analysis revealed that of the 35 parameters, the following eight were determined to be sensitive to groundwater recharge.

[0147] Effective precipitation, Permeability coefficient of topsoil (masa), Permeability coefficient of alluvial gravel, Permeability coefficient of mountain slope deposits, Permeability coefficient of lineament, Permeability coefficient of low permeability parts of Houou type granite, Permeability coefficient of impermeable parts of Houou type granite, Permeability coefficient of Kaikoma type granite

[0148] A quantitative evaluation was performed on these eight parameters using the Latin hypercube sampling method. A total of 100 samples were sampled. For each water cycle model calculated using the sampled parameter group, the reproducibility of the target spatial unit of the water cycle model was confirmed using river flow rate and the location of springs as indicators, and 19 cases were extracted as models for which reproducibility was confirmed. The groundwater recharge amount calculated by the 19 water cycle models was an average of 1798mm, with a standard deviation of 235mm, a maximum value of 2080mm, and a minimum value of 1208mm, which is sufficient for considering uncertainties. [Industrial Applicability]

[0149] As described above, the information processing device of the present invention has the effect of being able to perform uncertainty assessment with high accuracy when obtaining a quantified water balance for a target area using a distributed water circulation model, and is useful as an information processing device, etc. [Explanation of symbols]

[0150] 1. Information processing device 110 Storage area 111 Model information storage section 113 Observation Data Storage Unit 115 Extraction information storage unit 130 Reception 140 Processing section 141 Extraction part 143 Sample Creation Department 145 Sample Calculation Department 149 Uncertainty Assessment Section 151 Calculation Department 155 Components 160 Output section

Claims

1. An information processing device used to obtain information on the uncertainty of a water balance calculation value related to the balance of water resources obtained based on values ​​applied to each parameter using a distributed water circulation model configured using a parameter group including two or more parameters and in which a circulation state of water resources in a predetermined area is modeled, the information processing device comprising: a sample generating unit that generates two or more sets of sample value groups including sample values ​​within a range that each of the parameters can take, with the sample values ​​of at least one parameter being different from each other; a sample calculation unit that applies each of the two or more sets of sample values ​​created by the sample creation unit to the distributed water circulation model to obtain a sample calculation value related to the balance of water resources; and an uncertainty assessment unit that acquires information regarding the uncertainty of the water balance calculation value obtained using the distributed water circulation model based on sample calculation values ​​corresponding to each of the sample value groups acquired by the sample calculation unit.

2. An extraction unit that performs a sensitivity analysis on the influence of each of the parameters on the calculated water balance value to extract a high-influence parameter that has a large influence on the calculated water balance value, The information processing apparatus according to claim 1 , wherein the sample creating section creates the two or more sets of sample values ​​such that sample values ​​of at least one high influence parameter among the high influence parameters extracted by the extracting section are different from each other.

3. The extraction unit is a parameter setting unit that acquires one or more parameters and observation data related to the water balance, and sets values ​​of each parameter and other parameters corresponding to the acquired observation data so that the water balance calculation value corresponds to the acquired observation data related to the water balance; a post-change calculation unit that changes the value of each of the parameters set by the parameter setting unit to obtain a post-change calculated value related to the balance of water resources, The information processing apparatus according to claim 2 , wherein the highly influential parameters are extracted based on post-change calculated values ​​calculated by the post-change calculation unit for the respective parameters.

4. the post-change calculation unit changes the value of each of the parameters set by the parameter setting unit by a predetermined rate to obtain the post-change calculated value; 4. The information processing device according to claim 3, wherein the extraction unit extracts each of the parameters as the highly influential parameter when the difference or ratio between the water balance calculated value obtained using the value set by the parameter setting unit and the post-change calculated value is equal to or greater than a predetermined threshold value.

5. the post-change calculation unit changes the value of each of the parameters set by the parameter setting unit to a maximum value and a minimum value within a possible range, and obtains two of the post-change calculated values; The information processing apparatus according to claim 3 , wherein the extraction unit extracts, for each of the parameters, the parameter as the high-influence parameter when a difference or a ratio between the two post-change calculated values ​​is equal to or greater than a predetermined threshold value.

6. 6. An information processing device as described in any one of claims 1 to 5, wherein the uncertainty assessment unit acquires observation data regarding one or more parameters and the balance of the water resources, compares the group of sample values ​​and the corresponding sample estimated values ​​with the observation data to confirm that reproducibility is ensured, and acquires a value regarding the variance of the sample estimated values ​​obtained by performing statistical processing using the sample estimated values ​​for which reproducibility has been confirmed to be ensured, as information regarding the uncertainty.

7. a calculation unit that uses the distributed water cycle model to obtain the water balance calculation value based on values ​​applied to each parameter; The information processing device according to claim 1 , further comprising a component that configures output data by associating the water balance calculation value acquired by the calculation unit with information regarding the uncertainty acquired by the uncertainty assessment unit.

8. The information processing apparatus according to claim 1 , wherein the water balance calculation value relates to a flow amount, a storage amount, or a fluctuation amount thereof of the water resource in the predetermined area.

9. The information processing apparatus according to claim 1 , wherein the water balance calculation value relates to a groundwater flow rate, a groundwater storage rate, or a fluctuation amount thereof in the predetermined area.

10. The information processing apparatus according to claim 1 , wherein the water balance calculation value relates to an amount of groundwater recharge in the predetermined area.

11. An information processing method for acquiring information on the uncertainty of a water balance calculation value regarding the balance of water resources calculated using a distributed water circulation model that is configured using a parameter group including two or more parameters and that models the circulation state of water resources in a predetermined area, comprising: extracting a high influence parameter having a high influence on the calculated value from among the parameters; creating two or more sets of sample value groups including sample values ​​within a range that each of the parameters can take, such that the sample values ​​of at least one of the parameters are different from each other; applying each of the two or more sets of sample values ​​to the distributed water cycle model to obtain a sample estimate of a water resource balance; and acquiring information regarding the uncertainty of the water balance calculation value obtained using the distributed water cycle model based on sample calculation values ​​corresponding to each of the acquired groups of sample values.

12. A program for an information processing device used to obtain information on the uncertainty of a water balance calculation value related to the balance of water resources obtained based on values ​​applied to each parameter using a distributed water circulation model configured using a parameter group including two or more parameters and in which a circulation state of water resources in a predetermined area is modeled, the program comprising: The computer of the information processing device, a sample generating unit that generates two or more sets of sample value groups including sample values ​​within a range that each of the parameters can take, with the sample values ​​of at least one parameter being different from each other; a sample calculation unit that applies each of the two or more sets of sample values ​​created by the sample creation unit to the distributed water circulation model to obtain a sample calculation value related to the balance of water resources; A program for causing the program to function as an uncertainty assessment unit that acquires information regarding the uncertainty of the water balance calculation value obtained using the distributed water cycle model, based on sample calculation values ​​corresponding to each of the sample value groups acquired by the sample calculation unit.

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