JP4714164B2 - Rainwater inflow prediction device and rainwater inflow prediction method - Google Patents

Description

  The present invention relates to a stormwater inflow prediction technique for a stormwater treatment plant, and more particularly to a stormwater inflow prediction device and a stormwater inflow prediction method for predicting a stormwater inflow that flows into a stormwater treatment plant using rainfall.

  In predicting the amount of rainwater inflow into a storm water treatment plant, the relationship between the measured data or the rainfall data predicted by the Japan Meteorological Agency, etc. and the measured inflow measured at the storm water treatment plant is described using a black box method. However, a method for predicting the amount of inflow of rainwater into the rainwater treatment plant from rain data is common. Non-linear functions such as a method based on a similar pattern search based on past accumulated data (for example, see Patent Document 1), an RRL (load research laboratory) method, a modified RRL method, etc. A method using a genetic algorithm and a method using a genetic algorithm have been proposed.

  The relationship between the amount of rainfall and the amount of rainwater flowing into the sewage treatment plant differs depending on the method of use of the land in the area where the rainfall occurred, so that water absorption and water retention are different between areas paved with asphalt and forest areas. For example, in an urban area where the ground is paved, a cultivated area, and a forest area, the time and amount of inflow from when rain is observed until rainwater flows into the rainwater treatment plant are different. Therefore, a nonlinear function is used to predict the amount of rainwater inflow from the rain data to the storm water treatment plant.

By the way, in general, the rainfall is measured at discrete points as represented by the AMeDAS observation point. Since rainfall is not uniform in all rainfall areas, the rainfall observed at the rainfall observation point does not necessarily represent the rainfall in the area where the rainfall observation point is located. Moreover, the rainfall observation point does not necessarily coincide with the region where rainwater flows into the storm water treatment plant. In other words, the non-linearity of the relationship between the rainfall and the amount of rainwater runoff to the storm water treatment plant is greatly contributed not only by the water absorption characteristics of the ground but also by the discreteness of the observation points. In the modified RRL method, in consideration of the discreteness of such rainfall observation points, the area is divided according to the rainfall observation point such as the rain gauge installation location, and the weight is obtained by multiplying the rainfall by the area of the corresponding area. Considering the rainfall distribution by doing.
JP 2000-087433 A

  However, since the number of rain gauges is limited, the area divided according to the installation location of the rain gauge covers a wider area than the areas divided according to land use such as urban areas and forest areas. Therefore, the relationship between the amount of rainfall and the amount of inflow of rainwater into the storm water treatment needs to be modeled by the skilled person considering the distribution of land use in the area divided according to the rainfall observation point. .

  In recent years, precipitation prediction data based on weather calculations using fine meshes and observation networks using rainfall radars have spread throughout the country. For example, precipitation radar data and precipitation prediction data with meshes of 1 to 5 km on each side are available from the Japan Meteorological Agency. Is provided. However, in the modified RRL method, etc., high-resolution rainfall data that divides the area with a large amount of fine mesh is not assumed, and these data are effectively used as surface data to reduce the inflow to the storm water treatment plant. No prediction method has been established. Furthermore, even if a prediction method using a non-linear function such as a modified RRL method is applied to fine data for each mesh, the relationship between rainfall and inflow to the storm water treatment plant is estimated for each mesh from land use maps. It is necessary to do a lot of study with experience and intuition. In other words, with the method proposed above, even if rainfall data of a region divided into a fine mesh is acquired, the amount of rainwater inflow into the stormwater treatment plant is predicted with high accuracy using those rainfall data. I can't.

  An object of the present invention is to provide a rainwater inflow prediction device and a rainwater inflow prediction method that can predict rainwater inflow to a storm water treatment plant efficiently and with high accuracy using rainfall data of a plurality of regions. And

  According to one aspect of the present invention, (b) a measurement amount database that stores time-series data of rainfall amounts in a plurality of regions and time-series data of total rainwater inflows from the plurality of regions; A function generation unit that generates a linear function for each of a plurality of regions using the time-series data, and (c) a linear function; A rainwater inflow prediction device is provided that includes a prediction unit that predicts rainwater inflow from rainfall input data including at least one of predicted rainfall values and current measured values in a plurality of regions.

  According to another aspect of the present invention, a function generation unit, a prediction unit, a measurement amount database that stores time series data of each rainfall amount and total rainwater inflow amount from a plurality of regions, and a linear function storage A rainwater inflow prediction method for predicting rainwater inflow using a rainwater inflow prediction device having a region, wherein (a) a function generation unit reads time-series data from a measurement database and uses the time-series data. (B) a prediction unit that generates a linear function indicating a relationship between each of a plurality of regions of rainfall and rainwater inflow obtained in the past for each of the plurality of regions and stores the linear function in a linear function storage region; Read out the linear function from the linear function storage area, and use the linear function to generate rainfall that includes at least one of the predicted rainfall value and the current actual measurement value in multiple areas. Rainwater inflow prediction method comprising the steps of predicting the rainwater inflow from the input data is provided.

  ADVANTAGE OF THE INVENTION According to this invention, the rainwater inflow amount prediction apparatus and rainwater inflow amount prediction method which can estimate the rainwater inflow amount to a rainwater treatment plant efficiently and with high precision using the rainfall amount data of a some area | region can be provided.

  Next, first and second embodiments of the present invention will be described with reference to the drawings. In the following description of the drawings, the same or similar parts are denoted by the same or similar reference numerals. Further, the following first and second embodiments exemplify apparatuses and methods for embodying the technical idea of the present invention, and the technical idea of the present invention is a component part. The structure and arrangement are not specified as follows. The technical idea of the present invention can be variously modified within the scope of the claims.

(First embodiment)
As shown in FIG. 1, the rainwater inflow prediction device according to the first embodiment of the present invention has time series data on the amount of rainfall in each of a plurality of regions and the total amount of rainwater inflow from a plurality of regions. Using the measured amount database 30 for storing the series data and the time series data, a linear function indicating a relationship between each of the plurality of areas of the rainfall amount and the rainwater inflow obtained in the past is generated for each of the plurality of areas. A function generation unit 11 and a prediction unit 12 that predicts the amount of rainwater inflow from rainfall input data that includes at least one of predicted rainfall values and current measured values in a plurality of regions using a linear function. . Hereinafter, the target rainwater treatment plant for which the amount of rainwater inflow is predicted is referred to as “target treatment plant”. As shown in FIG. 1, the function generation unit 11 and the prediction unit 12 are included in a central processing unit (CPU) 10.

Here, as shown in FIG. 2, the entire area where the rain flows into the target treatment plant (hereinafter referred to as “inflow area”) is divided into a plurality of N individual areas as mesh 1 to mesh N. (N: an integer of 2 or more). In FIG. 2, the rainfall amounts of meshes 1 to N are f ₁ (t) to f _N (t), respectively, and the total rainwater inflow amount flowing into the rainwater treatment plant is g (t). The rainfall f ₁ (t) to f _N (t) and the total rainwater inflow g (t) are time series data. As a method of dividing the inflow region into N meshes, for example, a method of dividing the inflow region into a square region such as a square or a rectangle having the same shape and size as the mesh 1 to mesh N can be adopted. is there. The stormwater inflow prediction apparatus shown in FIG. 1 generates a linear function indicating the relationship between the individual rainfall amount in each mesh and the total stormwater inflow amount flowing into the target treatment plant for each of the meshes 1 to N. Then, the total rainwater inflow amount to the target treatment plant is predicted using the rain data and the linear function of the meshes 1 to N.

  The function generation unit 11 illustrated in FIG. 1 includes a Fourier transform module 111 and a function calculation module 112. The Fourier transform module 111 converts the time series data of each rainfall amount of the mesh 1 to the mesh N and the time series data of the total amount of rainwater flowing into the target treatment plant at a certain time including the time when the rain event is observed. Fourier transform. The function calculation module 112 uses the Fourier transform values of the time series data of the rainfall amounts of the meshes 1 to N and the Fourier transform values of the time series data of the total rainwater inflow amount to the target treatment plant. For each mesh N, a linear function indicating the relationship between the rainfall amount of each mesh 1 to mesh N and the total rainwater inflow amount flowing into the target treatment plant is calculated as a transfer function. Specifically, a transfer function having as input a Fourier transform value of time series data of rainfall amounts of each of meshes 1 to N, and outputting a Fourier transform value of time series data of total rainwater inflow to the target treatment plant Are calculated for each of meshes 1 to N. Details of the transfer function calculation method will be described later.

  Here, the “rainfall event” is an event in which the rainfall from the start to the end of the rain is continuous, for example, a mesh 1 to mesh in which the rainfall amount that is arbitrarily set or greater is divided into the inflow region An event observed at any of N is defined as a rain event. The time series data of the rainfall amount of each mesh measured at the time when the rainfall event is observed in any of the inflow regions and the time series data of the total rainwater inflow amount to the target treatment plant are the targets of Fourier transform. Accordingly, the time at which rainfall is observed in the same rain event does not necessarily match in meshes 1 to N, and there may be a rain event in which the rainfall amount in any of meshes 1 to N is zero.

  In general, data on the rainfall and the amount of rainwater flowing into the target treatment plant are always acquired, but a certain period of time including the time when the rainfall event is observed is subject to Fourier transform by the Fourier transform module 111. An example of a time subject to Fourier transform (hereinafter referred to as “transformation target time”) will be described below with reference to FIG.

  The vertical axis in FIG. 3 is a measurement value of the rainfall amount of an arbitrary mesh and the total rainwater inflow amount to the target treatment plant, and the horizontal axis is time. In FIG. 3, times t1 to t2 at which the rainfall amount equal to or greater than the measurement value L is measured per unit time are times when a rainfall event is observed with an arbitrary mesh. As shown in FIG. 3, the amount of rainwater flowing into the storm water treatment plant is observed after the time when rainfall starts to be observed. The conversion target time of the inflow region is set based on the earliest time when the rain event was observed in the meshes 1 to N divided into the inflow region and the last time when the rain event was observed. For example, when the first rain event is observed in mesh 1 to mesh N is time t1 shown in FIG. 3 and the last rain event is observed is time t2, mesh 1 to mesh N The conversion target time is set with a time t0 that is a time ts before the time t1 as a start point and a time t3 that is a time te after the time t2 as a finish point. Although the time ts and the time te can be set arbitrarily, the time te is preferably set so that the amount of rainwater inflow resulting from the rain event to the rainwater treatment plant at the time t3 becomes zero. However, the start point and end point of the conversion target time can be arbitrarily set. For example, the conversion target time can be arbitrarily set to 32 hours or 64 hours, and when a plurality of rain events are observed within the set conversion target time, those rain events are regarded as one event. It can be the target of Fourier transform. The rainfall is measured by, for example, a radar rain gauge or a ground rain gauge.

  The prediction unit 12 includes a Fourier transform module 121, a predicted value calculation module 122, and a Fourier inverse transform module 123. The Fourier transform module 121 uses the time series data of the rainfall input data of each of the meshes 1 to N including at least one of the predicted value and the current actual measured value, the time when the rainfall input data is predicted, and the measured time. Fourier transform with. The predicted value calculation module 122 adds the rainfall amounts of the meshes 1 to N generated by the function generation unit 11 and the total rainwater inflows flowing into the target processing field into the Fourier transform rainfall amount input data of the meshes 1 to N, respectively. Multiply each by a linear function that shows the relationship to the quantity. Then, the predicted value calculation module 122 calculates the predicted data as the total rainwater inflow amount in the Fourier space by taking the sum of the multiplication results for the meshes 1 to N. The inverse Fourier transform module 123 obtains time-series data by performing inverse Fourier transform on the prediction data, and calculates the total rainwater inflow amount flowing into the target treatment plant.

  The measured amount database 30 stores time-series data of past rainfall amounts measured in the meshes 1 to N in a plurality of rainfall events in the inflow region and total rainwater inflow amounts to the target treatment plant.

  The rainwater inflow prediction device shown in FIG. 1 further includes a storage device 20, an input device 40, and an output device 50. The storage device 20 includes a data set storage area 201, a data set conversion value storage area 202, a linear function storage area 203, a rainfall amount data storage area 204, a conversion value storage area 205, a predicted data storage area 206, and a predicted inflow amount storage area. 207.

  The data set storage area 201 stores past data that is time-series data of the rainfall amount for each of the meshes 1 to N and the total rainwater inflow amount to the target treatment plant. The data set conversion value storage area 202 stores values obtained by Fourier transform of past data. The linear function storage area 203 stores the linear function generated by the function generation unit 11. The rainfall amount data storage area 204 stores rainfall amount input data for each of the meshes 1 to N including at least one of the predicted rainfall amount and the current actual measurement value. The converted value storage area 205 stores a value obtained by Fourier transforming the rainfall input data. The prediction data storage area 206 stores prediction data calculated by the prediction unit 12. The predicted inflow amount storage area 207 stores the rainwater inflow amount predicted by the prediction unit 12.

  The input device 40 includes a keyboard, a mouse, a light pen, a flexible disk device, or the like. The rainwater inflow prediction executor can specify input data such as rainfall and rainwater inflow from the input device 40. Furthermore, the input device 40 can set parameters such as the Fourier transform conditions and the output data format such as the predicted rainwater inflow, and can also input instructions for executing or canceling the prediction work. Is possible. As the output device 50, a display, a printer, or the like that displays rainfall input data, a predicted rainwater inflow, or the like can be used.

  4A and 4B show examples of actual rainfall data input to the rainwater inflow prediction device. FIG. 4A is an example of the rainfall actual measurement value in an arbitrary mesh A included in the meshes 1 to N. FIG. 4B is an example of the rainfall actual measurement value in an arbitrary mesh B other than the mesh A. It is an example. In addition, FIG. 5 shows an example of the predicted value of the rainfall amount of an arbitrary mesh, and FIG. 6 shows an example of the data of the rainwater inflow amount to the target treatment plant. The horizontal axis in FIGS. 4 to 6 represents the time from the start of measurement or prediction. As shown in FIGS. 4 to 6, the rainfall data and the rainwater inflow data input to the rainwater inflow prediction device are time-series data.

  The inflow amount prediction apparatus shown in FIG. 1 predicts the amount of rainwater inflow to the target treatment plant based on a plurality of rainfall events. Therefore, the function generation unit 11 performs a Fourier transform for converting the time series data of the rainfall and the stormwater inflow into frequency domain data for a plurality of conversion target times each including different rainfall events, and uses the result of the Fourier transform. A linear function is generated for each rainfall event. The accuracy of the predicted value can be increased by using past data of rainfall at different locations and in different locations within the inflow area.

The function generation unit 11 receives a Fourier transform value of the time series data of each rainfall amount in the meshes 1 to N that satisfies the relationship of the following expression (1), and inputs the total rainwater inflow amount to the target treatment plant. Calculate a transfer function whose output is the Fourier transform value of the time-series data:

In Expression (1), Γ _n (ω _k ) is a transfer function in the Fourier space of each of mesh 1 to mesh N at frequency ω _k (n = 1 to N). Here, the frequency ω _k is a sampling frequency of discrete Fourier transform such as fast Fourier transform (FFT) performed by the Fourier transform module 111, and the number of sampling is K times (k = 1 to K, K: Natural number). Further, Γ _n (ω _k ) is a transfer function in the Fourier space of each of the meshes 1 to N at the frequency ω _k , and F _n (ω _k ) is a Fourier transform of the time series data of each rainfall amount in the meshes 1 to N. The value G (ω _k ) is a Fourier transform value of time series data of the total amount of rainwater inflow into the target treatment plant.

  Hereinafter, a method in which the function generation unit 11 generates a linear function indicating the relationship between each rainfall amount in the meshes 1 to N and the total rainwater inflow amount to the target treatment plant will be described with reference to the flowchart shown in FIG. To explain. Below, the case where a linear function is produced | generated is demonstrated exemplarily using the data set which is the time series data measured in each past I rain event 1-rain event I (I: natural number). The “data set” is time-series data of rainfall amounts of meshes 1 to N in each rain event and time-series data of total rainwater inflow amount to the target treatment plant. Here, it is assumed that the data set in each rain event is stored in the data set storage area 201.

  (A) In step S11, the Fourier transform module 111 reads the data set of the rain event 1 to the rain event I from the data set storage area 201. Then, the Fourier transform module 111 performs Fourier transform on the time-series data on the rainfall amount and the time-series data on the amount of rainwater inflow into the target treatment plant included in the read data set. The Fourier transform value of the time series data of the rainfall amount and the rainwater inflow amount is stored in the data set transform value storage area 202.

(B) In step S <b> 12, the function calculation module 112 reads the Fourier transform value of the data set of the rain event 1 to the rain event I from the data set transform value storage area 202. Then, the function calculation module 112 calculates the transfer functions of the meshes 1 to N that satisfy the relationship of the expression (1) using the Fourier transform value of the data set, for example, using the least square method. When the least square method is used, a transfer function Γ _n (ω _k ) that minimizes the residual sum S (ω _k ) with respect to the frequency ω _k expressed by the following equation (2) is calculated (k = 1 to 1). K):

In Expression (2), the Fourier transform value F ⁱ _n (ω _k ) of the time series data of the rainfall amount of each of the meshes 1 to N and the Fourier transform value G of the time series data of the total rainwater inflow amount to the target treatment plant. ^The superscript ^{i of i} (ω _k ) indicates the i-th data set (i = 1 to I). The function calculation module 112 calculates a transfer function Γ _n (ω _k ) that minimizes the residual sum S (ω _k ) for each frequency ω _k using Equation (2). Specifically, Equation (2) is differentiated by the transfer function Γ _v for each frequency to obtain the following Equation (3) (v = 1 to N):

In Formula (3), F ⁱ ₁ , F ⁱ ₂ ,..., F ⁱ _N are the Fourier transform values of the time series data of the respective rainfall amounts in mesh 1 to mesh N of each frequency, and G ⁱ is the frequency of each frequency. The Fourier transform values, Γ ₁ , Γ ₂ ,..., Γ _N of the time series data of the total amount of rainwater inflow into the target treatment plant are transfer functions obtained for each frequency for meshes 1 to N (the same applies hereinafter). .) Then, the function calculation module 112 calculates the transfer functions Γ ₁ , Γ ₂ ,..., Γ _N as solutions of simultaneous equations of the following formula (4) or formula (5):

The transfer functions Γ ₁ , Γ ₂ ,..., Γ _N are calculated for each of the meshes 1 to N for each sampling frequency in the Fourier transform of the time series data. The calculated transfer functions Γ ₁ , Γ ₂ ,..., Γ _N are linear functions indicating the relationship between the respective rainfall amounts of meshes 1 to N and the total rainwater inflow amount to the target treatment plant at each frequency. It is stored in the linear function storage area 203. FIG. 8 shows an example of a transfer function generated by the function generation unit 11.

  Next, a method for predicting the amount of rainwater inflow to the target treatment plant by the rainwater inflow amount prediction apparatus shown in FIG. 1 will be described with reference to the flowchart shown in FIG.

  (A) In step S100 of FIG. 9, the function generation unit 11 sends the time series data of the rainfall amounts measured in the past in the meshes 1 to N included in the inflow region of the target processing field, and the target processing field. I data sets including the time series data of the total rainwater inflow are read from the measured quantity database 30. The read data set is stored in the data set storage area 201.

(B) In step S110, the function generation unit 11 uses the method described with reference to the flowchart shown in FIG. 7, and the transfer function Γ ₁ calculated for each of the meshes 1 to N for each frequency ω _k , Γ ₂ ,..., Γ _N are generated as a linear function indicating the relationship between the rainfall amount of each of meshes 1 to N and the total rainwater inflow amount to the target treatment plant. The generated linear function is stored in the linear function storage area 203.

  (C) In step S120, the prediction unit 12 receives time-series rainfall input data including at least one of the predicted value and the current actual measured value in the meshes 1 to N of the prediction target day via the input device 40. get. The rainfall input data is transferred from the rainfall measurement facility to the rainwater inflow prediction device via a telecommunication line, for example. The acquired rainfall input data is stored in the rainfall data storage area 204.

(D) In step S130, the prediction unit 12 predicts the total amount of rainwater inflow into the target treatment plant using the linear function and rainfall input data generated for each of meshes 1 to N. Specifically, in step S <b> 131, the Fourier transform module 121 reads the rainfall input data of each of the meshes 1 to N from the rainfall data storage area 204. The Fourier transform module 121 performs Fourier transform on the rainfall input data and stores the Fourier transform value in the transform value storage area 205. Next, in step S <b> 132, the predicted value calculation module 122 reads the Fourier transform values and linear functions of the rainfall input data of each of the meshes 1 to N from the converted value storage area 205 and the linear function storage area 203, respectively. The predicted value calculation module 122 uses the Fourier transform values F ₁ , F ₂ ,..., F _N of the rainfall input data of meshes 1 to N to linear functions Γ ₁ , Γ ₂ , ..., Γ _N are multiplied, and the sum of the multiplication results is calculated for meshes 1 to N to calculate prediction data. For example, the frequency ω _k component G (ω _k ) of the total rainwater inflow to the target treatment plant is calculated by the following equation (6):

In Equation (6), F _n (ω _k ) is the frequency ω _k component of the rainfall input data of mesh n, and Γ _n (ω _k ) is the frequency ω _k component of the linear function of mesh n. The calculated prediction data is stored in the prediction data storage area 206. Next, in step S133, the inverse Fourier transform module 123 reads the prediction data from the prediction data storage area 206. The inverse Fourier transform module 123 performs inverse Fourier transform on the prediction data to calculate the amount of rainwater inflow flowing into the target treatment plant. The calculated rainwater inflow amount is stored in the predicted inflow amount storage area 207.

  The rainwater inflow amount of the target treatment plant stored in the predicted inflow amount storage area 207 is transferred to the output device 50. The inflow amount prediction executor can perform operation preparation of the target treatment plant according to the predicted value of the rainwater inflow amount displayed on the output device 50, for example. For example, it is possible to increase the number of personnel arranged in the target treatment plant or adjust the number of pumps to be activated. In addition, the amount of treatment at the target treatment plant can be increased, and a reservoir associated with the treatment plant can be emptied in advance.

In the method described above, an example in which the transfer functions Γ ₁ , Γ ₂ ,..., Γ _N are calculated using the least square method is shown, but the transfer functions Γ ₁ , Γ ₂ , Of course, Γ _N may be calculated.

  In the stormwater inflow prediction device shown in FIG. 1, by introducing a transfer function indicating the relationship between the rainfall and the stormwater inflow, the causal relationship between the rainfall and the stormwater inflow is clarified, and Rainwater inflow can be predicted. Generally, the relationship between the rainfall amount in the inflow region and the rainwater inflow amount to the storm water treatment plant is determined according to the geographical relationship between the inflow region where the rain flows into the storm water treatment plant and the storm water treatment plant. The delay of the inflow to the rainwater treatment plant, which depends on the distance from the inflow region to the rainwater treatment plant, topography, etc., is also considered in the form of being incorporated in the frequency spread of the transfer function.

  Furthermore, since the inflow region is divided into a plurality of meshes and the transfer function between the rainfall amount of each mesh and the rainwater inflow amount to the target treatment plant is used, the rainfall amount of each mesh and the rainwater inflow amount to the target treatment plant are used. The rainfall distribution in the inflow region is accurately reflected, and the amount of rainwater inflow to the target treatment plant can be predicted with high accuracy. Note that the smaller the size of the mesh that divides the inflow region, the more accurately the rainfall distribution in the inflow region is reflected, and the prediction accuracy is higher.

  The series of rainwater inflow prediction operations shown in FIG. 9 can be executed by controlling the rainwater inflow prediction device shown in FIG. 1 by an algorithm program equivalent to FIG. This program may be stored in the storage device 20 constituting the rainwater inflow prediction device shown in FIG. Further, the program is stored in a computer-readable recording medium, and the recording medium is read into the storage device 20 shown in FIG. 1, thereby executing a series of rainwater inflow prediction operations according to the present invention. . Here, the “computer-readable recording medium” refers to a medium capable of recording electronic data such as an external memory device of a computer, a semiconductor memory, a magnetic disk, an optical disk, a magneto-optical disk, and a magnetic tape. means. Specifically, a “flexible disk, CD-ROM, MO disk, etc.” are included in the “computer-readable recording medium”.

  As described above, in the rainwater inflow prediction apparatus shown in FIG. 1, the relationship between the rainfall and the rainwater inflow is expressed in a simple manner by using a transfer function. As a result, it is possible to accurately predict the amount of rainwater flowing into the target treatment plant using the rainfall data. That is, according to the rainwater inflow prediction device according to the first embodiment of the present invention, the causal relationship between the rainfall and the amount of rainwater inflow to the target treatment plant is clearly modeled, and the inflow region of the target treatment plant Provided is a highly accurate rainwater inflow prediction device that can cope with rainfall data that deviates from past accumulated data due to heavy rain that has not been observed.

  Furthermore, according to the rainwater inflow prediction device according to the first embodiment of the present invention, the rainwater inflow to the rainwater treatment plant is efficiently calculated using the rainfall data of the inflow region divided into a plurality of meshes. It can be predicted well and with high accuracy. In other words, it is not necessary to estimate the relationship between the amount of rainfall and the amount of inflow to the storm water treatment plant for each mesh from the land use map, etc., and a great amount of study with experience and intuition is not necessary. The relationship between the time series data of rainfall and the time series data of rainwater inflow to the target treatment plant is based on geometric factors such as the topography of the inflow region and the arrangement and shape of piping from the inflow region to the treatment plant. Therefore, it is not necessary to review the linear function frequently.

(Second Embodiment)
In the rainwater inflow prediction device according to the second embodiment of the present invention, as shown in FIG. 10, the function generation unit 11 further includes an inverse Fourier transform module 113, and the prediction unit 12 includes an integration module 124. However, this is different from FIG. Other configurations are the same as those of the first embodiment shown in FIG.

  The inverse Fourier transform module 113 performs an inverse Fourier transform on the transfer function calculated by the function calculation module 112 to divide the inflow region into N regions, and the rainfall amount of meshes 1 to N and the total amount to the target processing field. N linear functions indicating the relationship with the amount of rainwater inflow are calculated. The integration module 124 performs convolution integration with the rainfall input data of mesh 1 to mesh N including at least one of the predicted rainfall value and the actual measurement value and a linear function to calculate the rainwater inflow amount to the target treatment plant.

  Hereinafter, a method of predicting the amount of rainwater inflow to the target treatment plant by the rainwater inflow amount prediction apparatus shown in FIG. 10 will be described with reference to the flowchart shown in FIG.

  (A) In step S200 of FIG. 11, the function generation unit 11 divides the inflow region of the target treatment field measured in the past, the time series data of the respective rainfall amounts of meshes 1 to N, and meshes 1 to mesh The I data sets including the time series data of the total rainwater inflow amount from N to the target treatment plant are read out from the measured amount database 30. The read data set is stored in the data set storage area 201.

(B) In step S210, the function generation unit 11 generates a linear function indicating the relationship between the rainfall amounts of the meshes 1 to N and the total rainwater inflow amount to the target treatment plant. Specifically, in step S211, similarly to the method described in the first embodiment, transfer functions Γ _v are calculated for meshes 1 to N for each frequency (v = 1 to N), respectively. In step S212, the inverse Fourier transform module 113 reads the transfer functions Γ _v of the meshes 1 to N from the linear function storage area 203, respectively. The inverse Fourier transform module 113 performs inverse Fourier transform on the transfer functions Γ _v for the meshes 1 to N, respectively, and shows the relationship between the respective rainfall amounts of the meshes 1 to N and the total rainwater inflow amount as a response function. Generate a function. FIG. 12 shows an example of a response function that the function generation unit 11 generates as a linear function. The generated linear function is stored in the linear function storage area 203.

  (C) In step S220, the prediction unit 12 uses the input device 40 to divide the inflow area of the target processing area on the prediction target day, and at least one of the predicted value and the current actual measured value of each of the meshes 1 to N The rainfall input data including is acquired. The rainfall input data is stored in the rainfall data storage area 204.

(D) In step S230, the prediction unit 12 predicts the amount of rainwater flowing into the target treatment plant using the rainfall amount input data of mesh 1 to mesh N and the linear function. Specifically, the integration module 124 reads the linear function and the rainfall input data from the linear function storage area 203 and the rainfall data storage area 204, respectively. The integration module 124 performs the convolution integration shown in Expression (7) with the rainfall input data f _n (t) and the response function γ _n (t) for each of the meshes 1 to N, and the total flow that flows into the target processing field. Calculate rainwater inflow g (t):

The calculated total rainwater inflow amount g (t) is stored in the predicted inflow amount storage area 207.

  In the rainwater inflow prediction apparatus shown in FIG. 10, a response function is used as a linear function indicating the relationship between the rainfall amounts of meshes 1 to N and the total rainwater inflow amount to the target treatment plant. Therefore, it is easy to determine when rainfall at a certain time flows into the treatment plant. Others are substantially the same as those in the first embodiment, and redundant description is omitted.

  According to the rainwater inflow prediction device according to the second embodiment of the present invention, the causal relationship between the rainfall and the amount of rainwater inflow to the target treatment plant is clearly modeled, and the past accumulated data such as torrential rain is used. Provided is a highly accurate rainwater inflow prediction device that can cope with deviating rainfall data. Furthermore, according to the rainwater inflow prediction device according to the second embodiment, the relationship between the amount of rainfall and the amount of inflow into the storm water treatment plant using the data of the amount of rainfall in the inflow region divided into a plurality of meshes. There is provided a rainwater inflow prediction device that can predict the rainwater inflow to the storm water treatment plant efficiently and with high accuracy, without having to estimate each of the meshes.

(Other embodiments)
As described above, the present invention has been described according to the first and second embodiments. However, it should not be understood that the description and drawings constituting a part of this disclosure limit the present invention. From this disclosure, various alternative embodiments, examples, and operational techniques will be apparent to those skilled in the art.

  In the description of the first and second embodiments already described, the method for determining the transfer function between the rainfall data and the rainwater inflow data to the target treatment plant has been described. It is possible to generate the transfer function between the data of the river and the data of the amount of increase in the river in the same way, and build a system that predicts the amount of rainwater flowing into the treatment plant that adjusts the water level of the river be able to.

  In addition to determining a transfer function between past rainfall data and rainwater inflow data, a transfer function between past predicted rainfall and rainwater inflow data may be determined. Predicted rainfall is less accurate than the measured amount, but if there is a systematic trend in the method of forecasting rainfall, for example, when the amount of rainfall predicted by the forecast is calculated to be large, transmission that reflects that trend A function will be obtained. As a result, the accuracy of the predicted inflow amount may increase.

  As described above, the present invention naturally includes various embodiments not described herein. Therefore, the technical scope of the present invention is defined only by the invention specifying matters according to the scope of claims reasonable from the above description.

It is a schematic diagram which shows the structure of the rainwater inflow amount prediction apparatus which concerns on the 1st Embodiment of this invention. It is a schematic diagram for demonstrating an inflow area | region. It is a graph for demonstrating conversion object time. It is a graph which shows the example of data of the rainfall actual value input into the rainwater inflow amount prediction apparatus which concerns on the 1st Embodiment of this invention. It is a graph which shows the example of data of the rainfall predicted value input into the rainwater inflow prediction apparatus which concerns on the 1st Embodiment of this invention. It is a graph which shows the example of data of the rainwater inflow amount input into the rainwater inflow amount prediction apparatus which concerns on the 1st Embodiment of this invention. It is a flowchart for demonstrating the production | generation method of the linear function which concerns on the 1st Embodiment of this invention. It is a graph which shows the example of the transfer function which the rain water inflow prediction apparatus which concerns on the 1st Embodiment of this invention produces | generates. It is a flowchart for demonstrating the rainwater inflow amount prediction method which concerns on the 1st Embodiment of this invention. It is a schematic diagram which shows the structure of the rainwater inflow amount prediction apparatus which concerns on the 2nd Embodiment of this invention. It is a flowchart for demonstrating the rainwater inflow amount prediction method which concerns on the 2nd Embodiment of this invention. It is a graph which shows the example of the response function which the rainwater inflow prediction apparatus which concerns on the 2nd Embodiment of this invention produces | generates.

Explanation of symbols

DESCRIPTION OF SYMBOLS 11 ... Function generation unit 111 ... Fourier transform module 112 ... Function calculation module 113 ... Fourier inverse transform module 12 ... Prediction unit 121 ... Fourier transform module 122 ... Predicted value calculation module 123 ... Fourier inverse transform module 124 ... Integration module 30 ... Measurement amount Database 201 ... Data set storage area 202 ... Data set conversion value storage area 203 ... Linear function storage area 204 ... Rainfall data storage area 205 ... Conversion value storage area 206 ... Predicted data storage area 207 ... Predicted inflow storage area

Claims (9)

A measured amount database for storing time series data of rainfall in each of a plurality of areas, and time series data of total rainwater inflow from the plurality of areas;
A function generation unit that generates a linear function for each of the plurality of regions, using the time series data, and indicating a relationship for each of the plurality of regions of rainfall and rainwater inflow obtained in the past;
A prediction unit that predicts rainwater inflow from rainfall input data that includes at least one of a predicted value of rainfall and current measured values of the plurality of regions using the linear function. Rainwater inflow prediction device. A function generation unit, a prediction unit, a measurement amount database that stores time series data of each rainfall amount and total rainwater inflow from the plurality of regions, and a rainwater inflow prediction device including a linear function storage region A method for predicting the amount of rainwater inflow using a method for predicting the amount of rainwater inflow,
The function generation unit reads the time series data from the measured quantity database, and a linear function indicating a relationship between each of the plurality of regions of rainfall and rainwater inflow obtained in the past using the time series data Respectively for the plurality of regions and storing in the linear function storage region;
The prediction unit reads the linear function from the linear function storage area, and using the linear function, from the rainfall input data including at least one of the predicted rainfall value and the current actual measurement value of the plurality of areas. A step of predicting the amount of rainwater inflow.   The rainwater inflow prediction method according to claim 2, wherein the plurality of regions are rectangular regions having the same shape and size.   The linear function receives a Fourier transform value of the time series data of the rainfall amount of each of the plurality of regions, and outputs a Fourier transform value of the time series data of the total rainwater inflow amount from the plurality of regions. The rainwater inflow prediction method according to claim 2, wherein the rainwater inflow is predicted.   The transfer function is the sum of the Fourier transform values of the time-series data of the rainfall amounts of the plurality of regions at the respective sampling frequencies in the Fourier transform and the total rainwater inflow from the plurality of regions. 5. The rainwater inflow prediction method according to claim 4, wherein a difference between the time-series data and a Fourier transform value is determined to be minimum. Predicting the stormwater inflow amount,
Fourier transform each of the rainfall input data of each of the plurality of areas,
The multiplication of the Fourier transform rainfall input data and the transfer function is performed for each of the plurality of regions, the prediction data is calculated by taking the sum of the multiplication results,
The rainwater inflow prediction method according to claim 4, wherein a prediction value is calculated by performing inverse Fourier transform on the prediction data.   The linear function is a transfer function having as input a Fourier transform value of time series data of rainfall in each of the plurality of regions and outputting a Fourier transform value of time series data of total rainwater inflow from the plurality of regions. The rainwater inflow prediction method according to claim 2, wherein the rainwater inflow prediction method is a response function calculated by inverse Fourier transform.   The transfer function is the sum of the Fourier transform values of the time-series data of the rainfall amounts of the plurality of regions at the respective sampling frequencies in the Fourier transform and the total rainwater inflow from the plurality of regions. 8. The rainwater inflow prediction method according to claim 7, wherein a difference between the time series data and a Fourier transform value is determined to be a minimum.   The prediction of the rainwater inflow amount is calculated by the sum of the convolution integral value of the rainfall input data of each of the plurality of regions and the response function for each of the plurality of regions for the plurality of regions. The rainwater inflow prediction method according to claim 7 or 8, wherein the rainwater inflow is predicted.

Images