JP4836813B2 - 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.
JP 2000-087433 A

  However, various problems have been pointed out regarding the method proposed above. The method of predicting the amount of rainwater inflow by searching for similar patterns cannot cope with the case of torrential rain with no past accumulation. In addition, the method of predicting stormwater inflow by black boxing methods such as nonlinear functions and genetic algorithms does not clearly model the causal relationship between rainfall and stormwater inflow. In the case of heavy rain, the predicted value becomes an extrapolated value in the nonlinear function, which may cause divergence and the reliability of the predicted value decreases. Further, there is a problem that it is difficult to determine whether or not the predicted value is an abnormal value. That is, the method proposed above cannot predict the amount of rainwater inflow into the rainwater treatment plant with high accuracy.

  An object of the present invention is to provide a rainwater inflow prediction device and a rainwater inflow prediction method that can predict a rainwater inflow to a rainwater treatment plant with high accuracy.

According to one aspect of the present invention, (b) a measured amount database storing time series data of rainfall and rainwater inflow, and (b) Fourier of each of time series data of rainfall and rainwater inflow. A function generation unit that generates a linear function indicating the relationship between the rainfall and the inflow of rainwater using the converted value, and (c) includes at least one of a predicted value and an actual measurement value of the rainfall using the linear function There is provided a rainwater inflow prediction device comprising a prediction unit for predicting rainwater inflow from rainfall input data.

According to another aspect of the present invention, a rainwater inflow prediction device comprising a function generation unit, a prediction unit, a measurement amount database that stores time series data of each measurement value of rainfall and rainwater inflow, and a linear function storage area. A method for predicting stormwater inflow using (a) function generation unit reads time series data from a measured quantity database, and performs a Fourier transform of each of the time series data of rainfall and stormwater inflow Using the value to generate a linear function indicating the relationship between rainfall and stormwater inflow and storing it in the linear function storage area; and (b) the prediction unit using the linear function read from the linear function storage area. A method for predicting the amount of rainwater inflow including a step of predicting the amount of inflow of rainwater from rainfall input data including at least one of a predicted value of rainfall and an actual measurement value It is.

  ADVANTAGE OF THE INVENTION According to this invention, the rainwater inflow amount prediction apparatus and rainwater inflow amount prediction method which can predict the rainwater inflow amount to a rainwater treatment plant with high precision 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 includes a measurement amount database 30 for storing time series data of measured values of rainfall and rainwater inflow, and a time series. Using the data, the function generation unit 11 that generates a linear function indicating the relationship between the rainfall and the inflow of rainwater, and the rainfall input including at least one of the predicted value and the actual measurement value of the rainfall using the linear function And a prediction unit 12 that predicts the amount of rainwater inflow from the data. The function generation unit 11 and the prediction unit 12 are included in a central processing unit (CPU) 10.

  The function generation unit 11 includes a Fourier transform module 111, a function calculation module 112, and an average calculation module 113. The Fourier transform module 111 includes time series data of rainfall obtained in the past and time series of rainwater inflow to a target rainwater treatment plant (hereinafter referred to as “target treatment plant”) for which rainwater inflow is predicted. Each data is Fourier transformed at a certain time including the time when the rain event was observed. The function calculation module 112 divides the Fourier transform value of the time series data of the rainwater inflow amount into the target treatment plant by the Fourier transform value of the time series data of the rainfall amount, and shows a linear relationship indicating the relationship between the rainfall amount and the rainwater inflow amount. The function is calculated as a transfer function. The average calculation module 113 takes an average of the transfer functions calculated for each of a plurality of rainfall events, and determines a linear function indicating the relationship between the rainfall amount and the rainwater inflow amount.

  Here, the “rainfall event” is an event in which the rainfall from the start to the end of the rain is continuous. For example, an event in which an amount of rainfall of an arbitrarily set value is observed is defined as a rain event. Moreover, the time series data of the amount of rainwater flowing into the target treatment plant and the amount of rainfall subjected to Fourier transform are data measured at the time when at least one rainfall event was observed. 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 time”) will be described below with reference to FIG.

  The vertical axis in FIG. 2 is the measured value of the rainfall and the amount of rainwater flowing into the target treatment plant, and the horizontal axis is the time. In FIG. 2, times t1 to t2 when the rainfall amount equal to or greater than the measured value L is measured per unit time are times when the rain event is observed. As shown in FIG. 2, the amount of rainwater inflow into the rainwater treatment plant begins to be observed after the time when the rainfall has begun to be observed. The conversion target time is set with, for example, a time t0 that is a time ts before the time t1 as a starting point and a time t3 that is a time te after the time t2 as a terminal point. Although the time ts and the time te can be set arbitrarily, it is desirable that the time te is set so that the amount of rainwater inflow resulting from the rain event to the rainwater treatment plant at 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 performs Fourier transform on the time series data of the rainfall input data including at least one of the predicted value and the current actual measured value at the time when the rainfall input data is predicted and the measured time. The predicted value calculation module 122 multiplies the Fourier-transformed rainfall amount input data by the linear function generated by the function generation unit 11 and calculates predicted data as the amount of rainwater inflow in the Fourier space. The inverse Fourier transform module 123 obtains time-series data by performing inverse Fourier transform on the prediction data, and calculates an unknown rainwater inflow amount that flows into the target treatment plant.

  The measured amount database 30 includes the past rainfall measured in a plurality of rainfall events and the inflow of rainwater into the target treatment plant in a region where the rainfall in that region flows into the target treatment plant (hereinafter referred to as “inflow region”). Store quantity time-series data.

  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 past data storage area 201, a past data converted value storage area 202, a transfer function storage area 203, a linear function storage area 204, a rainfall amount data storage area 205, a converted value storage area 206, a predicted data storage area 207, And a predicted inflow amount storage area 208.

  The past data storage area 201 stores past data that is time-series data of rainfall amounts measured in the past and rainwater inflow amounts to the target treatment plant. The past data conversion value storage area 202 stores the Fourier-transformed value of past data. The transfer function storage area 203 stores the transfer function calculated by the function calculation module 112. The linear function storage area 204 stores the linear function generated by the function generation unit 11. The rainfall data storage area 205 stores rainfall input data including at least one of a predicted rainfall value and a current actual measurement value. The converted value storage area 206 stores a value obtained by Fourier transforming the rainfall input data. The prediction data storage area 207 stores prediction data calculated by the prediction unit 12. The predicted inflow amount storage area 208 stores an unknown predicted 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.

  Further, as the output device 50, a display or printer that displays the predicted amount of rainwater inflow or the like, or a recording device that stores data in a computer-readable recording medium can be used. 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”.

  An example of actual rainfall data input to the rainwater inflow prediction device is shown in FIG. 3, and an example of predicted rainfall data is shown in FIG. Moreover, the example of the data of the rainwater inflow amount to the target treatment plant inputted into the rainwater inflow amount prediction apparatus is shown in FIG. The horizontal axis in FIGS. 3 to 5 represents the time from the start of measurement or prediction. As shown in FIGS. 3 to 5, 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 inflow of rainwater into frequency domain data for a plurality of conversion target times each including different rain events, and a linear function for each rain event. Is generated.

  Hereinafter, a method in which the function generation unit 11 generates a linear function indicating the relationship between the rainfall and the amount of rainwater flowing into the target treatment plant will be described with reference to the flowchart shown in FIG. Here, it is assumed that past data, which is time-series data of the measured rainfall amount and the amount of rainwater flowing into the target treatment plant, is stored in the past data storage area 201.

  (A) In step S11, the Fourier transform module 111 selects one conversion target time from a plurality of conversion target times at which rainfall events are observed, and the past data of rainfall measured at the selected conversion target time, and The past data of the amount of rainwater flowing into the target treatment plant is read from the past data storage area 201. Then, the Fourier transform module 111 performs Fourier transform on the read past data of rainfall and the past data of rainwater inflow to the target treatment plant, respectively. The Fourier transform value of the past data of the rainfall amount and the inflow amount of rainwater is stored in the past data transform value storage area 202.

  (B) In step S <b> 12, the function calculation module 112 reads out the Fourier transform value of the past data of the rainfall amount and the rainwater inflow amount from the past data transform value storage area 202. Then, the function calculation module 112 calculates a transfer function by dividing the Fourier transform value of the past data of the rainwater inflow by the Fourier transform value of the past data of the rainfall. The calculated transfer function is stored in the transfer function storage area 203.

  (C) In step S13, the function generation unit 11 determines whether or not a transfer function has been calculated for all rain events used to generate the linear function. If transfer functions have been calculated for all rainfall events, the process proceeds to step S14. If there is a rainfall event whose transfer function has not been calculated, the process returns to step S11.

  (D) In step S14, the average calculation module 113 reads all the transfer functions stored in the transfer function storage area 203. The average calculation module 113 calculates the linear function which shows the relationship between the amount of rainfall and the amount of rainwater inflow by taking the average of all transfer functions. The calculated linear function is stored in the linear function storage area 204.

  When a plurality of rain events are observed within one conversion target time, usually, the Fourier transform is performed with these rain events as one event, and the transfer function is calculated. FIG. 7 shows an example of a transfer function generated by the function generation unit 11. Note that the frequency on the horizontal axis in FIG. 7 indicates a sampling frequency in discrete Fourier transform such as fast Fourier transform (FFT) performed by the Fourier transform module 111.

  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. 8, the function generation unit 11 reads from the measurement amount database 30 the past data of the rainfall amount in the inflow region of the target treatment plant and the past data of the rainwater inflow amount to the target treatment plant. The read past data is stored in the past data storage area 201.

  (B) In step S110, the function generation unit 11 generates a linear function indicating the relationship between the rainfall amount and the rainwater inflow amount using the method described with reference to the flowchart shown in FIG. Specifically, the transfer function for each rainfall event is calculated by dividing the Fourier transform value of the past data of rainwater inflow by the Fourier transform value of the past data of rainfall. Then, an average of all transfer functions is taken to generate a linear function indicating the relationship between the rainfall and the inflow of rainwater. The generated linear function is stored in the linear function storage area 204.

  (C) In step S120, the prediction unit 12 acquires the rainfall input data including at least one of the predicted value and the current actual measured value in the inflow region of the target treatment plant on the prediction target day via the input device 40. . 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 205.

  (D) In step S130, the prediction unit 12 predicts the amount of rainwater inflow into the target treatment plant using the linear function and the rainfall input data. Specifically, in step S131, the Fourier transform module 121 reads the rainfall input data from the rainfall data storage area 205. 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 206. Next, in step S <b> 132, the predicted value calculation module 122 reads the Fourier transform value and the linear function of the rainfall input data from the transform value storage area 206 and the linear function storage area 204, respectively. The prediction value calculation module 122 calculates the prediction data by multiplying the Fourier transform value of the rainfall input data by a linear function. The calculated prediction data is stored in the prediction data storage area 207. In step S133, the inverse Fourier transform module 123 reads the prediction data from the prediction data storage area 207. 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 208.

  The rainwater inflow amount of the target treatment plant stored in the predicted inflow amount storage area 208 is output 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 above, the case where the amount of rainwater inflow to the target treatment plant is predicted based on a plurality of rain events has been described. However, when there are few rain events, a linear function may be generated from one rain event. Of course.

  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. In general, the relationship between the amount of rainfall in the inflow region and the amount of rainwater inflow 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.

  The series of rainwater inflow prediction operations shown in FIG. 8 can be executed by controlling the rainwater inflow prediction device shown in FIG. 1 by a program of an algorithm 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. .

  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. 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. 9, the function generation unit 11 further includes an inverse Fourier transform module 114, 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 114 performs an inverse Fourier transform on the transfer function calculated by the function calculation module 112 to calculate a linear function indicating the relationship between the rainfall and the rainwater inflow. The integration module 124 performs the convolution integration shown in the following formula (1) with the rainfall input data f (t) including at least one of the predicted rainfall value and the actual measurement value and the linear function γ (t), and performs the target processing. Calculate rainwater inflow g (t) flowing into the field:

g (t) = ∫ {f (τ) γ (t−τ)} dτ (1)

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. 9 will be described with reference to the flowchart shown in FIG.

  (A) In step S200 of FIG. 9, the function generation unit 11 reads from the measurement amount database 30 the past data of the rainfall amount in the inflow region of the target treatment plant and the past data of the rainwater inflow amount to the target treatment plant. The read past data is stored in the past data storage area 201.

  (B) In step S210, the function generation unit 11 generates a linear function indicating the relationship between the rainfall and the rainwater inflow. Specifically, in step S211, in the same manner as the method described in the first embodiment, the Fourier transform value of the past data of the rainwater inflow is divided by the Fourier transform value of the past data of the rainfall, and the rainfall A transfer function for each event is calculated. Then, an average transfer function is calculated by taking an average of all the transfer functions. The generated average transfer function is stored in the linear function storage area 204. In step S212, the inverse Fourier transform module 114 performs inverse Fourier transform on the average transfer function read from the linear function storage area 204, and generates a linear function indicating the relationship between the rainfall and the stormwater inflow as a response function. . The generated linear function is stored in the linear function storage area 204. FIG. 11 shows an example of a response function that the function generation unit 11 generates as a linear function.

  (C) In step S220, the prediction unit 12 acquires rainfall amount input data including at least one of a predicted value and an actually measured value in the inflow region of the target treatment area on the prediction target day via the input device 40. The rainfall input data is stored in the rainfall data storage area 205.

  (D) In step S230, the prediction unit 12 predicts the amount of rainwater flowing into the target treatment plant using the linear function and the rainfall input data. Specifically, the integration module 124 reads the linear function and the rainfall input data from the linear function storage area 204 and the rainfall data storage area 205, respectively. The integration module 124 performs convolution integration with the rainfall input data and a linear function, and calculates 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 208.

  In the rainwater inflow prediction apparatus shown in FIG. 2, a response function is used as a linear function indicating the relationship between the rainfall and the rainwater inflow 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.

(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 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 ... Average calculation module 114 ... 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 ... Measured quantity database 201 ... Past data storage area 202 ... Past data conversion value storage area 203 ... Transfer function storage area 204 ... Linear function storage area 205 ... Rainfall data storage area 206 ... Conversion value storage area 207 ... Predicted data storage Area 208 ... Predicted inflow storage area

Claims (6)

A measured amount database for storing time series data of measured values of rainfall and inflow of rainwater;
A function generation unit that generates a linear function indicating a relationship between the rainfall and the rainwater inflow , using Fourier transform values of the time series data of the rainfall and the rainwater inflow,
A rainwater inflow prediction apparatus, comprising: a prediction unit that predicts the rainwater inflow based on rainfall input data including at least one of a predicted value and an actual measurement of the rainfall using the linear function. Rainwater that predicts rainwater inflow using a function generation unit, a prediction unit, a measurement amount database that stores time-series data of each measurement value of rainfall and rainwater inflow, and a rainwater inflow prediction device that includes a linear function storage area An inflow prediction method,
The function generation unit reads the time series data from the measured quantity database, and uses the Fourier transform values of the time series data of the rainfall amount and the rainwater inflow amount to determine the relationship between the rainfall amount and the rainwater inflow amount. Generating a linear function to be stored and storing it in the linear function storage area;
The prediction unit includes the step of predicting the rainwater inflow amount from rainfall input data including at least one of a predicted value of rainfall and an actual measurement value using the linear function read from the linear function storage area. The rainwater inflow prediction method characterized by this. Generating the linear function comprises:
Fourier transform each of the time series data of the rainfall and the rainwater inflow,
The rainwater inflow prediction method according to claim 2, wherein the linear function is calculated by dividing the Fourier transform value of the rainwater inflow by the Fourier transform of the rainfall. Predicting the stormwater inflow amount,
Fourier transform the rainfall input data,
Multiply the Fourier transformed rainfall input data and the linear function to calculate prediction data,
The rainwater inflow prediction method according to claim 3, wherein the rainwater inflow is calculated by performing inverse Fourier transform on the prediction data. Generating the linear function comprises:
Fourier transform each of the time series data of the rainfall and the rainwater inflow,
Divide the Fourier transform value of the rainfall by the Fourier transform value of the rainwater inflow to calculate a transfer function,
The rainwater inflow prediction method according to claim 2, wherein the linear function is calculated by performing inverse Fourier transform on the transfer function.   6. The rainwater inflow prediction method according to claim 5, wherein the rainwater inflow prediction is calculated by convolution integration of the rainfall input data and the linear function.

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