JP5823459B2 - Inflow amount prediction device, inflow amount prediction method, water level prediction device, and program - Google Patents

Description

  The present invention relates to an inflow amount prediction device, an inflow amount prediction method, a water level prediction device, and a program.

  It is necessary to predict the inflow for the operation of the water storage facility. Precipitation is taken into account when forecasting inflows. For example, in Patent Document 1, the most suitable one of three models is selected, and the inflow is calculated by applying the measured inflow and the predicted rainfall. A method for predicting the above is disclosed.

JP 2008-184838 A

  However, the method described in Patent Document 1 depends on a given model such as a tank model. For example, in the case where loss or underground infiltration is assumed but loss coefficient or infiltration coefficient is unknown, inflow The accuracy of the predicted value of the quantity is reduced.

  The present invention has been made in view of such a background, and can predict an inflow amount with high accuracy without depending on an existing model, an inflow amount prediction device, an inflow amount prediction method, a water level prediction device, and The purpose is to provide a program.

  A main invention of the present invention for solving the above problems is an apparatus for predicting an inflow amount into a water storage facility, and represents a relationship between forecast data of weather including precipitation and measured data of the weather and the inflow amount. A regression equation storage unit that stores a regression equation including an unknown regression coefficient, and the regression coefficient is estimated based on the past prediction data, the past actual measurement data, the past actual amount of inflow, and the regression equation. A regression analysis unit; a prediction data acquisition unit that acquires the prediction data; an actual data acquisition unit that acquires the actual measurement data; the acquired prediction data; the acquired actual measurement data; and the estimated regression coefficient And an inflow amount prediction unit that calculates the predicted value of the inflow amount by applying to the above.

  In the inflow amount prediction device of the present invention, the regression analysis unit estimates the coefficient using the actual value for the increment of the inflow amount, and the inflow amount prediction unit calculates the inflow amount from the regression equation. An increment prediction value may be calculated, and the inflow amount prediction value may be calculated according to the calculated inflow amount increment prediction value.

  In the inflow prediction device of the present invention, the prediction data and the actual measurement data may include precipitation, temperature, and snow depth, respectively.

  In the inflow amount prediction device of the present invention, the prediction data may be a predicted value for each area obtained by dividing a river basin flowing into the water storage facility into a mesh.

  In the inflow amount prediction device of the present invention, a weight is assigned to each observation point of the actual measurement data, and the regression analysis unit multiplies the past actual measurement data by the weight for each observation point. The coefficient may be estimated, and the inflow amount prediction unit may calculate the predicted value of the inflow amount after multiplying the acquired actual measurement data by the weight for each observation point.

  Further, another aspect of the present invention is an apparatus for predicting the water level of a water storage facility, which is an unknown that represents the relationship between forecast data of meteorology including precipitation and measured data of the weather and the amount of inflow to the water storage facility. A regression equation storage unit that stores a regression equation including a regression coefficient, and a regression analysis that estimates the regression coefficient based on the past prediction data, the past actual measurement data, the past actual value of the inflow, and the regression equation A prediction data acquisition unit for acquiring the prediction data, an actual data acquisition unit for acquiring the actual measurement data, and applying the acquired prediction data, the acquired actual measurement data, and the estimated regression coefficient to the regression equation. And an inflow amount prediction unit that calculates a predicted value of the inflow amount, an outflow amount acquisition unit that acquires an outflow amount from the water storage facility, and the predicted value of the inflow amount and the outflow amount based on the And further comprising a water level prediction unit for predicting a position, a.

  According to another aspect of the present invention, there is provided a method for predicting an inflow amount into a water storage facility, in which a computer represents unknown weather data including precipitation and a relationship between the measured weather data and the inflow amount. Storing a regression equation including a regression coefficient of the past, estimating the regression coefficient based on the past prediction data, the past actual measurement data, the past actual value of the inflow, and the regression formula; A step of acquiring prediction data; a step of acquiring the actual measurement data; and calculating the predicted value of the inflow amount by applying the acquired prediction data, the acquired actual measurement data, and the estimated regression coefficient to the regression equation And the step of performing.

  According to another aspect of the present invention, there is provided a program for predicting an inflow amount into a water storage facility, wherein the computer predicts meteorological prediction data including precipitation and the relationship between the meteorological measurement data and the inflow amount. A step of storing a regression equation including an unknown regression coefficient to represent, a step of estimating the regression coefficient based on the past prediction data, the past actual measurement data, the past actual value of the inflow, and the regression formula; Obtaining the predicted data; obtaining the measured data; applying the acquired predicted data, the acquired measured data, and the estimated regression coefficient to the regression equation; And a step of calculating.

  Other problems and solutions to be disclosed by the present application will be made clear by the embodiments of the invention and the drawings.

  According to the present invention, it is possible to accurately predict the inflow amount without depending on an existing model.

It is a figure explaining the outline | summary of a water level prediction process. It is a figure which shows the hardware structural example of the water level prediction apparatus. It is a figure which shows the software structural example of the water level prediction apparatus. 3 is a diagram illustrating a configuration example of an actual measurement value storage unit 131. FIG. 3 is a diagram illustrating a configuration example of an inflow amount storage unit 132. FIG. It is a figure which shows the structural example of the predicted value memory | storage part 133. FIG. 3 is a diagram illustrating a configuration example of a weight storage unit 135. FIG. It is a figure which shows the flow of an estimation process. It is a figure which shows the flow of a water level prediction process.

  Hereinafter, a water level prediction apparatus 10 according to an embodiment of the present invention will be described. The water level prediction device 10 of the present embodiment predicts the water level of a water storage facility such as a dam, and in particular, predicts the inflow amount using measured values and predicted values of weather, and sets the water level according to the predicted inflow amount. Predict.

  In this embodiment, the actual measured value of the weather is AMeDAS data provided by the Japan Meteorological Agency. AMeDAS data includes precipitation, temperature, snow depth, sunshine duration, and wind speed. In the present embodiment, the weather forecast value is a forecast value (GPV; Grid Point Value; hereinafter referred to as GPV data) based on a numerical forecast provided by the Japan Meteorological Agency. The GPV data is a global numerical usage model (GSM), a meso numerical prediction model (GSM) for the weather in each grid divided in a grid pattern with a predetermined grid interval (for example, 1 km, 5 km, 20 km, etc.). Although it is calculated by a computer using a model such as MSM (Meso Scale Model), Local Numerical Forecast Model (LFM), it does not matter which model is used. The GPV data includes precipitation, temperature, atmospheric pressure, cloud cover, and the like.

  FIG. 1 is a diagram illustrating an outline of a water level prediction process performed by the water level prediction apparatus 10 according to the present embodiment. As shown in the figure, the water level prediction device 10 of this embodiment uses the precipitation amount, temperature and snow depth of AMeDAS data, and the precipitation amount and temperature of GPV data as explanatory variables, and the increase in the amount of inflow to the water storage facility. The regression equation 11 is used as the objective variable, the past AMeDAS data 12, the past GPV data 13, and the past actual inflow amount actual value 14 are given to perform regression analysis 15 to estimate the regression coefficient, and the regression coefficient A prediction formula 16 is obtained by applying to the regression equation. The water level prediction device 10 applies the latest AMeDAS data 21 and the latest GPV data (predicted value) 22 to the prediction formula 16 to calculate a predicted value 23 of the increase in inflow, and increases the inflow The predicted value 25 of the inflow amount is calculated by adding the latest inflow amount actual value 24 to the amount 23. The water level prediction device 10 calculates the predicted value 28 of the next water level based on the predicted inflow 25, the scheduled value 26 of the outflow from the next storage facility, and the current water level 27.

  FIG. 2 is a diagram illustrating a hardware configuration example of the water level prediction apparatus 10. The water level prediction apparatus 10 includes a CPU 101, a memory 102, a storage device 103, a communication interface 104, an input device 105, and an output device 106. The storage device 103 stores various data and programs, such as a hard disk drive, a solid state drive, and a flash memory. The communication interface 104 is an interface for connecting to the communication network 30. For example, an adapter for connecting to Ethernet (registered trademark), a modem for connecting to a public telephone line network, and a wireless communication device for performing wireless communication A USB (Universal Serial Bus) connector for serial communication, an RS232C connector, or the like. The input device 105 is, for example, a keyboard, a mouse, a touch panel, a button, or a microphone that inputs data. The output device 106 is, for example, a display, a printer, or a speaker that outputs data.

  FIG. 3 is a diagram illustrating a software configuration example of the water level prediction apparatus 10. The water level prediction apparatus 10 includes an AMeDAS data acquisition unit 111, an inflow amount acquisition unit 112, a GPV data acquisition unit 113, a regression analysis unit 114, an inflow amount prediction unit 115, an outflow amount acquisition unit 116, a water level acquisition unit 117, and a water level prediction unit 118. , An actual value storage unit 131, an inflow amount storage unit 132, a predicted value storage unit 133, a regression equation storage unit 134, and a weight storage unit 135.

  The AMeDAS data acquisition unit 111, the inflow amount acquisition unit 112, the GPV data acquisition unit 113, the regression analysis unit 114, the inflow amount prediction unit 115, the outflow amount acquisition unit 116, the water level acquisition unit 117, and the water level prediction unit 118 The CPU 101 of the apparatus 10 is realized by reading the program stored in the storage device 103 into the memory 102 and executing the program, and the actual value storage unit 131, the inflow amount storage unit 132, the predicted value storage unit 133, and the regression equation storage unit. 134 and the weight storage unit 135 are realized as part of a storage area provided by the memory 102 and the storage device 103 provided in the water level prediction device 10.

  The measured value storage unit 131 stores measured values of weather. In the present embodiment, it is assumed that AMeDAS data is accumulated in the actual measurement value storage unit 131. FIG. 4 is a diagram illustrating a configuration example of the actual measurement value storage unit 131. As shown in the figure, the measured value storage unit 131 stores observation points, precipitation, temperature, and snow depth in association with the date and time. The observation point is an AMeDAS observation point. Precipitation, temperature and snow depth are measured values at the observation point and are included in the AMeDAS data. It is assumed that past AMeDAS data is registered in the actual value storage unit 131 in advance.

  The AMeDAS data acquisition unit 111 (corresponding to the actual data acquisition unit of the present invention) acquires AMeDAS data related to a basin such as a river that allows water to flow into the water storage facility. The AMeDAS data acquisition unit 111 may acquire AMeDAS data by accessing a computer that provides AMeDAS data operated by, for example, the Japan Meteorological Agency or a meteorological company, or an AMeDAS data from the operator via the input device 105. Data input may be accepted. The AMeDAS data acquisition unit 111 registers the acquired AMeDAS data in the actual measurement value storage unit 131.

  The inflow amount storage unit 132 stores an actually measured value of the inflow amount in the water storage facility. FIG. 5 is a diagram illustrating a configuration example of the inflow amount storage unit 132. As shown in the figure, the inflow amount storage unit 132 stores the actual value of the inflow amount in association with the date and time. In this embodiment, it is assumed that the amount of inflow per hour is measured.

  The inflow amount acquisition unit 112 acquires an actual measurement value of the inflow amount in the water storage facility. For example, the inflow amount acquisition unit 112 may receive an input of an actual measurement value of the inflow amount from the operator via the input device 105, or may automatically acquire the inflow amount from the inflow amount measurement device. Good. The inflow amount acquisition unit 112 registers the acquired actual measurement value of the inflow amount in the inflow amount storage unit 132.

  The predicted value storage unit 133 stores a predicted value of weather. In the present embodiment, it is assumed that GPV data is accumulated in the predicted value storage unit 133. FIG. 6 is a diagram illustrating a configuration example of the predicted value storage unit 133. As shown in the figure, the predicted value storage unit 133 stores precipitation, temperature, and the like in association with the forecast date / time, the target date / time, and the grid. The forecast date and time is the date and time when the numerical forecast was announced, and the target date and time is the date and time when the forecast was made. The grid is information for specifying each grid, and is expressed by latitude and longitude in the present embodiment, but may be any information that can specify the grid, such as a number assigned to each grid. It is assumed that past GPV data is registered in the predicted value storage unit 133 in advance.

  The GPV data acquisition unit 113 (corresponding to the prediction data acquisition unit of the present invention) acquires GPV data relating to a basin such as a river that allows water to flow into the water storage facility. The GPV data acquisition unit 113 may acquire the GPV data by accessing a computer that provides the GPV data operated by, for example, the Japan Meteorological Agency or a meteorological company, or the GPV data from the operator via the input device 105. Data input may be accepted. The GPV data acquisition unit 113 registers the acquired GPV data in the predicted value storage unit 133.

The regression equation storage unit 134 stores a regression equation including an unknown regression coefficient that represents the relationship between the actual and predicted values of weather and the increase in the inflow. In the present embodiment, the regression equation uses a measured value of precipitation, temperature and snow depth and a predicted value of precipitation and temperature as explanatory variables, and a regression with an increase in inflow to the water storage facility as an objective variable. It is assumed that ΔR is the increase in inflow, P1 _{p is} the precipitation of the latest AMeDAS data for each observation point p (p = 1 to n), T1 _{p is} the temperature, D1 _p is the snow depth, and each grid g (g = 1 to m) from the most recent GPV data, assuming that P2 _{g, t} is the predicted value of precipitation after t hours (t = 1 to x), T2 _{g, t} is the predicted value of temperature, and the regression equation is 1). Note that x is the time when the numerical forecast is provided. For example, the Japan Meteorological Agency provides up to 33 hours ahead in the numerical forecast of 5 km mesh (x = 33), and provides up to 264 hours ahead in the numerical forecast of 20 km mesh. (T = 264).

回帰分析部１１４は、上記回帰式（１）について回帰分析を行う。回帰分析部１１４は、流入量記憶部１３２に記憶されている流入量の実測値から流入量の増加分ΔＲを算出し、算出したΔＲと、実績値記憶部１３１に記憶されている降水量、気温および積雪深の実測値Ｐ１_ｐ、Ｔ１_ｐおよびＤ１_ｐと、予測値記憶部１３３に記憶されている降水量および気温の予測値Ｐ２_ｇおよびＴ２_ｇと、上記回帰式（１）とを用いて、一般的な回帰分析の手法により未知の回帰係数ａ〜ｅを推計する。また、回帰分析部１１４は、回帰係数ａ〜ｅを上記回帰式（１）に設定した式（以下、予測式という。）を回帰式記憶部１３４に登録する。

The regression analysis unit 114 performs regression analysis on the regression equation (1). The regression analysis unit 114 calculates an increase ΔR of the inflow amount from the actual measurement value of the inflow amount stored in the inflow amount storage unit 132, the calculated ΔR, and the precipitation amount stored in the actual value storage unit 131, Using measured values P1 _p , T1 _p and D1 _{p of} temperature and snow depth, predicted values P2 _g and T2 _{g of} precipitation and temperature stored in the predicted value storage unit 133, and the above regression equation (1) Thus, unknown regression coefficients a to e are estimated by a general regression analysis technique. Further, the regression analysis unit 114 registers an equation (hereinafter referred to as a prediction equation) in which the regression coefficients a to e are set in the regression equation (1) in the regression equation storage unit 134.

  The weight storage unit 135 stores a weight corresponding to the degree of contribution to the inflow amount for each area in the basin. FIG. 7 is a diagram illustrating a configuration example of the weight storage unit 135. As shown in the figure, the weight storage unit 135 stores data types, areas, and weights in association with each other. The data type is “AMeDAS” or “GPV”. When the data type is “AMeDAS”, the area is an AMeDAS observation point, and the weight for the AMeDAS observation point is stored. When the data type is “GPV”, the area is a grid, and the weight for the grid is stored.

The inflow amount predicting unit 115 predicts the measured values P1 _p , T1 _p, and D1 _p of precipitation, temperature, and snow depth at the most recent date and time, and predicted values P2 _g and T2 _g of precipitation and temperature at the most recent date. By applying to the equation, an increase ΔR of the inflow rate is obtained, and the predicted value of the inflow rate is calculated by adding the increase ΔR to the latest actual value R of the inflow rate.

  The outflow amount acquisition unit 116 acquires a scheduled value of the outflow amount of water from the water storage facility. For example, the outflow amount acquisition unit 116 may acquire the scheduled value of the outflow amount from the operator via the input device 105, or may access a computer that supports the operation of the water storage facility such as a water level plan to determine the outflow amount. A scheduled value may be acquired.

  The water level acquisition unit 117 acquires the current water level of the water storage facility. For example, the water level acquisition unit 117 may acquire the current water level from an operator via the input device 105, or may automatically acquire the current water level from a water level measuring device installed in the water storage facility. Alternatively, the current water level may be acquired from a computer that performs an operation plan of the water storage facility.

  The water level prediction unit 118 calculates the increase / decrease amount of the stored water amount from the predicted value of the inflow amount and the expected value of the outflow amount, converts the calculated increase / decrease amount into the change amount of the water level, and adds / subtracts to / from the current water level. The predicted value of is calculated. In addition, about the method of converting water storage amount into a water level, description is abbreviate | omitted here as what uses a general thing.

  Hereinafter, the process performed by the water level prediction apparatus 10 will be described.

FIG. 8 is a diagram showing the flow of estimation processing. 8 is executed every predetermined period (for example, any period such as one day, one week, or one month).
The regression analysis unit 114 reads the actual value of the inflow amount from the inflow amount storage unit 132 in order of date and time (S201), and calculates the increase ΔR of the inflow amount from the previous period at each date and time (S202). The regression analysis unit 114 reads the past AMeDAS data that is the same as or closest to the date and time from the actual measurement value storage unit 131 for each date and time from the predetermined analysis start date and time (S203), and assigns a weight for each observation point. The data is read from the weight storage unit 135 (S204), and the read weight is multiplied by the precipitation amount included in the AMeDAS data at the corresponding observation point (S205). The regression analysis unit 114 reads the past GPV data that is the same as or closest to the date and time from the predicted value storage unit 133 (S206), reads the weights for each grid from the weight storage unit 135 (S207), and sets the read weights. The amount of precipitation included in the GPV data of the corresponding grid is multiplied (S208). The regression analysis unit 114 applies the precipitation amount of AMeDAS data (multiplied by the weight), the temperature and the snow depth, the precipitation amount of the read GPV data (multiplied by the weight), and the temperature to the regression equation (1). (S209). The regression analysis unit 114 obtains a regression coefficient so that the difference between the value calculated by the regression equation (1) and ΔR calculated in step S202 is minimized (S210). The regression analysis unit 114 creates a prediction formula in which the obtained regression coefficient is applied to the regression formula (1) and registers it in the regression formula storage unit 134 (S211).

  As described above, the relationship between the past precipitation, actual temperature and snow depth, the predicted precipitation and temperature predicted in the past, and the actual measured value of the inflow increase is obtained by regression analysis. It becomes possible to register the prediction formula representing the relationship in the regression formula storage unit 134.

FIG. 9 is a diagram illustrating a flow of a water level prediction process.
The inflow amount predicting unit 115 reads the latest AMeDAS data from the current date and time from the measured value storage unit 131 (S221), reads the weight for each observation point from the weight storage unit 135 (S222), and reads the read weights. The precipitation amount included in the AMeDAS data at the corresponding observation point is multiplied (S223). The inflow amount predicting unit 115 reads the latest GPV data from the current date and time from the predicted value storage unit 133 (S224), reads the weight for each grid from the weight storage unit 135 (S225), and corresponds the read weights. The precipitation amount included in the GPV data of the grid to be multiplied is multiplied (S226). The inflow amount prediction unit 115 reads out the prediction formula from the regression equation storage unit 134 (S227), and is included in the precipitation (multiplied by weight), temperature, snow depth, and GPV data included in the AMeDAS data. A predicted value ΔR corresponding to an increase in the inflow is calculated by applying the precipitation (multiplied by the weight) and the temperature to the read prediction formula (S228). The inflow rate prediction unit 115 reads the latest measured value of the inflow rate from the current date and time from the inflow rate storage unit 132 as R _t (S229), adds ΔR to R _t and predicts the next inflow rate R It calculates to _{t + 1} (S230).

Runoff acquisition unit 116 acquires the predetermined value _{F t + 1} of the next runoff (S231), the water level acquisition unit 117 acquires the current water level _{H t} (S232). The water level prediction unit 118 converts the water level H _t into the water storage amount V _t (S233). In addition, description is abbreviate | omitted as what uses a general method for conversion from a water level to a stored water amount. The water level prediction unit 118 adds the predicted value R _{t + 1} of the next inflow amount to the stored water amount V _t and subtracts the estimated value F _{t + 1} of the next outflow amount to calculate the predicted value V _{t + 1} of the next water storage amount. (S234). The water level prediction unit 118 converts the predicted value V _{t + 1} of the next-stage water storage amount into a water level, and calculates the predicted value H _{t + 1} of the next-stage water level (S235).

  As described above, according to the water level prediction apparatus 10 of the present embodiment, the relational expression between the AMeDAS data and GPV data and the inflow increase is estimated, and the increase in inflow is predicted using this relational expression. It is possible to predict the next inflow by using the increase in inflow. Therefore, it is possible to predict the inflow amount using the actual measured value and the predicted value of the weather without depending on a known prediction model such as a tank model.

  Further, according to the water level prediction apparatus 10 of the present embodiment, not only the predicted value (GPV) based on the numerical forecast but also the AMeDAS data that is the actual measured value is added to the explanatory variable, so the predicted value based on the numerical forecast is incorrect. Even in such a case, it is possible to supplement the error of the numerical forecast by the actually measured value, and it is expected that the prediction accuracy of the inflow amount is improved.

Further, according to the water level prediction device 10 of the present embodiment, the actual value of the precipitation, and the like, and to predict the increase in the inflow amount using the predicted value, the inflow of the next by adding the increment to the current period of the inflow R _t The quantity R _{t + 1} can be predicted. Therefore, the prediction accuracy can be improved by the amount using the actually measured value for the inflow amount for the current period, compared to the case of directly predicting the inflow amount for the next period from the weather data.

  Moreover, according to the water level prediction apparatus 10 of the present embodiment, it is possible to evaluate the snow depth in addition to the precipitation amount and the temperature, thereby improving the prediction accuracy in the next snow accumulation period.

  Moreover, according to the water level prediction apparatus 10 of the present embodiment, it is possible to perform evaluation with weighting for each observation point and grid. Therefore, when the distance between points is not constant as in the AMeDAS observation point, it is possible to compensate using the Thiessen method or the like. Also, regarding the grid of numerical forecasts, when the contribution of the inflow to the river is known, the weight can be evaluated in advance, so that the prediction accuracy can be improved.

  Moreover, according to the water level prediction apparatus 10 of the present embodiment, the water level can be predicted based on the predicted value of the inflow amount with high accuracy as described above, and therefore, the water level can also be predicted with high accuracy. it can.

  In the present embodiment, the water level prediction device 10 predicts the water level of the water storage facility, but may be an inflow amount prediction device that predicts only the inflow amount. In this case, the process of FIG. 9 is performed only until step S230.

  In the present embodiment, the actual measured value of the weather is assumed to be AMeDAS data. However, the present invention is not limited to this. For example, it may be meteorological data obtained by analyzing a weather radar or a satellite photograph, a rain gauge, an anemometer, or the like. It may be meteorological data observed by installing a barometer or the like, or meteorological data observed visually.

  In the present embodiment, only the precipitation amount, the temperature, and the snow depth are used as the actual measured values of the weather. However, the present invention is not limited to this, and various types of weather data can be used. In this case, various actual measurement values to be used are added as explanatory variables of the regression equation (1).

  In the present embodiment, the weather forecast value is GPV data. However, the present invention is not limited to this, and a forecast value provided as a weather forecast other than the numerical forecast can be used. Further, the predicted value does not have to be for each mesh, and in that case, the weight is managed for each observation point.

  In the present embodiment, only the precipitation and the temperature are used as the predicted weather value. However, the present invention is not limited to this, and various types of weather data can be used. Also in this case, various prediction values to be used are added as explanatory variables of the regression equation (1).

  In this embodiment, the latest GPV data is used to estimate the regression equation and apply it to the prediction equation. However, the present invention is not limited to this. For example, the average value and median value of the prediction values published in the past are used. A statistical value such as may be used.

In the present embodiment, the prediction formula is created with the increment of the inflow amount as a prediction target, but it may be a regression formula with the inflow amount itself as the prediction target. In this case, the regression equation is expressed by the following equation (1 ′), and step S202 in FIG. 8 and step S230 in FIG. 9 are omitted.

  Although the present embodiment has been described above, the above embodiment is intended to facilitate understanding of the present invention and is not intended to limit the present invention. The present invention can be changed and improved without departing from the gist thereof, and the present invention includes equivalents thereof.

111 AMeDAS Data Acquisition Unit 112 Inflow Amount Acquisition Unit 113 GPV Data Acquisition Unit 114 Regression Analysis Unit 115 Inflow Amount Prediction Unit 116 Outflow Amount Acquisition Unit 117 Water Level Acquisition Unit 118 Water Level Prediction Unit 131 Actual Measurement Value Storage Unit 132 Inflow Amount Storage Unit 133 Predicted Value Storage unit 134 Regression equation storage unit 135 Weight storage unit

Claims (8)

An apparatus for predicting the amount of water flowing into a water storage facility,
A regression equation storage unit for storing a regression equation including an unknown regression coefficient representing the relationship between the forecast data of precipitation including precipitation and the measured data of the weather and the inflow,
A regression analysis unit that estimates the regression coefficient based on the past prediction data, the past actual measurement data, the past actual value of the inflow, and the regression equation;
A prediction data acquisition unit for acquiring the prediction data;
An actual measurement data acquisition unit for acquiring the actual measurement data;
An inflow prediction unit that calculates the predicted value of the inflow by applying the acquired prediction data, the acquired actual measurement data, and the estimated regression coefficient to the regression equation;
An inflow amount prediction apparatus comprising: The inflow amount prediction device according to claim 1,
The regression analysis unit estimates the coefficient using the actual value for the inflow increase,
The inflow rate prediction unit calculates a predicted value of the inflow rate increment from the regression equation, and calculates a predicted value of the inflow rate according to the calculated predicted value of the inflow rate increment;
An inflow amount predicting device characterized by the above. The inflow amount prediction device according to claim 1 or 2,
The prediction data and the actual measurement data include precipitation, temperature, and snow depth, respectively.
An inflow amount predicting device characterized by the above. The inflow amount prediction device according to any one of claims 1 to 3,
The prediction data is a prediction value for each area obtained by dividing a river basin flowing into the water storage facility into a mesh;
An inflow amount predicting device characterized by the above. The inflow amount prediction device according to any one of claims 1 to 4,
Each observation point of the actual measurement data is weighted,
The regression analysis unit estimates the coefficient after multiplying the past actual measurement data by the weight for each observation point,
The inflow amount prediction unit calculates the predicted value of the inflow amount after multiplying the acquired actual measurement data by the weight for each observation point;
An inflow amount predicting device characterized by the above. A device for predicting the water level of a water storage facility,
A regression equation storage unit for storing a regression equation including an unknown regression coefficient representing a relationship between forecast data of precipitation including precipitation and actual measurement data of the weather and inflow to the water storage facility;
A regression analysis unit that estimates the regression coefficient based on the past prediction data, the past actual measurement data, the past actual value of the inflow, and the regression equation;
A prediction data acquisition unit for acquiring the prediction data;
An actual measurement data acquisition unit for acquiring the actual measurement data;
An inflow prediction unit that calculates the predicted value of the inflow by applying the acquired prediction data, the acquired actual measurement data, and the estimated regression coefficient to the regression equation;
An outflow amount obtaining unit for obtaining an outflow amount from the water storage facility;
A water level prediction unit for predicting the water level based on the predicted value of the inflow amount and the outflow amount;
A water level prediction apparatus comprising: A method for predicting the inflow to a water storage facility,
Computer
Storing meteorological prediction data including precipitation and a regression formula including an unknown regression coefficient representing a relationship between the measured data of the meteorological data and the inflow;
Estimating the regression coefficient based on the past prediction data, the past actual measurement data, the past actual flow rate value, and the regression equation;
Obtaining the prediction data;
Obtaining the measured data;
Applying the acquired prediction data, the acquired actual measurement data, and the estimated regression coefficient to the regression equation to calculate a predicted value of the inflow amount;
The inflow amount prediction method characterized by performing this. A program for predicting the inflow to a water storage facility,
On the computer,
Storing meteorological prediction data including precipitation and a regression formula including an unknown regression coefficient representing a relationship between the measured data of the meteorological data and the inflow;
Estimating the regression coefficient based on the past prediction data, the past actual measurement data, the past actual flow rate value, and the regression equation;
Obtaining the prediction data;
Obtaining the measured data;
Applying the acquired prediction data, the acquired actual measurement data, and the estimated regression coefficient to the regression equation to calculate a predicted value of the inflow amount;
A program for running

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