CN116719103B - Hydrologic set forecast construction method, hydrologic set forecast construction device, computer equipment and storage medium - Google Patents

Hydrologic set forecast construction method, hydrologic set forecast construction device, computer equipment and storage medium Download PDF

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CN116719103B
CN116719103B CN202311002122.XA CN202311002122A CN116719103B CN 116719103 B CN116719103 B CN 116719103B CN 202311002122 A CN202311002122 A CN 202311002122A CN 116719103 B CN116719103 B CN 116719103B
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CN116719103A (en
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殷兆凯
王鹏翔
梁犁丽
文仁强
杨恒
翟然
李梦杰
刘琨
徐志
吕振豫
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Beijing Gezhouba Electric Power Rest House
China Three Gorges Corp
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China Three Gorges Corp
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Abstract

The invention relates to the technical field of hydrologic forecasting, and discloses a hydrologic set forecasting construction method, a hydrologic set forecasting construction device, computer equipment and a storage medium, wherein the method comprises the following steps: acquiring initial rainfall data of a target river basin in a time period to be forecasted; according to the initial rainfall data and the first number of target scaling factor combinations, first rainfall data of a first number group are obtained; obtaining a first number of average rainfall according to the first rainfall data of the first number group and the average rainfall calculation method; inputting the average rainfall and the corresponding target model parameters into a hydrological model to obtain a first number of first forecasting results; and inputting the first forecasting result into a target post-processing algorithm to obtain a target hydrologic set forecasting interval and a target deterministic forecasting result of the target river basin in the time period to be forecasted. The invention solves the problems that the data for constructing hydrologic set forecast is difficult to obtain and process and the construction method is complex.

Description

Hydrologic set forecast construction method, hydrologic set forecast construction device, computer equipment and storage medium
Technical Field
The invention relates to the technical field of hydrologic forecasting, in particular to a hydrologic set forecasting construction method, a hydrologic set forecasting construction device, computer equipment and a storage medium.
Background
The river basin hydrologic forecasting process has certain space-time uncertainty under the comprehensive influence of a plurality of factors such as climate, weather, underlying surface, human activities and the like. There are two current methods for hydrologic forecasting of the river basin, namely traditional deterministic hydrologic forecasting and emerging hydrologic set forecasting. Compared with the traditional deterministic hydrologic forecast, the emerging hydrologic set forecast can not only represent the uncertainty of the forecast in the form of probability or interval, but also give a most probable hydrologic process. The interval or probability forecast provided by the hydrologic set forecast can reflect the reliability and effectiveness of the forecast on the basis of evaluating the forecast precision and error. The current hydrologic set prediction construction method generally adopts a data set such as numerical weather prediction, analysis data and the like as an input set to drive a distributed hydrologic model to construct a prediction set. However, for business departments, data such as numerical weather forecast, analysis data and the like are difficult to acquire and process, and generally, the forecast accuracy is low, and the data can be used after complex analysis and correction are needed; in addition, the current hydrologic set forecasting construction method adopts a distributed hydrologic model, the simulation precision in some watersheds is not high, and the use is complex.
Therefore, the prior art has the problems that the data for constructing hydrologic set forecast are difficult to obtain and process and the construction method is complex.
Disclosure of Invention
In view of the above, the invention provides a hydrologic set forecast construction method, a hydrologic set forecast construction device, a computer device and a storage medium, so as to solve the problems that data for constructing hydrologic set forecast are difficult to obtain and process and the construction method is complex.
In a first aspect, the present invention provides a method for constructing a hydrologic set forecast, the method comprising:
acquiring initial rainfall data of a target river basin in a time period to be forecasted;
according to the initial rainfall data and the first number of target scaling factor combinations, first rainfall data of a first number group are obtained;
obtaining a first number of average rainfall according to the first rainfall data of the first number group and the average rainfall calculation method;
inputting the average rainfall and the corresponding target model parameters into a hydrological model to obtain a first number of first forecasting results;
and inputting the first forecasting result into a target post-processing algorithm to obtain a target hydrologic set forecasting interval and a target deterministic forecasting result of the target river basin in the time period to be forecasted.
According to the hydrologic set forecast construction method provided by the embodiment, initial rainfall data are obtained, and the initial rainfall data are processed by utilizing target scaling factor combination to obtain first rainfall data; calculating the average rainfall of the first rainfall data, and obtaining a forecast result by using the average rainfall and a hydrological model; and carrying out hydrologic set forecasting construction according to the forecasting result and the target post-processing algorithm. The method has the advantages that the data used by the method is easy to obtain, the data processing mode is simple and effective, the hydrologic set forecasting construction can be carried out by a simpler method and with lower cost, and the method can be widely applied to the improvement of most of the existing forecasting schemes so as to achieve the effect of generally improving the forecasting level of the existing hydrologic forecasting. The method solves the problems that the data for constructing hydrologic set forecast is difficult to obtain and process and the construction method is complex.
In an alternative embodiment, before acquiring the initial rainfall data of the target basin in the period to be forecasted, the method further includes:
acquiring topographic data, first historical rainfall data and first runoff observation data of a second number of rainfall stations in a target flow domain in a first time period;
obtaining a second forecasting result according to the first historical rainfall data, the average rainfall calculation method and the hydrological model;
obtaining a reference evaluation index according to the second forecasting result, the first runoff observing data and a first preset formula;
determining a scaling factor combination from the terrain data, wherein the scaling factor combination comprises a scaling factor for each rain station;
obtaining second rainfall data of each rainfall station according to the first historical rainfall data of each rainfall station and the corresponding scaling coefficient;
obtaining a first model parameter and a third forecasting result according to the second rainfall data, the average rainfall calculation method and the hydrologic model;
obtaining an evaluation index according to the third forecasting result, the first runoff quantity observation data and a first preset formula;
if the evaluation index is larger than the reference evaluation index, combining the scaling factor combination, the first model parameter and the third forecasting result into a forecasting member, taking the scaling factor combination in the forecasting member as a target scaling factor combination, and taking the first model parameter as a target model parameter;
Starting to execute the subsequent steps from determining the scaling factor combinations according to the terrain data until a third number of scaling factor combinations are obtained, stopping to obtain a first number of forecasting members or starting to execute the subsequent steps from determining the scaling factor combinations according to the terrain data until a first number of forecasting members are obtained, stopping;
training the post-processing algorithm according to the first number of forecast members and the first runoff observation data to obtain a target post-processing algorithm.
In the embodiment, the reference evaluation index is solved based on the first historical rainfall data, the scaling factor combination is determined according to the topographic data, the first historical rainfall data is adjusted according to the scaling factor combination to obtain the second rainfall data, the evaluation index is solved based on the second rainfall data, and the forecasting member is determined by comparing the evaluation index with the reference evaluation index, so that the method can utilize the target scaling factor combination and the target model parameter in the forecasting member to perform hydrologic set forecasting construction in a simpler method and at lower cost, can be widely used for improving most of existing forecasting schemes, and generally improves the forecasting level of the existing hydrologic forecasting.
In an alternative embodiment, obtaining the second prediction result according to the first historical rainfall data, the average rainfall calculation method and the hydrologic model includes:
obtaining a first historical average rainfall according to the first historical rainfall data and an average rainfall calculation method;
and inputting the first historical average rainfall into a hydrological model to obtain a second forecasting result.
In the embodiment, a first historical average rainfall of the first historical rainfall data is solved, and then a second forecasting result is obtained by using the first historical average rainfall and a hydrologic model, so that data support is provided for subsequent solving of the reference evaluation index.
In an alternative embodiment, inputting the first historical average rainfall into the hydrologic model to obtain a second forecast result, including:
inputting the first historical average rainfall into a hydrological model, and carrying out parameter calibration on the hydrological model to obtain a second model parameter;
and inputting the first historical average rainfall and the second model parameters into a hydrological model to obtain a second forecasting result.
In this embodiment, the hydrologic model is subjected to parameter calibration, and the second model parameters of the hydrologic model are determined. And inputting the first historical average rainfall and the second model parameters into a hydrological model to obtain a second forecasting result. The second forecasting result obtained by the process is more accurate and has smaller error.
In an alternative embodiment, determining a scaling factor combination from terrain data includes:
determining a variance value corresponding to each rainfall station according to the terrain data and a second preset formula, and determining a scaling factor value range of each rainfall station according to the variance value;
selecting the scaling coefficient of the corresponding rainfall station from each scaling coefficient value range to obtain a second number of scaling coefficients;
and obtaining a scaling factor combination according to the second number of scaling factors.
In this embodiment, firstly, a variance value corresponding to each rainfall station is calculated according to the topographic data, a scaling factor value range is determined according to the variance value, and the scaling factor of the rainfall station is selected from the scaling factor value range. The rainfall observation uncertainty is considered through the scaling coefficient, the method is simple and effective, can be widely applied to the improvement of most existing forecasting schemes, and generally improves the forecasting level of the existing hydrologic forecasting.
In an alternative embodiment, according to the second rainfall data, the average rainfall calculation method and the hydrologic model, obtaining the first model parameter and the third forecasting result includes:
obtaining a second historical average rainfall according to the second rainfall data and the average rainfall calculation method;
Inputting the second historical average rainfall into a hydrological model, and carrying out parameter calibration on the hydrological model to obtain a first model parameter;
and inputting the second historical average rainfall and the first model parameters into a hydrological model to obtain a third forecasting result.
In this embodiment, a second historical average rainfall of the second rainfall data is solved first, the second historical average rainfall is input into the hydrologic model, and the hydrologic model is subjected to parameter calibration to determine a first model parameter of the hydrologic model. And inputting the second historical average rainfall and the first model parameters into a hydrological model to obtain a third forecasting result. The first model parameters provide a basis for the subsequent composition forecasting members, the third forecasting result of the solution is more accurate, and data support is provided for the subsequent solution evaluation indexes.
In an alternative embodiment, after obtaining the target post-processing algorithm, the method further comprises:
acquiring second historical rainfall data of a second number of rainfall stations in a target flow field in a second time period and second runoff observation data;
according to the second historical rainfall data and the target scaling factor combination, obtaining a first quantity group of third rainfall data;
Obtaining a first number of third historical average rainfall according to the first number of third rainfall data and the average rainfall calculation method;
inputting the third historical average rainfall and corresponding target model parameters into a hydrological model to obtain a first number of fourth forecasting results;
inputting the fourth forecasting result into a target post-processing algorithm to obtain a hydrologic set forecasting interval and a deterministic forecasting result of the target river basin in the second time period;
and obtaining a verification result according to the hydrologic set prediction interval, the deterministic prediction result and the second runoff observation data.
In this embodiment, the hydrologic set prediction interval and the deterministic prediction result of the target river basin in the second time period are determined first, the hydrologic set prediction interval and the deterministic prediction result are compared with the second runoff observation data of the target river basin in the second time period, a verification result is obtained, and the effectiveness and the reliability of the invention can be analyzed by using the verification result.
In a second aspect, the present invention provides a hydrologic set forecast construction device, including:
the first acquisition module is used for acquiring initial rainfall data of the target river basin in the time period to be forecasted;
The first obtaining module is used for obtaining first rainfall data of a first quantity group according to the combination of the initial rainfall data and the first quantity of target scaling factors;
the second obtaining module is used for obtaining the first quantity of average rainfall according to the first quantity of the first rainfall data and the average rainfall calculation method;
the first input module is used for inputting the average rainfall and the corresponding target model parameters into the hydrological model to obtain a first number of first forecasting results;
the second input module is used for inputting the first forecasting result into a target post-processing algorithm to obtain a target hydrologic set forecasting interval and a target deterministic forecasting result of the target river basin in the time period to be forecasted.
In a third aspect, the present invention provides a computer device comprising: the device comprises a memory and a processor, wherein the memory and the processor are in communication connection, the memory stores computer instructions, and the processor executes the computer instructions so as to execute the hydrologic set forecast construction method according to the first aspect or any implementation mode corresponding to the first aspect.
In a fourth aspect, the present invention provides a computer-readable storage medium having stored thereon computer instructions for causing a computer to execute the hydrologic set forecast construction method of the first aspect or any of the embodiments corresponding thereto.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow diagram of a hydrologic set forecast construction method in accordance with an embodiment of the present invention;
FIG. 2 is a schematic diagram of a hydrologic set prediction interval, deterministic prediction results, and runoff observation data comparison results according to an embodiment of the present invention;
FIG. 3 is a block diagram of a hydrologic set forecast construction apparatus in accordance with an embodiment of the present invention;
fig. 4 is a schematic diagram of a hardware structure of a computer device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The deterministic hydrologic forecasting only can give the most probable hydrologic process, uncertainty of forecasting is not considered, in the actual forecasting process, the deterministic hydrologic forecasting is impossible to completely coincide with reality, precision and error of the deterministic hydrologic forecasting can only be evaluated, reliability and effectiveness of forecasting cannot be reflected, and risk assessment and management information cannot be provided. The reliability and effectiveness of the prediction can be reflected on the basis of evaluating the prediction precision and error by the hydrologic set prediction interval and the deterministic prediction result provided by hydrologic set prediction. However, the existing hydrologic set prediction construction method generally adopts a data set such as a numerical weather prediction set, an analysis data set and the like as an input set, drives a distributed hydrologic model to construct a prediction set, is difficult to acquire and process, has low prediction precision, can be used after complex analysis and correction, and has less simulation precision in some watershed than a traditional lumped model.
Based on the above, the embodiment of the invention provides a hydrologic set prediction construction method, which constructs hydrologic set prediction by multiplying rainfall station observation data by a random scaling factor and comparing schemes, can construct hydrologic set prediction by a simpler method and at lower cost, and can be widely used in the improvement of most existing prediction schemes so as to achieve the effect of generally improving the prediction level of the existing hydrologic prediction.
According to an embodiment of the present invention, a hydrologic set forecast construction method is provided, and it should be noted that, steps shown in a flowchart of the accompanying drawings may be executed in an intelligent terminal having data processing capability, for example: smartphones, computers, etc., and, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in an order different than that shown or described herein.
In this embodiment, a method for constructing a hydrologic set forecast is provided, which may be used for the above intelligent terminals such as a smart phone and a computer, and fig. 1 is a flowchart of the method for constructing a hydrologic set forecast according to an embodiment of the present invention, as shown in fig. 1, where the flowchart includes the following steps:
step S101, obtaining initial rainfall data of a target river basin in a time period to be forecasted.
Specifically, the time period to be forecasted is 1-n days in the future, and n is greater than or equal to 1. And when the actual forecasting work is carried out, acquiring rainfall data of each rainfall station in the target flow field for forecasting 1-n days in the future, and taking the rainfall data as initial rainfall data.
Step S102, according to the combination of the initial rainfall data and the first number of target scaling factors, first rainfall data of a first number group is obtained.
Specifically, each target scaling factor combination comprises scaling factors corresponding to all initial rainfall data of the rainfall station. The initial rainfall data of each rainfall station are multiplied by the corresponding scaling factors in a first number of target scaling factor combinations respectively to obtain first rainfall data of a first number group, the first number represents a plurality of first rainfall data, the specific number is not needed here, and the target scaling factor combinations can be obtained through training according to historical samples or can be set in advance according to requirements.
Step S103, obtaining a first number of average rainfall according to the first rainfall data of the first number group and the average rainfall calculation method.
Specifically, the average rainfall of each set of first rainfall data is calculated separately using a rainfall spread method, i.e., an average rainfall calculation method, such as: such as the Thiessen polygon method, etc.
And step S104, inputting the average rainfall and the corresponding target model parameters into a hydrological model to obtain a first number of first forecasting results.
Specifically, the average rainfall and the target model parameters are input into a hydrological model together to obtain a first forecasting result of a first number of future 1-n days, the target model parameters can be obtained by parameter calibration of the hydrological model according to a historical sample or can be set in advance according to requirements, and the hydrological model is a lumped hydrological model, for example: a Xinanjiang model, a TANK (TANK) model, and the like.
Step S105, inputting the first forecasting result into a target post-processing algorithm to obtain a target hydrologic set forecasting interval and a target deterministic forecasting result of the target river basin in the time period to be forecasted.
Specifically, inputting the first forecasting result into a target post-processing algorithm to obtain a target hydrologic set forecasting interval and a target deterministic forecasting result of a target river basin of 1-n days in the future, wherein the target post-processing algorithm comprises the following steps: the target post-processing algorithm can be obtained by training the post-processing algorithm according to historical samples and can be set in advance according to requirements.
According to the hydrologic set forecast construction method provided by the embodiment, initial rainfall data are obtained, and the initial rainfall data are processed by utilizing target scaling factor combination to obtain first rainfall data; calculating the average rainfall of the first rainfall data, and obtaining a forecast result by using the average rainfall and a hydrological model; and carrying out hydrologic set forecasting construction according to the forecasting result and the target post-processing algorithm. The method has the advantages that the data used by the method is easy to obtain, the data processing mode is simple and effective, the hydrologic set forecasting construction can be carried out by a simpler method and with lower cost, and the method can be widely applied to the improvement of most of the existing forecasting schemes so as to achieve the effect of generally improving the forecasting level of the existing hydrologic forecasting. The method solves the problems that the data for constructing hydrologic set forecast is difficult to obtain and process and the construction method is complex.
In some alternative embodiments, prior to obtaining the initial rainfall data of the target basin for the period of time to be forecasted, the method further includes:
acquiring topographic data, first historical rainfall data and first runoff observation data of a second number of rainfall stations in a target flow domain in a first time period;
obtaining a second forecasting result according to the first historical rainfall data, the average rainfall calculation method and the hydrological model;
obtaining a reference evaluation index according to the second forecasting result, the first runoff observing data and a first preset formula;
determining a scaling factor combination from the terrain data, wherein the scaling factor combination comprises a scaling factor for each rain station;
obtaining second rainfall data of each rainfall station according to the first historical rainfall data of each rainfall station and the corresponding scaling coefficient;
obtaining a first model parameter and a third forecasting result according to the second rainfall data, the average rainfall calculation method and the hydrologic model;
obtaining an evaluation index according to the third forecasting result, the first runoff quantity observation data and a first preset formula;
if the evaluation index is larger than the reference evaluation index, combining the scaling factor combination, the first model parameter and the third forecasting result into a forecasting member, taking the scaling factor combination in the forecasting member as a target scaling factor combination, and taking the first model parameter as a target model parameter;
Starting to execute the subsequent steps from determining the scaling factor combinations according to the terrain data until a third number of scaling factor combinations are obtained, stopping to obtain a first number of forecasting members or starting to execute the subsequent steps from determining the scaling factor combinations according to the terrain data until a first number of forecasting members are obtained, stopping;
training the post-processing algorithm according to the first number of forecast members and the first runoff observation data to obtain a target post-processing algorithm.
Specifically, the first period of time is, for example: XXXX year-YYYY year. Determining basin location and range information for a target basin, such as: j river basin, and further determining the number of all rainfall stations in the target river basinThe quantity n, and the number of rain stations n is denoted as a second quantity, which represents a plurality, for example: 20. obtaining terrain data for a second number of rain stations in a target stream within a first time period, comprising: elevation data, topography relief data, etc. for the rainfall station. Collecting all rainfall daily data recorded by the rainfall stations in a first time period as first historical rainfall data, for example: collecting rainfall daily data of the J river basin of 20 rainfall stations XXXX year-YYYY year as first historical rainfall data, and recording the first historical rainfall data as P i I represents the rain level, i e (1, 2, …, n). The method comprises the steps of determining the position of a section to be forecasted of a target river basin, and taking a hydrological station in the target river basin as the section to be forecasted. And collecting daily traffic observation data recorded by the hydrologic station in a first time period as first traffic observation data.
Taking topographic data as elevation data of a rainfall station as an example, collecting elevation data h of the rainfall station in a target river basin i I epsilon (1, 2, …, n). The collected elevation data for the rainfall station is shown in table 1, where n=20.
TABLE 1 elevation data sheet for target basin rainfall station
The average rainfall of the first historical rainfall data is calculated according to the average rainfall calculation method, and then the average rainfall is input into a hydrologic model, and the hydrologic model outputs a second forecasting result.
The reference evaluation index may be a deterministic coefficient, a relative error, etc., when using a deterministic coefficientNSEAs an evaluation index, a first preset formula such as formula (1):
（1）
wherein,and->Respectively->Measured and simulated values of time, +.>For the length of the data sequences involved in the evaluation, +.>Representing the mean of the measured values. NSE is in the range of +.>The larger the value is, the higher the representing precision is, namely the better the simulation effect is, and the NSE value is 1 in an ideal state.
Determining a simulation value from the second forecasting result, for example:determining an actual measurement value from the first traffic volume observation data, for example: />And calculating the mean value of the measured and simulated processes from the simulated value and the measured value>And substituting the reference evaluation index into a first preset formula, and calculating to obtain the reference evaluation index. For example: based on the first historical rainfall data of 20 rainfall stations in J Jiang Liu domain of 2000-2011, the reference evaluation index calculated finally through the steps is 0.831.
In order to take into account the uncertainty of the rain observation of each rain station, the scaling factor of each rain station is determined according to the topography data, such as elevation data, of each rain station, and the scaling factors of all rain stations form a scaling factor combination. Multiplying the first historical rainfall data of each rainfall station by the scaling factor of the rainfall station to obtain second rainfall data considering rainfall observation uncertainty, and recording the second historical rainfall data as P i ' scaling the coefficientsRecorded as theta i I represents the rainfall station, i.e. (1, 2, …, n), thus P i ′=P i ×θ i 。
For the second rainfall data P taking into account the uncertainty of the rainfall observation i Firstly, calculating the average rainfall of the second rainfall data according to the average rainfall calculation method, inputting the average rainfall into a hydrologic model, and carrying out parameter calibration on the hydrologic model to obtain a first model parameter of the hydrologic model, and outputting a third forecasting result by the hydrologic model.
Redetermining the analog value based on the third forecast result, for example:determining an actual measurement value from the first traffic volume observation data, for example: />And calculating the mean value of the measured and simulated processes from the simulated value and the measured value>Substituting the evaluation index into a first preset formula, and calculating to obtain an evaluation index of the third forecasting result.
If the evaluation index is greater than the above-mentioned reference evaluation index, for example: 0.831, all the scaling factor combinations, the first model parameters and the third forecasting results are recorded and combined into a forecasting member. The scaling factor combination in the prediction member is referred to as a target scaling factor combination, and the first model parameter is referred to as a target model parameter.
Repeating the above process until a third number of scaling factor combinations are obtained or a first number of forecasting members are obtained, and stopping to obtain the first number of forecasting members, for example: and if the third number is 10000, when the sampling number of the scaling factors reaches 10000, 10000 scaling factor combinations are generated, and no matter how many forecast members are obtained, the process is stopped to be repeated to obtain 316 forecast members, and if the first number is 316. In addition, the first number may be set to 316 in advance, and no matter how many times the scaling factor is sampled, only 316 forecast members are obtained, so that the process will stop being repeated.
Training the post-processing algorithm by using third forecasting results and first runoff observing data in the first number of forecasting members to obtain a trained target post-processing algorithm for the target river basin, wherein the post-processing algorithm comprises the following steps: an algorithm based on a Bayesian model averaging method.
In the embodiment, the reference evaluation index is solved based on the first historical rainfall data, the scaling factor combination is determined according to the topographic data, the first historical rainfall data is adjusted according to the scaling factor combination to obtain the second rainfall data, the evaluation index is solved based on the second rainfall data, and the forecasting member is determined by comparing the evaluation index with the reference evaluation index, so that the method can utilize the target scaling factor combination and the target model parameter in the forecasting member to perform hydrologic set forecasting construction in a simpler method and at lower cost, can be widely used for improving most of existing forecasting schemes, and generally improves the forecasting level of the existing hydrologic forecasting.
In some alternative embodiments, obtaining the second forecast result according to the first historical rainfall data, the average rainfall calculation method and the hydrologic model includes:
Obtaining a first historical average rainfall according to the first historical rainfall data and an average rainfall calculation method;
and inputting the first historical average rainfall into a hydrological model to obtain a second forecasting result.
Specifically, first historical rainfall data P is calculated according to an average rainfall calculation method i Is referred to as the first historical average rainfall. And inputting the first historical average rainfall into a hydrologic model, and outputting a second forecasting result by the hydrologic model. The average rainfall calculation method may be a rainfall spread method, for example: thiessen polygon method, etc. The hydrologic model is a lumped hydrologic model, for example: a Xinanjiang model, a TANK model and the like.
In the embodiment, a first historical average rainfall of the first historical rainfall data is solved, and then a second forecasting result is obtained by using the first historical average rainfall and a hydrologic model, so that data support is provided for subsequent solving of the reference evaluation index.
In some alternative embodiments, inputting the first historical average rainfall into the hydrologic model to obtain a second forecast result, including:
inputting the first historical average rainfall into a hydrological model, and carrying out parameter calibration on the hydrological model to obtain a second model parameter;
And inputting the first historical average rainfall and the second model parameters into a hydrological model to obtain a second forecasting result.
Specifically, after the first historical average rainfall is input into the hydrologic model, parameter calibration is carried out on the hydrologic model, and the optimal model parameter combination of the hydrologic model is obtained through parameter calibration and is recorded as the second model parameter. The first historical average rainfall and the second model parameters are input into a hydrologic model, and the hydrologic model outputs a second forecasting result.
The essence of parameter calibration is that a group of parameters are firstly assumed, substituted into a hydrologic model to obtain a calculation result, then the calculation result is compared with measured data, and if the calculation result is not different from the measured data, the model parameter at the moment is used as a second model parameter of the hydrologic model; if the calculated result and the measured data have larger differences, the model parameters are adjusted to be substituted into the hydrologic model for recalculation, and then comparison is carried out until the error between the calculated result and the measured data meets a certain range, so that second model parameters of the hydrologic model are obtained.
In this embodiment, the hydrologic model is subjected to parameter calibration, and the second model parameters of the hydrologic model are determined. And inputting the first historical average rainfall and the second model parameters into a hydrological model to obtain a second forecasting result. The second forecasting result obtained by the process is more accurate and has smaller error.
In some alternative embodiments, determining a scaling factor combination from terrain data includes:
determining a variance value corresponding to each rainfall station according to the terrain data and a second preset formula, and determining a scaling factor value range of each rainfall station according to the variance value;
selecting the scaling coefficient of the corresponding rainfall station from each scaling coefficient value range to obtain a second number of scaling coefficients;
and obtaining a scaling factor combination according to the second number of scaling factors.
Specifically, the terrain data may be elevation data, topographic relief data, etc. of the rainfall station, and the steps of determining the scaling factor combination according to different terrain data are the same, and the elevation data of the rainfall station is taken as an example for explanation.
The scaling coefficient theta of each rainfall station is firstly set i Variance sigma of i I represents the rain station, i e (1, 2, …, n), for example: sigma of the station with the lowest altitude i Has a value of 0.5, sigma for the highest elevation rain station i Has a value of 2.5, sigma for the remaining stations i The value of (2) is linearly interpolated between 0.5 and 2.5 according to the elevation data, namely the value range is [0.5,2.5 ]]The second preset formula at this time may be formula (2):
（2）
Wherein,elevation data for the ith rain station,/-for the station>For the lowest elevation data of sea level in each rainfall station,is the elevation data for the highest elevation in each rain station.
Calculating a variance value corresponding to each rainfall station by using the elevation data and the formula (2), as shown in table 2:
TABLE 2 target basin rain station scaling factor variance table
Scaling factor θ for rainfall station i Obeying normal distribution with mean value of 1 and variance of sigma i, and scaling factor value range of each rainfall station is as follows: n is equal to (1, sigma i) and theta i The product of the process is denoted by E (0, ++ infinity a) of the above-mentioned components, for example: the scale factor of the rainfall station 1 takes the value range as follows: (0,1.145) the scale factor of the rain station 19 ranges from: (0,0.758).
For each rainfall station, from normal distribution N to (1, σi, and θi E (0), ++ infinity), namely, in the value range of the scaling coefficient corresponding to the rainfall station, randomly extracting one theta i As a scaling factor for the rain station. The target basin has a second number of rain stations in common, and therefore, a second number of scaling factors. And obtaining a scaling factor combination according to the second number of scaling factors.
In this embodiment, firstly, a variance value corresponding to each rainfall station is calculated according to the topographic data, a scaling factor value range is determined according to the variance value, and the scaling factor of the rainfall station is selected from the scaling factor value range. The rainfall observation uncertainty is considered through the scaling coefficient, the method is simple and effective, can be widely applied to the improvement of most existing forecasting schemes, and generally improves the forecasting level of the existing hydrologic forecasting.
In some alternative embodiments, obtaining the first model parameter and the third forecast result according to the second rainfall data, the average rainfall calculation method and the hydrologic model includes:
obtaining a second historical average rainfall according to the second rainfall data and the average rainfall calculation method;
inputting the second historical average rainfall into a hydrological model, and carrying out parameter calibration on the hydrological model to obtain a first model parameter;
and inputting the second historical average rainfall and the first model parameters into a hydrological model to obtain a third forecasting result.
Specifically, the average rainfall of the second rainfall data is calculated according to the average rainfall calculation method, and is recorded as the second historical average rainfall. And inputting the second historical average rainfall into the hydrologic model, and after inputting the second historical average rainfall into the hydrologic model, carrying out parameter calibration on the hydrologic model, obtaining an optimal model parameter combination of the hydrologic model through parameter calibration, and recording the optimal model parameter combination as a first model parameter. And inputting the second historical average rainfall and the first model parameters into a hydrologic model, wherein the hydrologic model outputs a third forecasting result. The average rainfall calculation method may be a rainfall spread method, for example: thiessen polygon method, etc. The hydrologic model is a lumped hydrologic model, for example: a Xinanjiang model, a TANK model and the like.
In this embodiment, a second historical average rainfall of the second rainfall data is solved first, the second historical average rainfall is input into the hydrologic model, and the hydrologic model is subjected to parameter calibration to determine a first model parameter of the hydrologic model. And inputting the second historical average rainfall and the first model parameters into a hydrological model to obtain a third forecasting result. The first model parameters provide a basis for the subsequent composition forecasting members, the third forecasting result of the solution is more accurate, and data support is provided for the subsequent solution evaluation indexes.
In some alternative embodiments, after obtaining the target post-processing algorithm, the method further comprises:
acquiring second historical rainfall data of a second number of rainfall stations in a target flow field in a second time period and second runoff observation data;
according to the second historical rainfall data and the target scaling factor combination, obtaining a first quantity group of third rainfall data;
obtaining a first number of third historical average rainfall according to the first number of third rainfall data and the average rainfall calculation method;
inputting the third historical average rainfall and corresponding target model parameters into a hydrological model to obtain a first number of fourth forecasting results;
Inputting the fourth forecasting result into a target post-processing algorithm to obtain a hydrologic set forecasting interval and a deterministic forecasting result of the target river basin in the second time period;
and obtaining a verification result according to the hydrologic set prediction interval, the deterministic prediction result and the second runoff observation data.
Specifically, the second period of time is, for example: AAAA year-BBBB year. Collecting the daily rainfall data recorded by all rainfall stations in the target stream in a second time period as second historical rainfall data, for example: collecting rainfall daily data of the J river basin of the 20 rainfall stations AAAA-BBBBBB year as second historical rainfall data, and recording the second historical rainfall data as R i I represents the rain level, i e (1, 2, …, n). And collecting daily traffic observation data recorded by the hydrologic station in a second time period as second traffic observation data.
Second historical rainfall data R of each rainfall station i Multiplying the scaling factor θ of the rain station in the target scaling factor combination i Obtaining third rainfall data R of the rainfall station taking the rainfall observation uncertainty into consideration i ′，R i ′=R i ×θ i . Because there is a first number of forecast members, i.e., a first number of target scaling factor combinations, a first number of sets of third rainfall data, each set of third rainfall data comprising third rainfall data for all of the rainfall stations, may ultimately be obtained.
The average rainfall of the first quantity group of third rainfall data is calculated according to the average rainfall calculation method, and is recorded as the third historical average rainfall. And inputting the third historical average rainfall and the target model parameters in the forecasting members into a hydrologic model, and outputting the first number of fourth forecasting results by the hydrologic model.
Inputting the fourth forecasting result into a target post-processing algorithm to obtain a hydrologic set forecasting interval and a deterministic forecasting result of the target river basin in the second time period; obtaining a verification result according to the hydrologic set prediction interval, the deterministic prediction result and the second runoff observation data, wherein the hydrologic set prediction interval comprises the runoff quantity (m as shown in fig. 2 3 S) and the mean value of the probability prediction interval and the probability prediction interval, the deterministic prediction result comprises the runoff quantity (m 3 S), wherein the forecast in fig. 2 is the fourth forecast. The "measured course" curve represents the flow according to the second runoffThe amount of runoff (m) determined from the measurement data 3 /s) is used to compare the forecast results. FIG. 2 shows the comparison of hydrologic set forecast intervals, deterministic forecast results and traffic observation data for a period of 530 th to 565 th. Moreover, as can be seen from fig. 2, the accuracy of the hydrologic set forecast constructed by the invention is higher, and the invention has higher practical value.
In this embodiment, the hydrologic set prediction interval and the deterministic prediction result of the target river basin in the second time period are determined first, the hydrologic set prediction interval and the deterministic prediction result are compared with the second runoff observation data of the target river basin in the second time period, a verification result is obtained, and the effectiveness and the reliability of the invention can be analyzed by using the verification result.
The present embodiment provides a hydrologic set forecast construction device, as shown in fig. 3, including:
a first obtaining module 301, configured to obtain initial rainfall data of a target river basin in a period to be forecasted;
a first obtaining module 302, configured to obtain a first amount of first rainfall data of a first amount group according to the combination of the initial rainfall data and a first amount of target scaling coefficients;
a second obtaining module 303, configured to obtain a first number of average rainfall according to the first number of groups of first rainfall data and the average rainfall calculation method;
the first input module 304 is configured to input the average rainfall and the corresponding target model parameters into the hydrological model, so as to obtain a first number of first forecast results;
the second input module 305 is configured to input the first prediction result into a target post-processing algorithm, so as to obtain a target hydrologic set prediction interval and a target deterministic prediction result of the target river basin in the time period to be predicted.
In some alternative embodiments, the apparatus further comprises:
the second acquisition module is used for acquiring the topographic data, the first historical rainfall data and the first runoff observation data of a second number of rainfall stations in the target flow field in the first time period;
the third obtaining module is used for obtaining a second forecasting result according to the first historical rainfall data, the average rainfall calculation method and the hydrologic model;
a fourth obtaining module, configured to obtain a reference evaluation index according to the second prediction result, the first traffic observation data and the first preset formula;
a determining module for determining a combination of scaling factors from the terrain data, wherein the combination of scaling factors comprises a scaling factor for each rain station;
a fifth obtaining module, configured to obtain second rainfall data of each rainfall station according to the first historical rainfall data of each rainfall station and the corresponding scaling factor;
a sixth obtaining module, configured to obtain a first model parameter and a third prediction result according to the second rainfall data, the average rainfall calculation method, and the hydrologic model;
a seventh obtaining module, configured to obtain an evaluation index according to the third prediction result, the first traffic volume observation data, and the first preset formula;
The combination module is used for combining the scaling factor combination, the first model parameter and the third forecasting result into a forecasting member if the evaluation index is larger than the reference evaluation index, taking the scaling factor combination in the forecasting member as a target scaling factor combination and taking the first model parameter as a target model parameter;
the circulation module is used for starting to execute the subsequent steps from the scaling factor combination determined according to the topographic data until a third number of scaling factor combinations are obtained, stopping to obtain a first number of forecast members or starting to execute the subsequent steps from the scaling factor combination determined according to the topographic data until the first number of forecast members are obtained, stopping;
and the eighth obtaining module is used for training the post-processing algorithm according to the first number of forecast members and the first runoff observation data to obtain a target post-processing algorithm.
In some alternative embodiments, the third deriving module comprises:
the first obtaining unit is used for obtaining the first historical average rainfall according to the first historical rainfall data and the average rainfall calculation method;
the first input unit is used for inputting the first historical average rainfall into the hydrologic model to obtain a second forecasting result.
In some alternative embodiments, the first input unit includes:
the first input submodule is used for inputting the first historical average rainfall into the hydrologic model, and carrying out parameter calibration on the hydrologic model to obtain second model parameters;
and the second input sub-module is used for inputting the first historical average rainfall and the second model parameters into the hydrologic model to obtain a second forecasting result.
In some alternative embodiments, the determining module includes:
the determining unit is used for determining a variance value corresponding to each rainfall station according to the terrain data and a second preset formula, and determining a scaling factor value range of each rainfall station according to the variance value;
the second obtaining unit is used for selecting the scaling coefficient of the corresponding rainfall station from each scaling coefficient value range to obtain a second number of scaling coefficients;
and the third obtaining unit is used for obtaining a scaling coefficient combination according to the second number of scaling coefficients.
In some alternative embodiments, the sixth obtaining module comprises:
a fourth obtaining unit, configured to obtain a second historical average rainfall according to the second rainfall data and the average rainfall calculation method;
the second input unit is used for inputting a second historical average rainfall into the hydrologic model, and carrying out parameter calibration on the hydrologic model to obtain a first model parameter;
And the third input unit is used for inputting the second historical average rainfall and the first model parameters into the hydrologic model to obtain a third forecasting result.
In some alternative embodiments, the apparatus further comprises:
the third acquisition module is used for acquiring second historical rainfall data and second runoff observation data of a second number of rainfall stations in the target flow field in a second time period;
a ninth obtaining module, configured to obtain a first number group of third rainfall data according to the second historical rainfall data and the target scaling factor combination;
a tenth obtaining module, configured to obtain a first number of third historical average rainfall according to the first number of third rainfall data and the average rainfall calculation method;
the third input module is used for inputting the third historical average rainfall and the corresponding target model parameters into the hydrologic model to obtain a first number of fourth forecasting results;
the fourth input module is used for inputting a fourth forecasting result into the target post-processing algorithm to obtain a hydrologic set forecasting interval and a deterministic forecasting result of the target river basin in the second time period;
and the eleventh obtaining module is used for obtaining a verification result according to the hydrologic set prediction interval, the deterministic prediction result and the second runoff observation data.
Further functional descriptions of the above respective modules and units are the same as those of the above corresponding embodiments, and are not repeated here.
The hydrologic set forecast construction means in this embodiment are presented in the form of functional units, here ASIC (Application Specific Integrated Circuit ) circuits, processors and memories executing one or more software or fixed programs, and/or other devices that can provide the above described functionality.
The embodiment of the invention also provides computer equipment, which is provided with the hydrologic set forecast construction device shown in the figure 3.
Referring to fig. 4, fig. 4 is a schematic structural diagram of a computer device according to an alternative embodiment of the present invention, as shown in fig. 4, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components are communicatively coupled to each other using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions executing within the computer device, including instructions stored in or on memory to display graphical information of the GUI on an external input/output device, such as a display device coupled to the interface. In some alternative embodiments, multiple processors and/or multiple buses may be used, if desired, along with multiple memories and multiple memories. Also, multiple computer devices may be connected, each providing a portion of the necessary operations (e.g., as a server array, a set of blade servers, or a multiprocessor system). One processor 10 is illustrated in fig. 4.
The processor 10 may be a central processor, a network processor, or a combination thereof. The processor 10 may further include a hardware chip, among others. The hardware chip may be an application specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general-purpose array logic, or any combination thereof.
Wherein the memory 20 stores instructions executable by the at least one processor 10 to cause the at least one processor 10 to perform a method for implementing the embodiments described above.
The memory 20 may include a storage program area that may store an operating system, at least one application program required for functions, and a storage data area; the storage data area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, memory 20 may optionally include memory located remotely from processor 10, which may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
Memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk, or solid state disk; the memory 20 may also comprise a combination of the above types of memories.
The computer device also includes a communication interface 30 for the computer device to communicate with other devices or communication networks.
The embodiments of the present invention also provide a computer readable storage medium, and the method according to the embodiments of the present invention described above may be implemented in hardware, firmware, or as a computer code which may be recorded on a storage medium, or as original stored in a remote storage medium or a non-transitory machine readable storage medium downloaded through a network and to be stored in a local storage medium, so that the method described herein may be stored on such software process on a storage medium using a general purpose computer, a special purpose processor, or programmable or special purpose hardware. The storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, a solid state disk or the like; further, the storage medium may also comprise a combination of memories of the kind described above. It will be appreciated that a computer, processor, microprocessor controller or programmable hardware includes a storage element that can store or receive software or computer code that, when accessed and executed by the computer, processor or hardware, implements the methods illustrated by the above embodiments.
Although embodiments of the present invention have been described in connection with the accompanying drawings, various modifications and variations may be made by those skilled in the art without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope of the invention as defined by the appended claims.

Claims (7)

1. A hydrologic set forecast construction method, the method comprising:
acquiring topographic data, first historical rainfall data and first runoff observation data of a second number of rainfall stations in a target flow domain in a first time period;
obtaining a second forecasting result according to the first historical rainfall data, the average rainfall calculation method and the hydrologic model;
obtaining a reference evaluation index according to the second forecasting result, the first runoff observation data and a first preset formula, wherein the reference evaluation index is a deterministic coefficientNSEThe first preset formula is thatWherein->For the first traffic observation data +.>Measured value of time>For obtaining +.>Analog value of time of day->For the length of the data sequences involved in the evaluation, +.>For said->Is the average value of (2);
determining a combination of scaling factors from the terrain data, wherein the combination of scaling factors includes scaling factors for each of the rain stations, the determining a combination of scaling factors from the terrain data comprising: determining a variance value corresponding to each rainfall station according to the terrain data and a second preset formula, and determining a scaling factor value range of each rainfall station according to the variance value; selecting the scaling coefficients of the corresponding rainfall stations from each scaling coefficient value range to obtain the second number of scaling coefficients; obtaining the scaling factor combination according to the second number of scaling factors;
Obtaining second rainfall data of each rainfall station according to the first historical rainfall data of each rainfall station and the corresponding scaling coefficient;
obtaining a first model parameter and a third forecasting result according to the second rainfall data, the average rainfall calculation method and the hydrologic model, wherein obtaining the first model parameter and the third forecasting result according to the second rainfall data, the average rainfall calculation method and the hydrologic model comprises the following steps: obtaining a second historical average rainfall according to the second rainfall data and the average rainfall calculation method; inputting the second historical average rainfall into the hydrologic model, and carrying out parameter calibration on the hydrologic model to obtain the first model parameter; inputting the second historical average rainfall and the first model parameters into the hydrological model to obtain the third forecasting result;
obtaining an evaluation index according to the third forecasting result, the first runoff observing data and the first preset formula;
if the evaluation index is larger than the reference evaluation index, combining the scaling factor combination, the first model parameter and the third forecasting result into one forecasting member, taking the scaling factor combination in the forecasting member as a target scaling factor combination, and taking the first model parameter as a target model parameter;
Starting to execute the subsequent steps from the scaling factor combination determined according to the topographic data until a third number of scaling factor combinations are obtained or a first number of forecasting members are obtained, stopping, and if the third number of scaling factor combinations are obtained, obtaining the first number of forecasting members according to the third number of scaling factor combinations;
training a post-processing algorithm according to the first number of forecast members and the first runoff observation data to obtain a target post-processing algorithm;
acquiring initial rainfall data of a target river basin in a time period to be forecasted;
obtaining first rainfall data of the first quantity group according to the combination of the initial rainfall data and the first quantity of target scaling coefficients;
obtaining the first quantity of average rainfall according to the first rainfall data of the first quantity group and an average rainfall calculation method;
inputting the average rainfall and the corresponding target model parameters into a hydrological model to obtain a first number of first forecasting results;
and inputting the first forecasting result into a target post-processing algorithm to obtain a target hydrologic set forecasting interval and a target deterministic forecasting result of the target river basin in the time period to be forecasted, wherein the target post-processing algorithm is an algorithm based on a Bayesian model averaging method.
2. The method of claim 1, wherein the obtaining a second prediction result according to the first historical rainfall data, the average rainfall calculation method, and the hydrologic model includes:
obtaining a first historical average rainfall according to the first historical rainfall data and the average rainfall calculation method;
and inputting the first historical average rainfall into the hydrologic model to obtain the second forecasting result.
3. The method of claim 2, wherein said inputting the first historical average rainfall into the hydrological model to obtain the second forecast result comprises:
inputting the first historical average rainfall into the hydrologic model, and carrying out parameter calibration on the hydrologic model to obtain a second model parameter;
and inputting the first historical average rainfall and the second model parameters into the hydrologic model to obtain the second forecasting result.
4. The method of claim 1, wherein after said deriving said target post-processing algorithm, said method further comprises:
acquiring second historical rainfall data of the second number of rainfall stations in the target flow field in a second time period and second runoff observation data;
Obtaining third rainfall data of the first quantity group according to the combination of the second historical rainfall data and the target scaling coefficient;
obtaining a first number of third historical average rainfall according to the first number of third rainfall data and the average rainfall calculation method;
inputting the third historical average rainfall and the corresponding target model parameters into the hydrologic model to obtain the first number of fourth forecasting results;
inputting the fourth forecasting result into the target post-processing algorithm to obtain a hydrologic set forecasting interval and a deterministic forecasting result of the target river basin in the second time period;
and obtaining a verification result according to the hydrologic set prediction interval, the deterministic prediction result and the second runoff observation data.
5. A hydrologic set forecast construction device, the device comprising:
the second acquisition module is used for acquiring the topographic data, the first historical rainfall data and the first runoff observation data of a second number of rainfall stations in the target flow field in the first time period;
the third obtaining module is used for obtaining a second forecasting result according to the first historical rainfall data, the average rainfall calculation method and the hydrologic model;
A fourth step of obtaining a module, wherein,the method is used for obtaining a reference evaluation index according to the second forecasting result, the first runoff quantity observation data and a first preset formula, wherein the reference evaluation index is a deterministic coefficientNSEThe first preset formula is thatWherein->For the first traffic observation data +.>Measured value of time>For obtaining +.>Analog value of time of day->For the length of the data sequences involved in the evaluation, +.>For said->Is the average value of (2);
a determination module for determining a combination of scaling factors from the terrain data, wherein the combination of scaling factors comprises scaling factors for each of the rain stations, wherein the determination module comprises: the determining unit is used for determining a variance value corresponding to each rainfall station according to the terrain data and a second preset formula, and determining a scaling factor value range of each rainfall station according to the variance value; the second obtaining unit is used for selecting the scaling coefficient of the corresponding rainfall station from each scaling coefficient value range to obtain a second number of scaling coefficients; a third obtaining unit, configured to obtain a scaling factor combination according to the second number of scaling factors;
a fifth obtaining module, configured to obtain second rainfall data of each rainfall station according to the first historical rainfall data of each rainfall station and the corresponding scaling factor;
A sixth obtaining module, configured to obtain a first model parameter and a third prediction result according to the second rainfall data, the average rainfall calculation method, and the hydrologic model, where the sixth obtaining module includes: a fourth obtaining unit, configured to obtain a second historical average rainfall according to the second rainfall data and the average rainfall calculation method; the second input unit is used for inputting a second historical average rainfall into the hydrologic model, and carrying out parameter calibration on the hydrologic model to obtain a first model parameter; the third input unit is used for inputting the second historical average rainfall and the first model parameters into the hydrological model to obtain a third forecasting result;
a seventh obtaining module, configured to obtain an evaluation index according to the third prediction result, the first traffic volume observation data, and the first preset formula;
the combination module is used for combining the scaling factor combination, the first model parameter and the third forecasting result into a forecasting member if the evaluation index is larger than the reference evaluation index, taking the scaling factor combination in the forecasting member as a target scaling factor combination and taking the first model parameter as a target model parameter;
a circulation module, configured to perform subsequent steps from the determining of a scaling factor combination according to the terrain data, until a third number of scaling factor combinations is obtained or a first number of forecasting members is obtained, and stop, if the third number of scaling factor combinations is obtained, obtaining the first number of forecasting members according to the third number of scaling factor combinations;
The eighth obtaining module is used for training the post-processing algorithm according to the first number of forecast members and the first runoff observation data to obtain a target post-processing algorithm;
the first acquisition module is used for acquiring initial rainfall data of the target river basin in the time period to be forecasted;
the first obtaining module is used for obtaining the first rainfall data of the first quantity group according to the combination of the initial rainfall data and the first quantity of target scaling factors;
the second obtaining module is used for obtaining the first quantity of average rainfall according to the first quantity of groups of the first rainfall data and the average rainfall calculation method;
the first input module is used for inputting the average rainfall and the corresponding target model parameters into a hydrological model to obtain the first number of first forecasting results;
the second input module is used for inputting the first forecasting result into a target post-processing algorithm to obtain a target hydrologic set forecasting interval and a target deterministic forecasting result of the target river basin in the time period to be forecasted, wherein the target post-processing algorithm is an algorithm based on a Bayesian model averaging method.
6. A computer device, comprising:
A memory and a processor, the memory and the processor being communicatively connected to each other, the memory having stored therein computer instructions, the processor executing the computer instructions to perform the hydrologic set forecast construction method of any of claims 1 to 4.
7. A computer-readable storage medium having stored thereon computer instructions for causing a computer to perform the hydrologic set forecast construction method of any of claims 1 to 4.
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