CN114611778A - Reservoir water level early warning method and system based on warehousing flow - Google Patents
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Abstract
The invention relates to a reservoir water level early warning method and system based on warehousing flow, the system comprises a data acquisition module, a data transmission module, a reservoir capacity calculation module, a reservoir water level prediction module and an early warning module, the steps are S1 acquisition of historical and real-time reservoir capacity and reservoir water level data, S2 calculation of a reservoir capacity value in a future time period, S3 construction of a reservoir water level prediction model through multi-layer sensor model training, S4 prediction of a reservoir water level value in the future days, and S5 comparison of the predicted reservoir water level value and a water level warning value is carried out for early warning. The method has the advantages that the accuracy of the prediction result is high, the water level change data of the next several days can be predicted, the method has trend, the training model has strong prediction capability, the characteristics such as geographic environment and the like are fully considered, the hidden characteristics are trained and merged into the prediction model, and the fitting effect is very good.
Description
Technical Field
The invention relates to the technical field of multivariate time series information mining and reservoir safety early warning, in particular to a reservoir water level early warning method and system based on warehousing traffic.
Background
With the rapid development of society, China gradually starts large-scale flood control construction, flood control projects are enhanced, and flood is controlled to a certain degree. However, in China, medium and small rivers are numerous and have the characteristics of rapid river water level increase, rapid water flow turbulence and the like, once strong rainfall occurs, flood can be caused, the flow of the reservoir is rapidly increased, and the reservoir breaks and collapses under the more serious condition. How to predict the reservoir water level change condition in the next few days is of great significance to the flood control dispatching department.
In the prior art, a single-feature-based time training model is used for water level prediction, although modal decomposition is carried out, the model has strong dependence on time series data, and if the predicted reservoir engineering changes a little, the prediction capability of the prediction model is greatly reduced. And the water level prediction time is short and has no tendency.
Disclosure of Invention
The invention aims to overcome the defects and provides a reservoir water level early warning method and system based on warehousing flow.
The technical scheme adopted by the invention is as follows:
a reservoir water level early warning method based on warehousing traffic comprises the following steps:
s1, collecting historical and real-time reservoir capacity and reservoir water level data, transmitting the data and storing the data in a reservoir water regime database;
s2, acquiring warehousing flow in the next several days based on a known warehousing flow forecasting model, acquiring current reservoir capacity data from a reservoir water regime database, and calculating a reservoir capacity value in the future time period;
s3, obtaining historical reservoir capacity and reservoir water level data from a database, dividing the historical reservoir capacity and reservoir water level data into a training set and a verification set, constructing a multilayer sensor model for predicting the reservoir water level based on the training set, verifying the performance of the model by using the verification set, adjusting model parameters according to the value obtained by the Root Mean Square Error (RMSE) of an evaluation function, and circularly iterating the training model to obtain the optimal parameters and the model structure of the model as a reservoir water level prediction model until the RMSE value predicted by the model is minimum;
s4, predicting the reservoir water level value of the future days by using the reservoir water level prediction model obtained in the step S3 according to the reservoir capacity value of the future time period obtained in the step S2;
and S5, determining the disaster situation according to the comparison between the predicted reservoir water level value and the water level warning value, and thus linking the visual platform to perform early warning.
In the method for early warning of the water level of the reservoir based on the warehousing traffic, the method for calculating the volume value of the reservoir in the step S2 is as follows:
wherein b istThe storage capacity of the reservoir at the current moment is shown, t shows the time of the current moment, and n is the time length; xTRepresenting the forecast warehousing flow on the Tth day; f. ofnIndicating the reservoir capacity on day n.
The specific case of reservoir capacity of t +1, t +2, …, t + n days can be calculated respectively, n is preferably 5 in the present invention.
The multilayer perceptron model multilayer perceptron network described in step S3 has three structures, namely an input layer, a hidden layer and an output layer, and the corresponding network input X ═ X1,x2,x3,…,xd]The corresponding network output O ═ O1,o2,o3,…,oq]The output of the hidden layer is H, and the weight parameter and the deviation parameter of the hidden layer are W respectivelyhAnd bhThe weight and deviation parameters of the output layer are WoAnd boThe objective function of the network model is:
O＝(XWh+bh)Wo+bo＝XWhWo+bhWo+bo。
the evaluation function is as follows:
where RMSE denotes the root mean square error, aiRepresenting true value, piRepresenting the predicted value, and m representing the number of predicted data samples.
The utility model provides a reservoir water level early warning system based on flow of putting in storage, including data acquisition module, data transmission module, reservoir capacity calculation module, reservoir water level prediction module and early warning module, data acquisition module is the water level of collecting hydrology monitoring station measurement, reservoir capacity data, data transmission module is responsible for transmitting the measuring result to long-range server, and save in the reservoir water regime database, reservoir capacity calculation module is used for calculating the reservoir capacity transform condition in the next several days, reservoir water level prediction module is used for based on reservoir capacity prediction reservoir water level, early warning module is used for sending out early warning information when surpassing the waters warning value according to the prediction water level.
The invention has the beneficial effects that:
(1) on the basis of forecasting the warehousing flow, the invention combines the research of the relation between the reservoir water regime data and the reservoir geographical features, fully excavates the relation between the reservoir capacity and the reservoir water level, and constructs a future reservoir water level forecasting model by using a multilayer sensor algorithm technology.
(2) The method can predict water level change data of several days in the future, has trend, has strong prediction capability of the training model, fully considers the characteristics of geographic environment and the like, trains and integrates the hidden characteristics into the prediction model, has very good fitting effect, and also has good performance on sparse data.
Drawings
Fig. 1 is a flow chart of a reservoir water level early warning method based on warehousing traffic according to the present invention.
Fig. 2 is a block diagram of the early warning system according to the present invention.
Fig. 3 is a diagram showing the effect of fitting the predicted value and the observed value of the water level of the space reservoir in embodiment 3 of the present invention.
Detailed Description
The invention is described in further detail below with reference to the figures and specific examples.
Example 1
The embodiment is a method for predicting the storage capacity of a flood season reservoir, namely a method for calculating the capacity value of the reservoir in a future time period, and comprises the following steps:
(1) the method comprises the steps that a reservoir hydrological monitoring station for measuring reservoir capacity and reservoir water level is arranged on a reservoir, historical and real-time reservoir capacity and reservoir water level data are collected and stored in a reservoir water regime database;
(2) acquiring warehousing flow in the next several days based on a known warehousing flow forecasting model (such as a hydrodynamic model in the prior art), and then transmitting the forecasted warehousing flow data to a storage capacity calculation module under the condition that the warehousing flow at the future moment is known; meanwhile, statistical analysis is carried out on the capacity data of the reservoir capacity of the historical site reservoir stored in the database, and the data are preprocessed, so that the capacity data of the reservoir on the same day are obtained and transmitted to the capacity calculation module;
(3) based on the current reservoir capacity and the warehousing flow in the next days, the reservoir capacity calculation module is used for calculating the reservoir capacity change condition in the next days, and the calculation method of the reservoir capacity calculation module is as follows:
wherein b istThe storage capacity of the reservoir at the moment of the day is represented, t represents the time of the day, and n is the time length; xTRepresenting the forecast warehousing flow on the Tth day; f. ofnIndicates the reservoir capacity of the nth dayAn amount;
(4) according to the specific situation that the storage capacity calculation module can calculate the storage capacity of t +1, t +2, …, t + n days, respectively, n is set to 5 in the present embodiment.
Example 2
The embodiment is a flood season reservoir water level prediction method, which comprises the following steps:
(1) reading historical reservoir capacity and reservoir water level data from a reservoir water regime database, analyzing the data, cleaning abnormal or missing data, perfecting the data, storing the perfected data in a corresponding format into a training file, and acquiring training data from the training file, wherein the training data comprises N pieces of data, and each piece of data is represented as Vn＝{v1,v2N denotes the nth sample in the training data, v1Indicating reservoir capacity, v2Representing the reservoir water level, and dividing the training data into a training set and a verification set;
(2) the method comprises the steps of constructing an initialized multi-layer sensor network, wherein the multi-layer sensor network has three structures, namely an input layer, a hidden layer and an output layer, and the corresponding network input X is [ X ]1,x2,x3,…,xd]The corresponding network output O ═ O1,o2,o3,…,oq]The output of the hidden layer is H, and the weight parameter and the deviation parameter of the hidden layer are W respectivelyhAnd bhThe weight and deviation parameters of the output layer are WoAnd boThe objective function of the network model is: o ═ X (XW)h+bh)Wo+bo＝XWhWo+bhWo+bo
Relu is used as an activation function of the network model, and the definition of the activation function is as follows:
ReLU(x)＝max(x,0)；
(3) training an initial network model by using a training set which is normalized by a linear function to obtain a prediction model of reservoir water level, verifying the performance of the model by using a normalized verification set, adjusting model parameters according to a value obtained by an evaluation function Root Mean Square Error (RMSE), and iteratively training the model in a circulating manner until the RMSE value of the prediction model is minimum, so that the optimal parameters and the model structure of the model are obtained, a hidden layer is three layers, and the number of neurons in each layer is 20;
(4) obtaining optimal model parameters and model structures according to model training, and constructing a multilayer sensor model for predicting the reservoir water level; on the basis of obtaining the capacity of the reservoir in the future time period in the embodiment 1, predicting the water level condition of the reservoir in the future days by using a reservoir water level prediction model;
(5) performing disaster situation determination according to the comparison of the predicted water level value and the warning water level value, and linking a visual platform to perform display early warning; when the reservoir water level prediction module predicts that the reservoir water level value in the next few days exceeds the warning value, immediately issuing early warning; and acquiring early warning information, and performing corresponding scheduling treatment such as opening a gate to discharge water by combining with the change trend of the water level prediction value in the next several days so that the reservoir can bear a certain amount of flood after water discharge.
Example 3
As shown in fig. 2, the reservoir water level early warning system based on the warehousing flow comprises a data acquisition module, a data transmission module, a reservoir capacity calculation module, a reservoir water level prediction module and an early warning module, wherein the data acquisition module is used for collecting water level and reservoir capacity data measured by a hydrological monitoring station, the data transmission module is used for transmitting a measurement result to a remote server and storing the measurement result in a reservoir water situation database, the reservoir capacity calculation module is used for calculating the reservoir capacity conversion condition in the next several days, the reservoir water level prediction module is used for predicting the reservoir water level based on the reservoir capacity, and the early warning module is used for sending out early warning information according to the predicted water level when the predicted water level exceeds the water area warning value.
The simulation of the early warning system in this embodiment is run in a hardware environment with a CPU with a master frequency of 1.6GHZ and a memory of 16GB and a software environment with python3.6, keras2.2.4 and Pycharm 2016.
The operation early warning method comprises the steps of embodiment 1 and embodiment 2.
The reservoir to be tested is a Taihe reservoir, historical water situation data of a basin of the Taihe reservoir are adopted, the historical data are 1994-01-10 to 2021-02-01, 2000-01-01 to 2021-02-01 are selected as data sets through screening, and then the data sets are segmented according to the proportion of 4:1 and are respectively used as a training set and a testing set.
FIG. 3 is a graph of the effect of fitting the predicted value and observed value of the level of the Taihe reservoir by the method of the present invention;
in this example, the method proposed by the present invention is compared with the following model prediction methods, and the prediction ability of the model is measured by using the evaluation index (RMSE), and the comparison results are shown in the following table:
TABLE 1 two model prediction comparisons
As can be seen from Table 1, the prediction of the reservoir level of the Taihe river is more accurate than the prediction capability of the integrated model.
The present invention has been described in detail with reference to the embodiments, and it should be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit of the invention.
Claims (6)
1. A reservoir water level early warning method based on warehousing traffic is characterized by comprising the following steps:
s1, collecting historical and real-time reservoir capacity and reservoir water level data, transmitting the data and storing the data in a reservoir water regime database;
s2, acquiring warehousing flow in the next several days based on a known warehousing flow forecasting model, acquiring current reservoir capacity data from a reservoir water regime database, and calculating a reservoir capacity value in the future time period;
s3, obtaining historical reservoir capacity and reservoir water level data from a database, dividing the historical reservoir capacity and reservoir water level data into a training set and a verification set, constructing a multilayer sensor model for predicting the reservoir water level based on the training set, verifying the performance of the model by using the verification set, adjusting model parameters according to the value obtained by the Root Mean Square Error (RMSE) of an evaluation function, and circularly iterating the training model to obtain the optimal parameters and the model structure of the model as a reservoir water level prediction model until the RMSE value predicted by the model is minimum;
s4, predicting the reservoir water level value of the future days by using the reservoir water level prediction model obtained in the step S3 according to the reservoir capacity value of the future time period obtained in the step S2;
and S5, determining the disaster situation according to the comparison between the predicted reservoir water level value and the water level warning value, and thus linking the visual platform to perform early warning.
2. The method for early warning of water level in reservoir based on warehousing traffic as claimed in claim 1, wherein the method for calculating the capacity value of reservoir in step S2 is as follows:
wherein b istThe storage capacity of the reservoir at the current moment is shown, t shows the time of the current moment, and n is the time length; xTRepresenting the forecast warehousing flow on the Tth day; f. ofnIndicating the reservoir capacity on day n.
3. The reservoir water level early warning method based on warehousing traffic as claimed in claim 2, wherein n is 5.
4. The reservoir water level early warning method based on warehousing traffic as claimed in claim 1, wherein the multi-layer sensor model multi-layer sensor network described in step S3 has three structures, i.e. an input layer, a hidden layer and an output layer, respectively, and the corresponding network input X ═ X1,x2,x3,…,xd]The corresponding network output O ═ O1,o2,o3,…,oq]The output of the hidden layer is H, and the weight parameter and the deviation parameter of the hidden layer are W respectivelyhAnd bhThe weight and deviation parameters of the output layer are WoAnd boThe objective function of the network model is:
O＝(XWh+bh)Wo+bo＝XWhWo+bhWo+bo。
5. the reservoir water level early warning method based on warehousing traffic as claimed in claim 1, wherein the evaluation function is as follows:
where RMSE denotes the root mean square error, aiRepresenting true value, piRepresenting the predicted value, and m representing the number of predicted data samples.
6. The reservoir water level early warning system based on the warehousing flow is characterized by comprising a data acquisition module, a data transmission module, a reservoir capacity calculation module, a reservoir water level prediction module and an early warning module, wherein the data acquisition module is used for collecting water level and reservoir capacity data measured by a hydrological monitoring station, the data transmission module is used for transmitting a measurement result to a remote server and storing the measurement result in a reservoir water situation database, the reservoir capacity calculation module is used for calculating the reservoir capacity conversion condition in the next several days, the reservoir water level prediction module is used for predicting the reservoir water level based on the reservoir capacity, and the early warning module is used for sending out early warning information according to the predicted water level when the predicted water level exceeds the water area warning value.
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| CN115713165A (en) * | 2022-11-28 | 2023-02-24 | 中国长江电力股份有限公司 | Reservoir ultra-short term water level prediction method and system considering dynamic reservoir capacity influence and storage medium |
| CN116258278A (en) * | 2023-05-10 | 2023-06-13 | 青岛研博数据信息技术有限公司 | Method, system and equipment for deducing constructed water level reservoir capacity |
| CN117689501A (en) * | 2024-02-03 | 2024-03-12 | 深圳市广汇源环境水务有限公司 | Reservoir flood prevention dynamic forecasting and early warning method and system platform |
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