CN115330088A - Flood control fine prediction method for small reservoir - Google Patents
Flood control fine prediction method for small reservoir Download PDFInfo
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Abstract
The invention relates to the field of flood control refined prediction, in particular to a flood control refined prediction method for a small reservoir, which comprises the following steps: acquiring reservoir water level data at a moment to be predicted by using historical meteorological data of the small reservoir; the flood control refined prediction result of the small reservoir is obtained by using the reservoir water level data of the moment to be predicted, the reservoir flood control information of each moment in the future is forecasted according to the historical meteorological information of the position of the reservoir, the output prediction quantity is screened according to the data before and after the prediction moment while the prediction data is obtained, the most approximate predicted water level data can be obtained, the prediction accuracy is improved, the prediction range is expanded, the flood control and flood discharge measures of the reservoir can be adjusted according to the predicted reservoir water level data, and the safety of upstream and downstream watersheds of the reservoir is guaranteed.
Description
Technical Field
The invention relates to the field of flood control refined prediction, in particular to a flood control refined prediction method for a small reservoir.
Background
Due to the water flow phenomenon that the water quantity is rapidly increased or the water level rapidly rises, when special meteorological conditions occur in a drainage basin, the water flow is increased, the water level correspondingly rises, but the small reservoir is easily influenced by weather conditions due to small stock, the change is large, the rising speed of flood is high, the real-time collection of water conservancy water level data cannot be timely processed, the situation of the water level change of the reservoir can not be accurately predicted only by the recent meteorological conditions, and the adverse effects are caused on the work of flood control processing and the like.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a flood control fine prediction method for a small reservoir, which is used for acquiring reservoir water level data at a moment to be predicted through historical meteorological data.
In order to achieve the purpose, the invention provides a flood control fine prediction method for a small reservoir, which comprises the following steps:
s1, acquiring reservoir water level data at a moment to be predicted by using historical meteorological data of a small reservoir;
s2, obtaining a flood control refined prediction result of the small reservoir by using the reservoir water level data at the moment to be predicted;
s2-1, judging whether the water level data of the reservoir at the moment to be predicted is larger than a flood control water level standard, if so, using the historical water level data which is most similar to the water level data of the reservoir at the moment to be predicted at the same time as the flood control fine prediction result of the small reservoir, otherwise, using the water level data of the reservoir at the moment to be predicted as the flood control fine prediction result of the small reservoir.
Preferably, the acquiring of the water level data of the small reservoir at the moment to be predicted by using the historical meteorological data of the small reservoir includes:
collecting historical meteorological data of the small reservoir to establish a historical meteorological data set of the small reservoir;
acquiring historical reservoir water level data corresponding to each meteorological data in the historical meteorological data set;
establishing a reservoir water level prediction model by using the historical meteorological data and the historical reservoir water level data;
and inputting the meteorological data at the moment to be predicted into a reservoir water level prediction model, and outputting the reservoir water level data at the moment to be predicted.
Further, the establishing of the reservoir water level prediction model by using the historical meteorological data and the historical reservoir water level data set comprises:
constructing a sample set by using the historical meteorological data set and historical reservoir water level data corresponding to meteorological data in the historical meteorological data set;
dividing the sample set into a training set and a testing set according to a preset proportion;
training the structure of the reservoir water level prediction model by utilizing a training set based on a neural network learning algorithm;
verifying by using the trained structure of the reservoir water level prediction model based on the test set to determine the reservoir water level prediction model;
wherein the preset proportion is that training data accounts for 80% to 90%, and test data accounts for 20% to 10% correspondingly.
Further, the training the structure of the reservoir water level prediction model by using a training set based on a neural network learning algorithm includes:
calculating the number of nodes of an input layer of a neural network learning algorithm by using the data type number of the meteorological data;
calculating the number of hidden layer nodes of the neural network learning algorithm by using the number of input layer nodes;
determining a neural network learning algorithm structure by using the number of nodes of the input layer and the number of nodes of the hidden layer;
and training the structure of the reservoir water level prediction model by utilizing a training set based on a neural network learning algorithm structure.
Further, the calculation formula for calculating the number of nodes of the input layer of the neural network learning algorithm by using the number of the data types of the meteorological data is as follows:
wherein m is the number of nodes of an input layer of the neural network learning algorithm, and n is the number of data types of meteorological data.
Further, calculating the number of hidden layer nodes of the neural network learning algorithm by using the number of input layer nodes comprises:
wherein p is the number of hidden layer nodes of the neural network learning algorithm, m is the number of input layer nodes of the neural network learning algorithm, and a is a constant.
Preferably, the flood control fine prediction result of the small reservoir by using the most similar historical reservoir water level data at the same time as the reservoir water level data at the time to be predicted comprises the following steps:
acquiring reservoir water level data of adjacent moments before and after the reservoir water level data of the moment to be predicted as first reservoir water level data and second reservoir water level data;
acquiring reservoir water level data of a moment to be predicted and reservoir water level data of a moment before and after the adjacent moment of the historical reservoir water level data of the same moment as the historical first reservoir water level data set and the historical second reservoir water level data set;
acquiring most approximate historical reservoir water level data by utilizing the historical first reservoir water level data set and the historical second reservoir water level data set;
and using the most approximate historical reservoir water level data as a flood control fine prediction result of the small reservoir.
Further, the obtaining reservoir water level data of adjacent previous and subsequent moments of the reservoir water level data of the moment to be predicted as the first reservoir water level data and the second reservoir water level data includes:
and inputting meteorological data of adjacent previous and next moments of the time to be predicted into a reservoir water level prediction model, and respectively outputting reservoir water level data of adjacent previous and next moments of the time to be predicted as first reservoir water level data and second reservoir water level data.
Further, the obtaining the most similar historical reservoir water level data by using the historical first reservoir water level data set and the historical second reservoir water level data set includes:
acquiring first similarity between each historical first reservoir water level data in the historical first reservoir water level data set and the first reservoir water level data;
acquiring second similarity of each historical second reservoir water level data in the historical second reservoir water level data set and the second reservoir water level data;
acquiring historical first reservoir water level data and historical second reservoir water level data which correspond to each other and correspond to the maximum value of the sum of the first similarity and the second similarity;
and acquiring corresponding historical reservoir water level data as the most approximate historical reservoir water level data by utilizing the historical first reservoir water level data and the historical second reservoir water level data.
Compared with the closest prior art, the invention has the following beneficial effects:
the method comprises the steps of pre-judging weather conditions at future moments based on historical weather data, obtaining a preliminary water level prediction result, when water level early warning exceeds a standard, calculating the similarity of previous and next moments to ensure that the selected and compared historical moments are most similar to moments to be predicted, and forecasting flood control information of reservoirs at the future moments according to the historical weather information of the positions of the reservoirs due to the fact that the water storage capacity of small-sized reservoirs is small and the influence of weather condition changes is large.
Drawings
Fig. 1 is a flow chart of a flood control fine prediction method for a small reservoir provided by the invention.
Detailed Description
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Example 1:
the invention provides a flood control refined prediction method for a small reservoir, which comprises the following steps as shown in figure 1:
s1, obtaining reservoir water level data at a moment to be predicted by using historical meteorological data of a small reservoir;
and S2, obtaining a flood control fine prediction result of the small reservoir by using the reservoir water level data at the moment to be predicted.
In this embodiment, a flood control fine prediction method for a small reservoir, where meteorological data is observable data of a ground monitoring station of the small reservoir, includes: wind speed, wind direction, air temperature, cloud cover, cloud base height, ground air pressure, relative humidity, precipitation and precipitation type.
S1 specifically comprises the following steps:
s1-1, collecting historical meteorological data of the small reservoir to establish a historical meteorological data set of the small reservoir;
s1-2, acquiring historical reservoir water level data corresponding to each meteorological data in the historical meteorological data set;
s1-3, establishing a reservoir water level prediction model by using the historical meteorological data and the historical reservoir water level data;
s1-4, inputting meteorological data at the moment to be predicted into a reservoir water level prediction model, and outputting reservoir water level data at the moment to be predicted.
S1-3 specifically comprises:
s1-3-1, constructing a sample set by using the historical meteorological data set and historical reservoir water level data corresponding to each meteorological data in the historical meteorological data set;
s1-3-2, dividing the sample set into a training set and a testing set according to a preset proportion;
s1-3-3, training the structure of the reservoir water level prediction model by utilizing a training set based on a neural network learning algorithm;
s1-3-4, verifying by using the trained structure of the reservoir water level prediction model based on a test set, and determining the reservoir water level prediction model;
wherein, the preset proportion is that the training data accounts for 80% to 90%, and the test data accounts for 20% to 10% correspondingly.
S1-3-3 specifically includes:
s1-3-3-1, calculating the number of nodes of an input layer of a neural network learning algorithm by using the data type number of meteorological data;
s1-3-3-2, calculating the number of hidden layer nodes of the neural network learning algorithm by using the number of input layer nodes;
s1-3-3-3, determining a neural network learning algorithm structure by using the input layer node number and the hidden layer node number;
s1-3-3-4, training the structure of the reservoir water level prediction model by utilizing a training set based on a neural network learning algorithm structure.
The formula for S1-3-3-1 is calculated as follows:
wherein m is the number of nodes of an input layer of the neural network learning algorithm, and n is the number of data types of meteorological data.
The calculation formula of S1-3-3-2 is as follows:
wherein p is the number of hidden layer nodes of the neural network learning algorithm, m is the number of input layer nodes of the neural network learning algorithm, and a is a constant.
S2 specifically comprises the following steps:
s2-1, judging whether the water level data of the reservoir at the moment to be predicted is larger than a flood control water level standard, if so, using the historical water level data which is most similar to the water level data of the reservoir at the moment to be predicted at the same time as the flood control fine prediction result of the small reservoir, otherwise, using the water level data of the reservoir at the moment to be predicted as the flood control fine prediction result of the small reservoir.
S2-1 specifically comprises:
s2-1-1, acquiring reservoir water level data of adjacent previous and subsequent moments of the reservoir water level data of the moment to be predicted as a first reservoir water level data and a second reservoir water level data;
s2-1-2, acquiring reservoir water level data of a reservoir at a time to be predicted and water level data of a historical reservoir at a time before and after the adjacent time of the historical reservoir water level data at the same time as a historical first reservoir water level data set and a historical second reservoir water level data set;
s2-1-3, obtaining most approximate historical reservoir water level data by utilizing the historical first reservoir water level data set and the historical second reservoir water level data set;
and S2-1-4, using the most approximate historical reservoir water level data as a flood control fine prediction result of the small reservoir.
S2-1-1 specifically comprises:
s2-1-1-1, inputting meteorological data of adjacent moments of the time to be predicted into a reservoir water level prediction model, and respectively outputting reservoir water level data of adjacent moments of the time to be predicted as a first reservoir water level data and a second reservoir water level data.
S2-1-3 specifically comprises:
s2-1-3-1, acquiring first similarity of each historical first reservoir water level data in the historical first reservoir water level data set and the first reservoir water level data;
s2-1-3-2, obtaining second similarity of each historical second reservoir water level data in the historical second reservoir water level data set and the second reservoir water level data;
s2-1-3-3, obtaining historical first reservoir water level data and historical second reservoir water level data which correspond to the maximum value of the sum of the first similarity and the second similarity and correspond to each other;
and S2-1-3-4, obtaining corresponding historical reservoir water level data as the most approximate historical reservoir water level data by utilizing the historical first reservoir water level data and the historical second reservoir water level data.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: although the present invention has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (9)
1. A flood control fine prediction method for a small reservoir is characterized by comprising the following steps:
s1, obtaining reservoir water level data at a moment to be predicted by using historical meteorological data of a small reservoir;
s2, obtaining a flood control refined prediction result of the small reservoir by using the reservoir water level data at the moment to be predicted;
s2-1, judging whether the water level data of the reservoir at the moment to be predicted is larger than a flood control water level standard, if so, using the historical water level data which is most similar to the water level data of the reservoir at the moment to be predicted at the same time as the flood control fine prediction result of the small reservoir, otherwise, using the water level data of the reservoir at the moment to be predicted as the flood control fine prediction result of the small reservoir.
2. The flood control fine prediction method for the small reservoir according to claim 1, wherein the step of obtaining the reservoir water level data of the moment to be predicted by using the historical meteorological data of the small reservoir comprises the following steps:
collecting historical meteorological data of the small reservoir to establish a historical meteorological data set of the small reservoir;
acquiring historical reservoir water level data corresponding to each meteorological data in the historical meteorological data set;
establishing a reservoir water level prediction model by using the historical meteorological data and the historical reservoir water level data;
and inputting the meteorological data at the moment to be predicted into a reservoir water level prediction model, and outputting the reservoir water level data at the moment to be predicted.
3. The flood control fine prediction method for the small reservoir according to claim 2, wherein the step of establishing a reservoir water level prediction model by using the historical meteorological data and the historical reservoir water level data set comprises the following steps:
constructing a sample set by using the historical meteorological data set and historical reservoir water level data corresponding to meteorological data in the historical meteorological data set;
dividing the sample set into a training set and a test set according to a preset proportion;
training the structure of the reservoir water level prediction model by utilizing a training set based on a neural network learning algorithm;
verifying by using the trained structure of the reservoir water level prediction model based on the test set to determine the reservoir water level prediction model;
wherein, the preset proportion is that the training data accounts for 80% to 90%, and the test data accounts for 20% to 10% correspondingly.
4. The flood control fine prediction method for small-sized reservoirs according to claim 3, wherein the training of the structure of the reservoir water level prediction model by using the training set based on the neural network learning algorithm comprises:
calculating the number of nodes of an input layer of a neural network learning algorithm by using the data type number of the meteorological data;
calculating the number of hidden layer nodes of the neural network learning algorithm by using the number of input layer nodes;
determining a neural network learning algorithm structure by using the number of nodes of the input layer and the number of nodes of the hidden layer;
and training the structure of the reservoir water level prediction model by utilizing a training set based on a neural network learning algorithm structure.
5. The flood control fine prediction method for small reservoirs according to claim 4, wherein the calculation formula for calculating the number of nodes of the input layer of the neural network learning algorithm by using the data type number of the meteorological data is as follows:
wherein m is the number of nodes of an input layer of the neural network learning algorithm, and n is the number of data types of meteorological data.
6. The flood control refined prediction method for the small reservoir as claimed in claim 4, wherein the calculation formula for calculating the number of hidden layer nodes of the neural network learning algorithm by using the number of input layer nodes is as follows:
wherein p is the number of hidden layer nodes of the neural network learning algorithm, m is the number of input layer nodes of the neural network learning algorithm, and a is a constant.
7. The flood control fine prediction method for the small reservoir according to claim 1, wherein the step of using the historical reservoir water level data which is most similar to the reservoir water level data at the moment to be predicted as the flood control fine prediction result for the small reservoir comprises the following steps:
acquiring reservoir water level data of adjacent moments before and after the reservoir water level data of the moment to be predicted as first reservoir water level data and second reservoir water level data;
acquiring reservoir water level data of a moment to be predicted and reservoir water level data of a moment before and after the adjacent moment of the historical reservoir water level data of the same moment as the historical first reservoir water level data set and the historical second reservoir water level data set;
acquiring most similar historical reservoir water level data by utilizing the historical first reservoir water level data set and the historical second reservoir water level data set;
and using the most approximate historical reservoir water level data as a flood control fine prediction result of the small reservoir.
8. The flood control fine prediction method for the small reservoir according to claim 7, wherein the step of obtaining reservoir water level data at adjacent previous and subsequent moments of the reservoir water level data at the moment to be predicted as the first reservoir water level data and the second reservoir water level data comprises:
and inputting meteorological data of adjacent previous and next moments of the time to be predicted into a reservoir water level prediction model, and respectively outputting reservoir water level data of adjacent previous and next moments of the time to be predicted as first reservoir water level data and second reservoir water level data.
9. The flood control fine prediction method for the small reservoir according to claim 8, wherein the obtaining the most approximate historical reservoir water level data by using the historical first reservoir water level data set and the historical second reservoir water level data set comprises:
acquiring first similarity between each historical first reservoir water level data in the historical first reservoir water level data set and the first reservoir water level data;
acquiring second similarity of each historical second reservoir water level data in the historical second reservoir water level data set and the second reservoir water level data;
acquiring historical first reservoir water level data and historical second reservoir water level data which correspond to each other and correspond to the maximum value of the sum of the first similarity and the second similarity;
and acquiring corresponding historical reservoir water level data as the most approximate historical reservoir water level data by utilizing the historical first reservoir water level data and the historical second reservoir water level data.
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