CN110991776A - Method and system for realizing water level prediction based on GRU network - Google Patents
Method and system for realizing water level prediction based on GRU network Download PDFInfo
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Abstract
The invention discloses a system for realizing water level prediction based on a GRU network, which comprises: the system comprises a construction module, a training module and a verification module; the building module is used for building a GRU network model based on GRU; the training module is used for acquiring water level and rainfall information and inputting the characteristic vectors corresponding to the water level and rainfall information into the GRU network to realize the training of the GRU network model; and the verification module is used for inputting the test data into the trained GRU network model and predicting the height of the water level through the trained GRU network model. The invention can accurately predict the height of the water outlet level, achieve early warning, and take corresponding measures in advance to ensure that the water level of the water area is at the normal height, thereby ensuring the life safety and property safety of people.
Description
Technical Field
The invention relates to the technical field of water level prediction, in particular to a method and a system for realizing water level prediction based on a GRU network.
Background
With the rapid development of economy in China, the continuous progress of science and technology, particularly the rise of the Internet, a lot of work becomes simple, convenient and informationized. In the years, the flood season of China is more and more long, the flood prevention work of China is daily severe when the flood prevention work of China enters a rainy season, and therefore how to improve the information of the flood prevention work of China and quickly and accurately give an early warning becomes the most critical and urgent current flood prevention situation, and the prediction and early warning should be timely realized through an effective analysis method.
There are many methods for predicting water levels, but they all have some drawbacks. For example, the core task of the traditional water level prediction is to construct a water level prediction model, describe and describe the water level process through different theoretical methods, hierarchical structures and modeling modes, express the water level process in a form of a mathematical language or a physical model, and finally apply the water level prediction model to prediction calculation and water level simulation analysis. For example, the models of the new anjiang river have a certain application range and can be mastered only by some professionals, so that the adaptability is not strong. Therefore, in order to solve the above technical problems, the present invention provides a method and a system for implementing water level prediction based on a GRU network to solve the existing technical problems.
Disclosure of Invention
The invention aims to provide a method and a system for realizing water level prediction based on a GRU network, aiming at the defects of the prior art, which can accurately predict the height of the water level, achieve early warning, take response measures in advance, enable the water level of a water area to be at a normal height, and guarantee the life safety and property safety of people.
In order to achieve the purpose, the invention adopts the following technical scheme:
a system for realizing water level prediction based on a GRU network comprises: the system comprises a construction module, a training module and a verification module;
the building module is used for building a GRU network model based on GRU;
the training module is used for acquiring water level and rainfall information and inputting the characteristic vectors corresponding to the water level and rainfall information into the GRU network to realize the training of the GRU network model;
and the verification module is used for inputting the test data into the trained GRU network model and predicting the height of the water level through the trained GRU network model.
Further, the training module comprises an acquisition module, a conversion module, a first prediction module, a comparison module and a processing module;
the acquisition module is used for acquiring water level and rainfall information and carrying out characteristic statistics on the acquired water level and rainfall information;
the conversion module is used for converting the counted characteristics of the water level and rainfall information into characteristic vectors;
the first prediction module is used for taking the characteristic vector as an input vector of a GRU network, and the GRU network outputs the height of the predicted water level according to the input characteristic vector;
the comparison module is used for comparing the output predicted water level height with the real water level height;
and the processing module is used for obtaining parameters required in the GRU network model by adopting forward and reverse iterative transfer according to the obtained comparison result.
Furthermore, the acquisition module acquires characteristics of water level and rainfall information of each hour in a day; the characteristics comprise rainfall at the current time, water level at the current time, the increment and decrement of the rainfall amount of 6 hours before and after a half day, water level difference between the current time and 6 hours before, the increment and decrement of the rainfall amount of 12 hours before and after a day, and water level difference between the current time and 12 hours before.
The system further comprises a second prediction module for receiving the monitored data, performing water level prediction on the received data, and storing and analyzing the predicted water level to obtain an analysis result.
Further, the system also comprises a display module used for displaying the obtained analysis result.
Correspondingly, a method for realizing water level prediction based on a GRU network is also provided, which comprises the following steps:
s1, constructing a GRU network model based on GRUs;
s2, collecting water level and rainfall information, and inputting a feature vector corresponding to the water level and rainfall information into the GRU network to realize training of a GRU network model;
and S3, inputting the test data into the trained GRU network model, and predicting the height of the water level through the trained GRU network model.
Further, the step S2 includes:
s21, collecting water level and rainfall information, and carrying out characteristic statistics on the collected water level and rainfall information;
s22, converting the counted characteristics of the water level and rainfall information into characteristic vectors;
s23, taking the characteristic vector as an input vector of a GRU network, and outputting the height of the predicted water level by the GRU network according to the input characteristic vector;
s24, comparing the output predicted water level height with the real water level height;
and S25, adopting forward and reverse iterative transfer according to the obtained comparison result to obtain the parameters required in the GRU network model.
Further, the step S21 is to collect characteristics of water level and rainfall information per hour in a day; the characteristics comprise rainfall at the current time, water level at the current time, the increment and decrement of the rainfall amount of 6 hours before and after a half day, water level difference between the current time and 6 hours before, the increment and decrement of the rainfall amount of 12 hours before and after a day, and water level difference between the current time and 12 hours before.
Further, the method also comprises the following steps:
and S4, receiving the monitored data, predicting the water level of the received data, storing and analyzing the predicted water level, and obtaining an analysis result.
Further, the method also comprises the following steps:
and S5, displaying the obtained analysis result.
Compared with the prior art, the method aims to collect rainfall, water level and other information of each hour of the whole day, construct characteristics such as water level and rainfall total increase of 6 hours before and after half a day and 12 hours before and after one day, and analyze the characteristics, so that the water level height of each hour is accurately predicted, early warning is achieved, response measures are taken in advance, the water level of a water area is enabled to be at a normal height, and life safety and property safety of people are guaranteed.
Drawings
Fig. 1 is a system structure diagram for implementing water level prediction based on a GRU network according to an embodiment;
FIG. 2 is a diagram of a GRU model provided in the first embodiment;
FIG. 3 is a schematic diagram of an embodiment providing prediction of monitored data.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
The invention aims to provide a method and a system for realizing water level prediction based on a GRU network aiming at the defects of the prior art.
Example one
The embodiment provides a system for realizing water level prediction based on a GRU network, as shown in fig. 1, including: the system comprises a construction module 11, a training module 12 and a verification module 13;
the building module 11 is configured to build a GRU network model based on a GRU;
the training module 12 is configured to acquire water level and rainfall information, and input a feature vector corresponding to the water level and rainfall information into the GRU network to implement training of a GRU network model;
and the verification module 13 is configured to input the test data into the trained GRU network model, and predict the height of the water level through the trained GRU network model.
In the building block 11, a GRU network model based on GRUs is built.
In this embodiment, a GRU module of the tensrflow is used as a framework to form a GRU network model.
Among them, the tensrflow is a symbolic mathematical system based on data flow programming (dataflow programming), and is widely applied to programming implementation of various machine learning (machine learning) algorithms.
The GRU algorithm belongs to a recurrent neural network, and the chain structure of the GRU algorithm is suitable for processing various timing problems. There are two gates in the GRU model: respectively an update gate and a reset gate. The specific structure diagram is shown in FIG. 2: in the figurez t Andr t respectively representing an update gate and a reset gate. The update gate is used to control the extent to which the state information at the previous time is brought into the current state, and a larger value of the update gate indicates that more state information at the previous time is brought in. How much information is written to the current candidate set before reset gate controls the previous state
The smaller the reset gate, the less information of the previous state is written.
In the training module 12, water level and rainfall information are collected, and feature vectors corresponding to the water level and rainfall information are input into the GRU network to realize training of the GRU network model.
Specifically, the training module is used for carrying out characteristic statistics on collected water level and rainfall information per hour in one day; converting the statistical features per hour into 24 x 6-dimensional feature vectors; and taking the feature vector as the input of the GRU network, outputting the predicted water level height per hour, comparing the predicted water level height per hour with the real water level height per hour, and predicting to obtain proper weight under the condition of error allowance in the process of multiple forward and backward iterations to complete model training.
The training module 12 comprises an acquisition module, a conversion module, a first prediction module, a comparison module and a processing module;
the acquisition module is used for acquiring water level and rainfall information and carrying out characteristic statistics on the acquired water level and rainfall information;
in the embodiment, the characteristics of the water level and rainfall information of each hour in a day are collected in the collection module; the characteristics comprise 6 characteristics of rainfall at the current time, water level at the current time, increment and decrement of rainfall amount in 6 hours before and after half a day, water level difference between the current time and 6 hours before, increment and decrement of rainfall amount in 12 hours before and after one day, and water level difference between the current time and 12 hours before, and the total time is 24 hours.
Wherein the characteristic sampling time interval is one hour,L t indicating the water level (in meters), D, at a certain point in time ttIndicating the amount of rainfall in millimeters in one hour,
，
respectively corresponding to the water level and the rainfall one hour before to obtain a training data set
Then the sampled data for each day is in the form of
Is a pair of timing features in the training data and their corresponding true values, wherein
，
. In this embodiment, 80% of the total data is selected as the training set of the model, and 20% of the total data is selected as the modelAnd (5) verifying the set.
The conversion module is used for converting the counted characteristics of the water level and rainfall information into characteristic vectors;
in this example, the hourly statistical features are converted into 24 x 6 dimensional feature vectors.
The method specifically comprises the steps of preprocessing the data of the statistical characteristics of each hour, and recording the input characteristics of a certain time point t as
For water level changes requiring prediction, wherein
，
，
(the total rainfall amount is increased and decreased in 6 hours before and after half a day),
=
，
，
=
,
。
in the first prediction module, the characteristic vector is used as an input vector of a GRU network, and the GRU network outputs the height of the predicted water level according to the input characteristic vector;
in the comparison module, the output predicted water level height is compared with the real water level height;
and the processing module is used for obtaining parameters required in the GRU network model by adopting forward and reverse iteration transmission according to the obtained comparison result.
In this embodiment, the forward and backward iterations specifically include:
forward propagation: suppose that at a certain time step t in the training, the activation value of the last time step is obtained
Input feature vector at t
Weight corresponding to input feature vector at t
Updating the weight parameter of the door
Resetting the weight parameter of the gate
Outputting corresponding weight parameter at t
Activation value at t
. Respectively passing the activation value at t-1 and the input at t through the weight
，
Connected to obtain reset gates respectively by sigmoid function
Indicating how much information was written to the current candidate set for the previous state; updating door
Indicating the degree to which state information for controlling the previous time is brought into the current state; passing the activation value at t-1 and the input at t by weight
Connected and reused
Calculating to obtain a candidate set represented as
(ii) a The activation value at the time of t is finally obtained and is expressed as
The output is expressed as
. Wherein
，
，[]Indicates that the two vectors are connected and indicates the product of the matrices.
Iteration and back propagation: learning the network by adopting a backward error propagation algorithm to obtain a loss function of the sample, and assuming that the obtained predicted value is
True value of
The loss of a single sample at a certain time is
The loss of a single sample at all times is
(ii) a After the partial derivatives for each parameter are calculated, the parameters can be updated, and the loss convergence is known by iteration in turn.
In the verification module 13, the test data is input into the trained GRU network model, and the height of the water level is predicted by the trained GRU network model.
And taking the selected 20% data as a data set for verifying the GRU network model.
The specific verification mode is that a verified data set is processed through a conversion module, a first prediction module, a comparison module and a processing module, and a predicted value is given to input feature data through a trained GRU network model.
In this embodiment, the method further includes:
and the second prediction module is used for receiving the monitored data, performing water level prediction on the received data, and storing and analyzing the predicted water level to obtain an analysis result.
And the display module is used for displaying the obtained analysis result.
As shown in fig. 3, the overall process of predicting the monitored data includes a communication security gateway, a receiving node, a distributing node, and a receiving program, where the communication security gateway is connected to a virtual machine cluster, a cloud cluster, and a physical machine cluster.
1. Collecting reported and monitored data such as water level, rainfall and the like through a communication security gateway according to a set time frequency; data can be monitored and collected in real time;
2. the data receiving node receives the data, decrypts and analyzes the data, predicts the water level by using a GRU network model through a prediction engine, stores the predicted water level data, and analyzes the predicted water level data to obtain an analysis result; the embodiment converts the acquired data, converts the data characteristics into a characteristic vector, takes the characteristic vector as an input vector of a GRU network model, predicts the height of the water level through the GRU network model, compares the characteristic vector with a preset threshold value, judges whether the water level height threshold value is reached or not, and sends out a warning if the water level height threshold value is reached; the embodiment can predict the water level condition in a certain hour, and the prediction range is more accurate;
3. the data distribution node can distribute conventional data and early warning data to a receiving program; the embodiment sends the predicted water level height to a display interface in real time;
4. the receiving program receives the conventional data and the alarm to display the page; this embodiment passes through the interface with data and shows, makes things convenient for personnel in time to know the change condition of water level, and the countermeasure is taken in advance to the early warning in advance.
Compared with the prior art, the embodiment aims to collect the rainfall, the water level and other information of each hour of the whole day, construct the characteristics of the water level and the total rainfall increase amount and the like of 6 hours before and after half a day and 12 hours before and after one day, accurately predict the water level height of each hour after the half day, achieve early warning, take response measures in advance, enable the water level of a water area to be at the normal height, and guarantee the life safety and property safety of people.
Example two
The embodiment provides a method for realizing water level prediction based on a GRU network, which comprises the following steps:
s1, constructing a GRU network model based on GRUs;
s2, collecting water level and rainfall information, and inputting a feature vector corresponding to the water level and rainfall information into the GRU network to realize training of a GRU network model;
and S3, inputting the test data into the trained GRU network model, and predicting the height of the water level through the trained GRU network model.
Further, the step S2 includes:
s21, collecting water level and rainfall information, and carrying out characteristic statistics on the collected water level and rainfall information;
s22, converting the counted characteristics of the water level and rainfall information into characteristic vectors;
s23, taking the characteristic vector as an input vector of a GRU network, and outputting the height of the predicted water level by the GRU network according to the input characteristic vector;
s24, comparing the output predicted water level height with the real water level height;
and S25, adopting forward and reverse iterative transfer according to the obtained comparison result to obtain the parameters required in the GRU network model.
Further, the step S21 is to collect characteristics of water level and rainfall information per hour in a day; the characteristics comprise rainfall at the current time, water level at the current time, the increment and decrement of the rainfall amount of 6 hours before and after a half day, water level difference between the current time and 6 hours before, the increment and decrement of the rainfall amount of 12 hours before and after a day, and water level difference between the current time and 12 hours before.
Further, the method also comprises the following steps:
and S4, receiving the monitored data, predicting the water level of the received data, storing and analyzing the predicted water level, and obtaining an analysis result.
Further, the method also comprises the following steps:
and S5, displaying the obtained analysis result.
It should be noted that, the method for implementing water level prediction based on the GRU network in this embodiment is similar to the embodiment, and will not be described herein again.
Compared with the prior art, the embodiment aims to collect the rainfall, the water level and other information of each hour of the whole day, construct the characteristics of the water level and the total rainfall increase amount and the like of 6 hours before and after half a day and 12 hours before and after one day, accurately predict the water level height of each hour after the half day, achieve early warning, take response measures in advance, enable the water level of a water area to be at the normal height, and guarantee the life safety and property safety of people.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A system for realizing water level prediction based on a GRU network is characterized by comprising: the system comprises a construction module, a training module and a verification module;
the building module is used for building a GRU network model based on GRU;
the training module is used for acquiring water level and rainfall information and inputting the characteristic vectors corresponding to the water level and rainfall information into the GRU network to realize the training of the GRU network model;
and the verification module is used for inputting the test data into the trained GRU network model and predicting the height of the water level through the trained GRU network model.
2. The system for realizing water level prediction based on GRU network of claim 1, wherein the training module comprises an acquisition module, a conversion module, a first prediction module, a comparison module and a processing module;
the acquisition module is used for acquiring water level and rainfall information and carrying out characteristic statistics on the acquired water level and rainfall information;
the conversion module is used for converting the counted characteristics of the water level and rainfall information into characteristic vectors;
the first prediction module is used for taking the characteristic vector as an input vector of a GRU network, and the GRU network outputs the height of the predicted water level according to the input characteristic vector;
the comparison module is used for comparing the output predicted water level height with the real water level height;
and the processing module is used for obtaining parameters required in the GRU network model by adopting forward and reverse iterative transfer according to the obtained comparison result.
3. The system for realizing water level prediction based on GRU network as claimed in claim 2, wherein the characteristics of water level and rainfall information per hour in a day are collected in the collection module; the characteristics comprise rainfall at the current time, water level at the current time, the increment and decrement of the rainfall amount of 6 hours before and after a half day, water level difference between the current time and 6 hours before, the increment and decrement of the rainfall amount of 12 hours before and after a day, and water level difference between the current time and 12 hours before.
4. The system of claim 1, further comprising a second prediction module configured to receive the monitored data, perform water level prediction on the received data, and store and analyze the predicted water level to obtain an analysis result.
5. The system of claim 4, further comprising a display module for displaying the obtained analysis result.
6. A method for realizing water level prediction based on a GRU network is characterized by comprising the following steps:
s1, constructing a GRU network model based on GRUs;
s2, collecting water level and rainfall information, and inputting a feature vector corresponding to the water level and rainfall information into the GRU network to realize training of a GRU network model;
and S3, inputting the test data into the trained GRU network model, and predicting the height of the water level through the trained GRU network model.
7. The method for realizing water level prediction based on GRU network as claimed in claim 6, wherein said step S2 includes:
s21, collecting water level and rainfall information, and carrying out characteristic statistics on the collected water level and rainfall information;
s22, converting the counted characteristics of the water level and rainfall information into characteristic vectors;
s23, taking the characteristic vector as an input vector of a GRU network, and outputting the height of the predicted water level by the GRU network according to the input characteristic vector;
s24, comparing the output predicted water level height with the real water level height;
and S25, adopting forward and reverse iterative transfer according to the obtained comparison result to obtain the parameters required in the GRU network model.
8. The method of claim 7, wherein the characteristics of the water level and rainfall information per hour in a day are collected in step S21; the characteristics comprise rainfall at the current time, water level at the current time, the increment and decrement of the rainfall amount of 6 hours before and after a half day, water level difference between the current time and 6 hours before, the increment and decrement of the rainfall amount of 12 hours before and after a day, and water level difference between the current time and 12 hours before.
9. The method of claim 6, further comprising the steps of:
and S4, receiving the monitored data, predicting the water level of the received data, storing and analyzing the predicted water level, and obtaining an analysis result.
10. The method of claim 9, further comprising the steps of:
and S5, displaying the obtained analysis result.
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Application publication date: 20200410 |