CN107578093A - The Elman neural network dynamic Forecasting Methodologies of Landslide Deformation - Google Patents
The Elman neural network dynamic Forecasting Methodologies of Landslide Deformation Download PDFInfo
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Abstract
The invention discloses the Elman neural network dynamic Forecasting Methodologies of Landslide Deformation, it is related to Landslide Prediction technical field, the dynamic characteristic of Elman neutral nets is applied in Landslide Deformation dynamic prediction, establish the Landslide Deformation dynamic prediction model based on Elman neutral nets, and the model is applied in the acceleration Deformation Prediction example of middle area's Slip moinitoring point, prediction result shows that Transfiguration Prediction Result and measured value trend are basically identical, prediction result precision is high, and the Landslide Deformation forecast model based on Elman neutral nets has very strong dynamic characteristic and adaptive ability, dynamic prediction can be carried out to Landslide Deformation according to landslide real-time deformation state, and prediction result accuracy rate is high.
Description
Technical Field
The invention relates to the technical field of landslide prediction, in particular to an Elman neural network dynamic prediction method for landslide deformation.
Background
At present, a plurality of deformation prediction methods are used in landslide, including a grey (G, M) prediction model, chaotic time sequence prediction, a static feedforward neural network and the like. The condition for establishing the gray prediction model is that original discrete data is a smooth discrete function, otherwise, a large error is caused; the traditional chaotic linear prediction model has limitations inside, and the chaotic prediction model based on the weighted index method has a complex modeling process and is complex to calculate; when the feedforward neural network including the BP performs landslide deformation prediction, the dynamic system is actually identified by a static model, and the prediction is unreasonable by adopting the mode.
Disclosure of Invention
The embodiment of the invention provides an Elman neural network dynamic prediction method for landslide deformation, which can solve the problems in the prior art.
An Elman neural network dynamic prediction method for landslide deformation, comprising the following steps:
an Elman neural network is established according to the following formulas (1), (2) and (3):
x(k)＝f(w 1 x c (k)+w 2 (u(k-1))) (1)
x c (k)＝αx c (k-1)+x(k-1) (2)
y(k)＝g(w 3 x(k)) (3)
where k denotes the time of day, the connection weight w 1 For the connection weight matrix of the bearer layer and the hidden layer, w 2 For the connection weight matrix of the input layer and the hidden layer, w 3 A connection weight matrix, x, for the hidden layer and the output layer c (k) And x (k) represents the output of the underlying layer and the output of the hidden layer, respectively, y (k) represents the output of the output layer, and 0 ≦ α&1 is a self-connection feedback gain factor, f (x) is a transfer function of a hidden layer, and g (x) is a function of an output layer;
setting the number of nodes of an input layer, an output layer and a hidden layer, selecting a hidden layer transfer function and an output layer transfer function, and setting a training error and a step number;
inputting landslide deformation history monitoring data as a data sample, and training the Elman neural network;
and inputting the current actual monitoring data into the trained Elman neural network, and predicting to obtain a predicted value at the next moment.
Preferably, the number of nodes of the input layer is set to be 3, the number of nodes of the output layer is set to be 1, for the number of nodes of the hidden layer, the intersection of the number of nodes of the hidden layer and the number of nodes of the hidden layer is determined according to the following two empirical formulas, and then the optimum number M of nodes of the hidden layer is determined according to trial and error:
in the formula, M and n respectively represent the number of nodes of an input layer and an output layer, a belongs to an integer between [0 and 10], the value range of the number M of the nodes of the hidden layer is obtained according to the formulas (4) and (5), and the highest precision is obtained when the M is 12 through trial and error calculation.
Preferably, the transfer function of the hidden layer adopts tansig nonlinear function, the transfer function of the output layer adopts purelin linear function, and the network training error is set to be 10 -4 Maximum number of training stepsThe process is 1000 steps.
Preferably, before inputting the actual monitoring data, the input data is firstly normalized, and the input value of the monitoring data is normalized between [0,1 ]:
wherein, U is the neural network input value after normalization processing, U is the actual monitoring data, U is the neural network input value after normalization processing max For the maximum value of the monitoring data, 1.25 times of the maximum value of the actual monitoring data is taken, u min Taking 0.75 times of the minimum value of the actual monitoring data as the minimum value of the monitoring data;
after the predicted value at the next moment is obtained, the predicted value is converted into an actual value by using the following formula:
Y＝y min +y(y max -y min ) (7)
wherein Y is the neural network output value after the inverse normalization processing, Y is the actual output value of the neural network, and Y is the actual output value of the neural network max Is the maximum value of the actual output value, y min Is the minimum value of the actual output value.
According to the method for dynamically predicting the landslide deformation through the Elman neural network, dynamic characteristics of the Elman neural network are applied to dynamic prediction of the landslide deformation, a landslide deformation dynamic prediction model based on the Elman neural network is established, the model is applied to an acceleration deformation prediction example of a middle region landslide deformation monitoring point, the prediction result shows that the deformation prediction result is basically consistent with the trend of an actually measured value, the prediction result is high in precision, the model based on the Elman neural network has strong dynamic characteristics and self-adaption capability, dynamic prediction can be performed on the landslide deformation according to the real-time deformation state of the landslide, and the prediction result is high in accuracy.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of the structure of an Elman neural network;
fig. 2 is a flow chart of an Elman neural network dynamic prediction method for landslide deformation according to an embodiment of the present invention;
FIG. 3 is a comparison graph of the actual measurement curve and the predicted curve of the horizontal accumulated displacement of the J24 monitoring point;
FIG. 4 is a graph comparing the measured and predicted horizontal cumulative displacement curves for the J29 monitor point.
Detailed Description
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 only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Before introducing the technical scheme of the invention, the Elman neural network is firstly explained:
as shown in fig. 1, the Elman neural network has a four-layer structure: the network comprises an input layer, a hidden layer, a receiving layer and an output layer, wherein the input layer is used for transmitting signals, the hidden layer can adopt a linear or nonlinear transfer function, the output layer plays a linear weighting role, and the receiving layer can memorize the output of the hidden layer at the previous moment to the input of the network and return the output to the network.
In the figure:
x(k)＝f(w 1 x c (k)+w 2 (u(k-1))) (1)
x c (k)＝αx c (k-1)+x(k-1) (2)
y(k)＝g(w 3 x(k)) (3)
where k denotes the time of day, the connection weight w 1 As a connection weight matrix of the bearer layer and the hidden layer, w 2 For the connection weight matrix of the input layer and the hidden layer, w 3 A connection weight matrix, x, for the hidden layer and the output layer c (k) And x (k) represents the output of the underlying layer and the output of the hidden layer, respectively, y (k) represents the output of the output layer, and 0 ≦ α&And lt, 1 is a self-connection feedback gain factor, f (x) is a transfer function of the hidden layer, and g (x) is a function of the output layer.
Referring to fig. 2, the method for dynamically predicting the Elman neural network of landslide deformation provided in the embodiment of the present invention includes the following steps:
step 100, establishing an Elman neural network according to the formulas (1), (2) and (3);
step 110, setting the number of nodes of an input layer, an output layer and a hidden layer, selecting a hidden layer transfer function and an output layer transfer function, and setting a training error and a step number;
specifically, in this embodiment, the number of nodes of the input layer is set to be 3, the number of nodes of the output layer is set to be 1, for the number of nodes of the hidden layer, the intersection of the number of nodes of the hidden layer and the number of nodes of the hidden layer is determined according to the following two empirical formulas, and then the optimal number M of nodes of the hidden layer is determined according to trial and error:
wherein M and n respectively represent the number of nodes of an input layer and an output layer, a belongs to an integer between [0 and 10], the number M of nodes of the hidden layer is found to be 2-12, M-12 according to the formulas (4) and (5), and the Elman network is used for trial calculation of M to find that the calculation precision is the highest when M is 12, so the number M of the nodes of the hidden layer is set to be 12.
The transfer function of the hidden layer adopts tansig nonlinear function, and the transfer function of the output layer adopts purelin linear function. Network trainingError is set to 10 -4 The maximum number of training steps is 1000 steps.
Step 120, inputting landslide deformation history monitoring data as a data sample, and training the Elman neural network;
in training, assuming that the initial number of data samples is n, the training process is as shown in table 1. And then predicting the (n + 1) th monitoring data by using the trained network, replacing the predicted value with the (n + 1) th measured value, supplementing the predicted value into a data sample, and continuing training to predict the (n + 2) th monitoring data. By analogy, the prediction effect of the Elman neural network prediction model is more and more reasonable along with the increase of the landslide deformation monitoring sample data.
TABLE 1 training Process for network models
And step 130, inputting the current actual monitoring data into the trained Elman neural network, and predicting to obtain a predicted value at the next moment.
In this embodiment, before inputting the actual monitoring data, the input data is first normalized, and the input value of the monitoring data is normalized to be between [0,1 ]:
wherein, U is the neural network input value after normalization processing, U is the actual monitoring data, U is the neural network input value after normalization processing max For the maximum value of the monitoring data, 1.25 times of the maximum value of the actual monitoring data is generally taken as u min For the minimum value of the monitored data, it is generally 0.75 times the minimum value of the actual monitored data.
After the predicted value at the next moment is obtained, the predicted value is converted into an actual value by using the following formula:
Y＝y min +y(y max -y min ) (7)
wherein Y is inverse normalizationThe processed output value of the neural network, y is the actual output value of the neural network, y max Is the maximum value of the actual output value, y min Is the minimum value of the actual output value.
The following is a dynamic prediction of the monitored data of the accelerated deformation phases of the two monitoring points J24 and J29. Selecting 14 groups of historical monitoring data between 2016, 10 months and 5 days to 2016, 11 months and 14 days as training samples, and predicting landslide deformation after 2016, 11 months and 14 days as shown in table 2; the measured values, predicted values, prediction errors, and prediction average errors of the landslide deformations of J24 and J29 are shown in tables 3 and 4, respectively. The landslide deformation prediction curve and the measured curve are shown in fig. 3 and 4, respectively. Meanwhile, a landslide acceleration deformation curve and a curve equation are obtained based on the 14 sets of historical monitoring data through fitting, and the landslide acceleration deformation curve and the curve equation are respectively shown in the figure 3 and the figure 4.
Table 2 14 sets of training samples
TABLE 3J24 comparison table of horizontal accumulative displacement measured value and predicted value
TABLE 4J29 comparison table of horizontal accumulative displacement measured value and predicted value
From table 3, the maximum error between the predicted value and the measured value of the landslide deformation accumulated displacement of the J24 monitoring point is 8.00 percent, the minimum error is 0.14 percent, and the average error is 1.72 percent; as can be seen from fig. 3, the landslide deformation accumulated displacement prediction curve and the actually measured displacement curve have high goodness of fit and basically consistent trend; the Boltzmann fitted curve in the figure represents the deformation trend line of the accelerated deformation stage of the landslide if the deformation stage is not treated.
From table 4, the maximum error between the predicted value and the measured value of the landslide deformation accumulated displacement of the J29 monitoring point is 5.84%, the minimum error is 0.01%, and the average error is 1.07%; as can be seen from fig. 4, the landslide deformation accumulated displacement prediction curve and the actually measured displacement curve have high goodness of fit and basically consistent trend; a quadratic polynomial fitting curve in the graph represents a deformation trend line of the landslide acceleration deformation stage if the landslide acceleration deformation stage is not treated.
In summary, the following results can be obtained: the Elman neural network landslide deformation prediction model can accurately predict landslide deformation. Similarly, the landslide prediction model can be used for deformation prediction of other monitoring points.
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 invention. 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.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (4)
1. The method for dynamically predicting the landslide deformation by using the Elman neural network is characterized by comprising the following steps of:
the Elman neural network is established according to the following formulas (1), (2) and (3):
x(k)＝f(w 1 x c (k)+w 2 (u(k-1))) (1)
x c (k)＝αx c (k-1)+x(k-1) (2)
y(k)＝g(w 3 x(k)) (3)
where k denotes the time of day, the connection weight w 1 As a connection weight matrix of the bearer layer and the hidden layer, w 2 For the connection weight matrix of the input layer and the hidden layer, w 3 For the connection of a hidden layer to an output layerAccess matrix, x c (k) And x (k) represents the output of the underlying layer and the output of the hidden layer, respectively, y (k) represents the output of the output layer, and 0 ≦ α&1 is a self-connection feedback gain factor, f (x) is a transfer function of a hidden layer, and g (x) is a function of an output layer;
setting the number of nodes of an input layer, an output layer and a hidden layer, selecting a hidden layer transfer function and an output layer transfer function, and setting a training error and a step number;
inputting landslide deformation history monitoring data as a data sample, and training the Elman neural network;
and inputting the current actual monitoring data into the trained Elman neural network, and predicting to obtain a predicted value at the next moment.
2. The method for dynamically predicting an Elman neural network of a landslide deformation according to claim 1, wherein the number of nodes of the input layer is set to be 3, the number of nodes of the output layer is set to be 1, for the number of nodes of the hidden layer, the intersection of the number of nodes of the hidden layer and the number of nodes of the hidden layer is determined according to the following two empirical formulas, and then the optimal number M of nodes of the hidden layer is determined according to trial and error:
in the formula, M and n respectively represent the number of nodes of an input layer and an output layer, a belongs to an integer between [0 and 10], the value range of the number M of the nodes of the hidden layer is obtained according to the formulas (4) and (5), and the highest precision is obtained when M is 12.
3. The method for dynamically predicting the Elman neural network of landslide deformation according to claim 1, wherein the transfer function of the hidden layer adopts a tansig nonlinear function, the transfer function of the output layer adopts a purelin linear function, and the network training error is set to 10 -4 The maximum number of training steps isAnd (1000) steps.
4. The method for dynamically predicting the landslide deformation Elman neural network according to claim 1, wherein before inputting the actual monitoring data, the input data is firstly normalized, and the input value of the monitoring data is normalized to be between [0,1 ]:
wherein U is the neural network input value after normalization processing, U is the actual monitoring data, and U is max For the maximum value of the monitoring data, 1.25 times of the maximum value of the actual monitoring data is taken, u min Taking 0.75 times of the minimum value of the actual monitoring data as the minimum value of the monitoring data;
after the predicted value at the next moment is obtained, the predicted value is converted into an actual value by using the following formula:
Y＝y min +y(y max -y min ) (7)
wherein Y is the neural network output value after the inverse normalization processing, Y is the actual neural network output value, Y max Is the maximum value of the actual output value, y min Is the minimum value of the actual output value.
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Application publication date: 20180112 |