CN112348275A - Regional ecological environment change prediction method based on online incremental learning - Google Patents
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Abstract
The invention relates to the field of ecological environment prediction models, and discloses a regional ecological environment change prediction method based on online incremental learning. The invention combines a convolutional neural network, a long-short term memory neural network and a fully-connected neural network with an increment limit learning machine based on an adaptive genetic algorithm. A traditional incremental extreme learning machine is optimized, and the number of hidden layer optimal nodes of a model is obtained by using an adaptive genetic algorithm. And the change of the regional ecological environment is predicted by using an incremental learning method, the method can be used for carrying out on-line training on the model according to the change of new environment data, and the method can also be used for greatly reducing the cost of the training model and improving the prediction precision. The regional ecological environment change prediction method based on online incremental learning can realize accurate prediction of regional ecological environment change.
Description
Technical Field
The invention relates to the field of ecological environment prediction models, in particular to a regional ecological environment change prediction method based on online incremental learning.
Background
With the development of economy and science, environmental issues are becoming increasingly the topic of interest and enthusiasm. Because environmental issues directly concern our physical and mental health and sustainable development of green ecology, especially the complicated problem of regional ecological environment change, government departments and experts in the environment are engaged in research and analysis. However, the regional ecological environment change can be influenced by various environmental factors and the mutual influence between adjacent regions, so the invention provides a regional ecological environment change prediction method based on online incremental learning.
The traditional prediction method for studying regional ecological environment change can be roughly divided into two methods, namely statistics and classical machine learning. However, because the data volume of the existing ecological environment, the data volume of the existing ecological environment and the large data volume and the related influence factors are very various, and the traditional method for predicting the regional ecological environment change consumes a great deal of time and financial resources, the invention provides a method for adopting incremental learning to solve the problem.
Disclosure of Invention
In order to solve the problems, the invention provides a regional ecological environment change prediction method based on online incremental learning, and compared with the traditional statistical method and machine learning method, the method can better improve the prediction accuracy and reduce the cost spent in model training.
The invention is realized by the following technical scheme: a regional ecological environment change prediction method based on online incremental learning comprises the following steps:
step 2: establishing a convolutional neural network layer;
and step 3: adding a time window and establishing an incremental limit learning machine model based on an adaptive genetic algorithm;
and 4, step 4: and establishing a long-term and short-term memory neural network layer and a full-connection neural network layer.
Further, the step 1 comprises the following specific steps:
step 1-1, data source: acquiring a time sequence composed of characteristics which may influence the change of the ecological environment of a certain area through a plurality of monitoring points arranged in the area;
step 1-2, discrete characteristic numeralization coding: processing the collected character features in the time sequence in a one-hot coding mode to obtain digital information;
step 1-3, missing value processing: filling missing data of not more than two time intervals by using the previous data, and filling missing data of more than two time intervals by adopting a linear interpolation method;
step 1-4, abnormal value processing: for the time sequence with the characteristic value of a few time periods obviously higher than the characteristic values of the preceding and following time periods, performing first-order difference operation on the characteristic value of the data of the preceding time interval and the characteristic value of the data of the preceding time interval; setting a threshold value to be 0.1, if the threshold value does not exceed 0.1, determining the data to be reasonable mutation data, otherwise, determining the data to be abnormal data; the abnormal data is processed by regarding the data as missing values, namely, the step 1-3 is operated;
step 1-5, standardization treatment: because the normalization processing is easily influenced by extreme values and has poor robustness, the data is normalized;
1-6, dividing a data set: the processed data is divided into 80% of training set according to the length of the time sequence, and the rest 20% of training set is testing set.
Further, the step 2 comprises the following specific steps:
2-1, establishing a convolutional neural network, and adding a convolutional layer and a pooling layer;
2-2, converting the data obtained through preprocessing into an n-dimensional matrix with a time sequence, wherein n-dimension refers to the characteristic quantity of the data;
and 2-3, inputting the n-dimensional matrix into a CNN structure for training and extracting important features.
Further, the step 3 comprises the following specific steps:
step 3-1, setting a proper time window size k;
step 3-2, the time sequence with the extracted important features enters an incremental extreme learning machine in batches through a time window;
3-3, setting a time input sequence as X;
3-4, establishing an expected threshold value as epsilon in the incremental extreme learning machine;
3-5, setting the number of initial nodes of a hidden layer in the incremental extreme learning machine to be 1;
3-6, setting the maximum value of the hidden layer node as M in the incremental extreme learning machine;
3-7, establishing a self-adaptive genetic algorithm model, which is specifically divided into a fitness calculation part, a selection operator part, a self-adaptive crossover operator part and a self-adaptive mutation operator part;
step 3-8, in the fitness calculation, the fitness of the ith sample F (i) is 1/E (i), and E (i) is an error function of the ith sample;
in the step 3-9 and the adaptive cross factor calculation, the calculation of the adaptive cross probability P (c) of the ith sample is divided into two cases:
(1) when f (i) < f (mean), f (mean) is the average fitness value of all samples;
at this time, p (c) ═ p (c) max, and p (c) max is the maximum cross probability of all samples;
(2) when F (i) > F (mean), P (c) min is the minimum cross probability in all samples;
at this time, P (c) ═ P (c) max- [ (P (c) max-P (c) min)
Enpochs (max) ]. Enpochs (i), Enpochs (max) and Enpochs (i)
Respectively the maximum iteration times of the samples and the iteration times of the ith sample;
3-10, in the calculation of the adaptive mutation factor, the calculation of the adaptive mutation probability P (m) of the ith sample is divided into two cases:
(1) when F (i) < F (mean), P (m) min is the minimum variation probability of all samples;
at this time, p (m) ═ p (m) min;
(2) when F (i) > F (mean), P (m) max is the maximum variation probability of all samples;
at this time, P (m) ═ P (m) min + [ (P (m) max-P (m) min)
/Enpochs(max)]*Enpochs(i)；
3-11, establishing a self-adaptive genetic algorithm model through the steps, and then obtaining the optimal hidden layer node number of the model through the model;
3-12, randomly assigning a weight W and a bias b to a hidden layer of the incremental extreme learning machine at the moment;
3-13, obtaining an input vector X1 ═ W × X + b of the activation function from the hidden layer of the incremental limit learning machine;
3-14, using an activation function sigmoid in the incremental limit learning machine, namely g (X) is 1/(1+ e ^ -X), namely the output obtained by the activation function is g (X1) and is recorded as H;
3-15, at the moment, the output weight W2 of the hidden layer node of the incremental limit learning machine is (E ^ H ^ T)/(H ^ T), and the initial value of E is the time sequence of the first batch of input incremental limit learning machines;
3-16, setting a residual error function E-W2H in the incremental extreme learning machine;
and 3-17, obtaining a final incremental limit learning machine model based on the adaptive genetic algorithm.
Further, the step 4 comprises the following specific steps:
step 4-1, establishing an LSTM deep learning network, wherein the network model mainly comprises an input layer, a hidden layer and an output layer;
4-2, the network optimizer uses an Adam algorithm, and the activation function uses a Relu function;
4-3, after the LSTM network is established, establishing a pruning layer (Dropout) to inhibit the result of the prediction of the LSTM layer from being over-fitted;
and 4-4, finally, establishing a full-connection neural network, decoding the output result of the LSTM layer, and finally obtaining the prediction result.
Compared with the prior art, the invention has the following beneficial effects: the invention provides a regional ecological environment change prediction method based on online incremental learning, which utilizes the idea of combining incremental learning and deep learning. The method can retain part of old knowledge and learn new knowledge in the Learning process through Incremental Learning (Incremental Learning); the learning efficiency does not decrease with the increase of data; the knowledge of the old model is not used in the training process; the incremental learning method can not only reduce the cost of training a large amount of data, but also improve the accuracy of a prediction system. The prediction method in the application not only can analyze a large amount of environment data and automatically extract important features, but also utilizes the advantage of the idea of incremental learning. The method can realize the retention of partial important old data of the line environment and the learning of the newly added data, and can greatly reduce the calculation amount and the complexity of the data, thereby reducing the calculation cost. The method also uses CNN neural networks, which are models that excel in extracting features, and therefore is used here to extract important features of data. In addition, the LSTM deep learning network is added in the method, and the special gate structure of the network model is particularly suitable for the time series prediction problem and can solve the defects that the gradient disappearance and the gradient explosion of the circulating neural network can occur. Therefore, compared with the traditional deep learning model, the accuracy of the operation can be improved.
Drawings
FIG. 1 is an overall flow framework diagram of the present invention;
FIG. 2 is a flow diagram of data preprocessing of the present invention;
FIG. 3 is a schematic diagram of the convolutional neural network structure of the present invention;
FIG. 4 is a schematic structural diagram of an incremental limit learning machine model based on an adaptive genetic algorithm according to the present invention;
FIG. 5 is a schematic diagram of a long term memory network according to the present invention.
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.
Moreover, the technical solutions in the embodiments of the present invention may be combined with each other, but it is necessary to be able to be realized by a person skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent, and is not within the protection scope of the present invention.
The invention provides a regional ecological environment change prediction method based on online Incremental Learning, which comprises the specific steps of preprocessing data, constructing a feature extraction layer by using a Convolutional Neural Network (CNN), putting the processed data into the CNN for training, putting a time sequence containing important features obtained by training into an Incremental Extreme Learning Machine (IELM) optimized based on an adaptive genetic algorithm in batches according to the size of a set time window for training and Learning, putting an output result into a Long Short-Term Memory Network (LSTM) for training, and decoding the obtained result by a Full Connected Neural Network (FCNN) to obtain a final prediction result.
A regional ecological environment change prediction method based on online incremental learning comprises the following steps:
step 2: establishing a convolutional neural network layer;
and step 3: adding a time window and establishing an incremental limit learning machine model based on an adaptive genetic algorithm;
and 4, step 4: and establishing a long-term and short-term memory neural network layer and a full-connection neural network layer.
Further, the step 1 comprises the following specific steps:
step 1-1, data source: acquiring a time sequence composed of characteristics which may influence the change of the ecological environment of a certain area through a plurality of monitoring points arranged in the area;
step 1-2, discrete characteristic numeralization coding: processing the collected character features in the time sequence in a one-hot coding mode to obtain digital information;
step 1-3, missing value processing: filling missing data of not more than two time intervals by using the previous data, and filling missing data of more than two time intervals by adopting a linear interpolation method;
step 1-4, abnormal value processing: for the time sequence with the characteristic value of a few time periods obviously higher than the characteristic values of the preceding and following time periods, performing first-order difference operation on the characteristic value of the data of the preceding time interval and the characteristic value of the data of the preceding time interval; setting a threshold value to be 0.1, if the threshold value does not exceed 0.1, determining the data to be reasonable mutation data, otherwise, determining the data to be abnormal data; the abnormal data is processed by regarding the data as missing values, namely, the operation of the step 1-3 is carried out;
step 1-5, standardization treatment: because the normalization processing is easily influenced by extreme values and has poor robustness, the data is normalized;
1-6, dividing a data set: the processed data is divided into 80% of training set according to the length of the time sequence, and the rest 20% of training set is testing set.
The step 2 comprises the following specific steps:
2-1, establishing a convolutional neural network, and adding a convolutional layer and a pooling layer;
2-2, converting the data obtained through preprocessing into an n-dimensional matrix with a time sequence, wherein n-dimension refers to the characteristic quantity of the data;
and 2-3, inputting the n-dimensional matrix into a CNN structure for training and extracting important features.
The step 3 comprises the following specific steps:
step 3-1, setting a proper time window size k;
step 3-2, the time sequence with the extracted important features enters an incremental extreme learning machine in batches through a time window;
3-3, setting a time input sequence as X;
3-4, establishing an expected threshold value as epsilon in the incremental extreme learning machine;
3-5, setting the number of initial nodes of a hidden layer in the incremental extreme learning machine to be 1;
3-6, setting the maximum value of the hidden layer node as M in the incremental extreme learning machine;
3-7, establishing a self-adaptive genetic algorithm model, which is specifically divided into a fitness calculation part, a selection operator part, a self-adaptive crossover operator part and a self-adaptive mutation operator part;
step 3-8, in the fitness calculation, the fitness of the ith sample F (i) is 1/E (i), and E (i) is an error function of the ith sample;
in the step 3-9 and the adaptive cross factor calculation, the calculation of the adaptive cross probability P (c) of the ith sample is divided into two cases:
(1) when f (i) < f (mean), f (mean) is the average fitness value of all samples;
at this time, p (c) ═ p (c) max, and p (c) max is the maximum cross probability of all samples;
(2) when F (i) > F (mean), P (c) min is the minimum cross probability in all samples;
at this time, P (c) ═ P (c) max- [ (P (c) max-P (c) min)
Enpochs (max) } enpochs (i), enpochs (max) and enpochs (i) are the maximum number of iterations of the sample and the number of iterations up to the ith sample, respectively;
3-10, in the calculation of the adaptive mutation factor, the calculation of the adaptive mutation probability P (m) of the ith sample is divided into two cases:
(1) when F (i) < F (mean), P (m) min is the minimum variation probability of all samples;
at this time, p (m) ═ p (m) min;
(2) when F (i) > F (mean), P (m) max is the maximum variation probability of all samples;
at this time, P (m) ═ P (m) min + [ (P (m) max-P (m) min)
/Enpochs(max)]*Enpochs(i)；
3-11, establishing an adaptive genetic algorithm model through the steps, and obtaining the adaptive genetic algorithm model through the model
The number of nodes of the optimal hidden layer of the model;
3-12, randomly assigning a weight W and a bias b to a hidden layer of the incremental extreme learning machine at the moment;
3-13, obtaining an input vector X1 ═ W × X + b of the activation function from the hidden layer of the incremental limit learning machine;
3-14, using an activation function sigmoid in the incremental limit learning machine, namely g (X) is 1/(1+ e ^ -X), namely the output obtained by the activation function is g (X1) and is recorded as H;
3-15, wherein the output weight W2 of the hidden layer node of the incremental limit learning machine is (E x H ^ T)/(H x H ^ T), and the initial value of E is the time sequence of entering the IELM of the first batch;
3-16, setting a residual error function E-W2H in the incremental extreme learning machine;
and 3-17, obtaining a final incremental limit learning machine model based on the adaptive genetic algorithm.
The step 4 comprises the following specific steps:
step 4-1, establishing an LSTM deep learning network, wherein the network model mainly comprises an input layer, a hidden layer and an output layer;
4-2, the network optimizer uses an Adam algorithm, and the activation function uses a Relu function;
4-3, after the LSTM network is established, establishing a pruning layer (Dropout) to inhibit the result of the prediction of the LSTM layer from being over-fitted;
and 4-4, finally, establishing a full-connection neural network, decoding the output result of the LSTM layer, and finally obtaining the prediction result.
In the step 3 of the invention, an incremental limit learning machine model based on an adaptive genetic algorithm is used, and the improved method can quickly find the number of nodes of the optimal hidden layer of the model and also has the advantage of incremental learning. The method has the advantages that the characteristics of reserving partial important old data and learning newly added data are realized, and compared with the traditional increment limit learning machine, the calculation cost is reduced, and the over-fitting phenomenon is also inhibited.
The invention uses the idea of combining various deep learning algorithms and incremental learning algorithms to build a prediction system. The method comprises the steps of extracting features by using a convolutional neural network, enabling a time sequence with the features to enter an incremental model in batches by using a time window, and training and learning the time sequence by using a long-short term memory network and a fully-connected neural network. By the method, the change of the regional ecological environment can be accurately predicted.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (5)
1. A regional ecological environment change prediction method based on online incremental learning is characterized by comprising the following steps: the method comprises the following steps:
step 1, preprocessing data;
step 2: establishing a convolutional neural network layer;
and step 3: adding a time window and establishing an incremental limit learning machine model based on an adaptive genetic algorithm;
and 4, step 4: and establishing a long-term and short-term memory neural network layer and a full-connection neural network layer.
2. The regional ecological environment change prediction method based on online incremental learning of claim 1, wherein the regional ecological environment change prediction method comprises the following steps: the step 1 comprises the following specific steps:
step 1-1, data source: acquiring a time sequence composed of characteristics which may influence the change of the ecological environment of a certain area through a plurality of monitoring points arranged in the area;
step 1-2, discrete characteristic numeralization coding: processing the collected character features in the time sequence in a one-hot coding mode to obtain digital information;
step 1-3, missing value processing: filling missing data of not more than two time intervals by using the previous data, and filling missing data of more than two time intervals by adopting a linear interpolation method;
step 1-4, abnormal value processing: for the time sequence with the characteristic value of a few time periods obviously higher than the characteristic values of the preceding and following time periods, performing first-order difference operation on the characteristic value of the data of the preceding time interval and the characteristic value of the data of the preceding time interval; setting a threshold value to be 0.1, if the threshold value does not exceed 0.1, determining the data to be reasonable mutation data, otherwise, determining the data to be abnormal data; the abnormal data is processed by regarding the data as missing values, namely, the operation of the step 1-3 is carried out;
step 1-5, standardization treatment: because the normalization processing is easily influenced by extreme values and has poor robustness, the data is normalized;
1-6, dividing a data set: the processed data is divided into 80% of training set according to the length of the time sequence, and the rest 20% of training set is testing set.
3. The regional ecological environment change prediction method based on online incremental learning of claim 1, wherein the regional ecological environment change prediction method comprises the following steps: the step 2 comprises the following specific steps:
2-1, establishing a convolutional neural network, and adding a convolutional layer and a pooling layer;
2-2, converting the data obtained through preprocessing into an n-dimensional matrix with a time sequence, wherein n-dimension refers to the characteristic quantity of the data;
and 2-3, inputting the n-dimensional matrix into a CNN structure for training and extracting important features.
4. The regional ecological environment change prediction method based on online incremental learning of claim 1, wherein the regional ecological environment change prediction method comprises the following steps: the step 3 comprises the following specific steps:
step 3-1, setting a proper time window size k;
step 3-2, the time sequence with the extracted important features enters an incremental extreme learning machine in batches through a time window;
3-3, setting a time input sequence as X;
3-4, establishing an expected threshold value as epsilon in the incremental extreme learning machine;
3-5, setting the number of initial nodes of a hidden layer in the incremental extreme learning machine to be 1;
3-6, setting the maximum value of the hidden layer node as M in the incremental extreme learning machine;
3-7, establishing a self-adaptive genetic algorithm model, which is specifically divided into a fitness calculation part, a selection operator part, a self-adaptive crossover operator part and a self-adaptive mutation operator part;
step 3-8, in the fitness calculation, the fitness of the ith sample F (i) is 1/E (i), and E (i) is an error function of the ith sample;
in the step 3-9 and the adaptive cross factor calculation, the calculation of the adaptive cross probability P (c) of the ith sample is divided into two cases:
(1) when f (i) < f (mean), f (mean) is the average fitness value of all samples;
at this time, p (c) ═ p (c) max, and p (c) max is the maximum cross probability of all samples;
(2) when F (i) > F (mean), P (c) min is the minimum cross probability in all samples;
at this time, p (c) ═ p (c) max- [ (p (c) max-p (c) min)/enpochs (max) ] × enpochs (i), enpochs (max) and enpochs (i) are the maximum number of iterations of the sample and the number of iterations up to the ith sample, respectively;
3-10, in the calculation of the adaptive mutation factor, the calculation of the adaptive mutation probability P (m) of the ith sample is divided into two cases:
(1) when F (i) < F (mean), P (m) min is the minimum variation probability of all samples;
at this time, p (m) ═ p (m) min;
(2) when F (i) > F (mean), P (m) max is the maximum variation probability of all samples;
where p (m) ═ p (m) min + [ (p (m) max-p (m) min)/enpochs (max) ]. enpochs (i);
3-11, establishing a self-adaptive genetic algorithm model through the steps, and then obtaining the optimal hidden layer node number of the model through the model;
3-12, randomly assigning a weight W and a bias b to a hidden layer of the incremental extreme learning machine at the moment;
3-13, obtaining an input vector X1 ═ W × X + b of the activation function from the hidden layer of the incremental limit learning machine;
step 3-14, using the activation function sigmoid in the incremental limit learning machine, i.e. g (x) 1/(1+ e ^ -x),
namely, the output g (X1) obtained by the activation function is recorded as H;
3-15, at the moment, the output weight W2 of the hidden layer node of the incremental limit learning machine is (E ^ H ^ T)/(H ^ T), and the initial value of E is the time sequence of the first batch of input incremental limit learning machines;
3-16, setting a residual error function E-W2H in the incremental extreme learning machine;
and 3-17, obtaining a final incremental limit learning machine model based on the adaptive genetic algorithm.
5. The regional ecological environment change prediction method based on online incremental learning of claim 1, wherein the regional ecological environment change prediction method comprises the following steps: the step 4 comprises the following specific steps:
step 4-1, establishing an LSTM deep learning network, wherein the network model mainly comprises an input layer, a hidden layer and an output layer;
4-2, the network optimizer uses an Adam algorithm, and the activation function uses a Relu function;
4-3, after the LSTM network is established, establishing a pruning layer (Dropout) to inhibit the result of the prediction of the LSTM layer from being over-fitted;
and 4-4, finally, establishing a full-connection neural network, decoding the output result of the LSTM layer, and finally obtaining the prediction result.
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