CN108510132A - A kind of sea-surface temperature prediction technique based on LSTM - Google Patents

A kind of sea-surface temperature prediction technique based on LSTM Download PDF

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CN108510132A
CN108510132A CN201810717212.XA CN201810717212A CN108510132A CN 108510132 A CN108510132 A CN 108510132A CN 201810717212 A CN201810717212 A CN 201810717212A CN 108510132 A CN108510132 A CN 108510132A
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曹亮
张鹏
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China International Science And Technology (beijing) Satellite Communication Technology Co Ltd
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Abstract

A kind of sea-surface temperature prediction technique based on LSTM, including prediction model and forecast future time period sea-surface temperature two parts are generated, which includes：1) z score standardizations are carried out to the history sea-surface temperature data within the scope of setting longitude and latitude and generates corresponding time row；2) it is 1 (day) to utilize time row and the standardized history sea-surface temperature data of z score to train LSTM models, time step, and input dimension is 1, obtains the sea-surface temperature prediction model based on LSTM；The forecast future time period sea-surface temperature includes：The standardized same day sea-surface temperature data of z score are inputted into forecasting model, setting prediction step number obtains the output of future time period as a result, output dimension is 1, and carrying out anti-z score standardizations to output result obtains sea-surface temperature predicted value.The present invention can be between mining data length time-dependent relation, be more suitable for learning the long periodicity changing rule of sea-surface temperature, preferable prediction result can be obtained.

Description

A kind of sea-surface temperature prediction technique based on LSTM
Technical field
The present invention relates to marine meterologal prediction fields, more particularly to a kind of sea based on LSTM recurrent neural networks models Surface temperature prediction method.
Background technology
Sea-surface temperature is a kind of and the closer maritime meteorology data of human relation, such as ship's navigation, sea fishery The production operations such as production, offshore oil platform are closely bound up with sea-surface temperature, scientific and reasonable sea-surface temperature predicted value There is certain directive significance for mankind's offshore production operation.The acquisition of sea-surface temperature, because far from inland, conventional observation hand Section such as buoy is difficult to cover comprehensively, main at present to carry out inverting using remote sensing satellite, therefore the prediction of sea-surface temperature becomes Relative difficulty.
LSTM (Long-Short Term Memory) recurrent neural network achieved in many fields great in recent years It breaks through, which can go through input data autonomous learning with the length dependence between learning time sequence data What contribution of the history data to prediction data, the especially neural network overcame that other neural networks can not learn for a long time lacks Point is more suitable for the long periodicity changing rule of excavation event.Existing neural network model is complex, need to use a variety of nerves Network, just can be achieved sea-surface temperature prediction, occur that the extra large surface temperature of LSTM recurrent neural network systems progress is used alone The method of prediction is spent, and the warm forecast precision Standard General in sea, at 0.8 DEG C or so, forecast precision is relatively low.
Invention content
The present invention provides a kind of sea-surface temperature prediction technique based on LSTM, solves sea-surface temperature in the prior art The not high technical problem of precision of prediction.
To solve the above-mentioned problems, the present invention adopts the following technical scheme that：
Include the following steps：Step A generates prediction model；Step B forecasts future time period sea-surface temperature；
The step A includes the following steps：
A1 extracts marine environment data, using the existing remote sensing satellite data in the whole world, is finally inversed by marine surface temperature SST numbers According to precision is 15 ' × 15 ', daily；
A2 extraction times arrange, and according to history SST data, the time of first time extraction SST are set as 1, next nature day is 2, and so on, smoothly gradually add 1 according to natural day, generated time row；
The history SST data that A3 obtains step A1 carry out z-score standardizations, and the time that step A2 is obtained arranges Input data with the standardized history SST of z-score as LSTM models, input dimension are 1；
A4 build and training step A3 obtained by LSTM models, continuous adjusting parameter, preferentially Selecting All Parameters be based on The sea-surface temperature prediction model of LSTM；
The step B includes the following steps：
Same day SST data handle with the z-score standardized methods used in step A3 by B1, and the time on the same day is arranged Enter fishing ground forecasting model with standardized SST data category, Prediction Parameters are set, obtains the output of future time period as a result, output dimension Degree is 1；
The output result that B2 obtains B1 carries out anti-z-score standardizations, the anti-z-score standardized methods and Z-score standardization is corresponding in step A3, and processing obtains the SST predicted values of future time period, and precision of prediction is 15 ' × 15 ', often It.
As further preferably, the step A4 includes as follows：
A41：LSTM models are built under Linux system；
A42：The input dimension of LSTM and the time step of input data are set；
A43：LSTM input datas are set and read batch sizes and length of window；
A44：LSTM model optimizers and learning rate are set；
A45：Hidden layer neuromere is arranged to count；
A46：Model iterations are set；
A47：Continuous adjusting parameter checks model degree of convergence with model loss, preferentially chooses high degree of convergence parameter, formed Fishing ground prediction model based on LSTM.
It is to be based on the beneficial effects of the invention are as follows a kind of sea-surface temperature prediction technique based on LSTM provided by the invention Time series excavates the long-term Fluctuation of sea-surface temperature data, can obtain preferable simulation and forecast effect, and precision of prediction is high.
Description of the drawings
Fig. 1 is sea-surface temperature prediction flow chart of the present invention；
Fig. 2 is LSTM recurrent neural network location mode control figures；
Fig. 3 is LSTM Recursive Neural Network Structure figures；
Fig. 4 is LSTM sea-surface temperatures prediction model at 45 ° 7 ' 30 " marine sites E N, 155 ° 7 ' 30 " sea-surface temperature Simulation and forecast design sketch.
Specific implementation mode
The present invention is described in detail below in conjunction with attached drawing and concrete example, it is noted that discussed below Example is only the more convenient understanding present invention, does not play any restriction effect to invention itself.
A kind of sea-surface temperature prediction technique based on LSTM that the present invention provides, as shown in Figure 1, the method comprising the steps of A generates two step of sea-surface temperature of prediction model and step B prediction future time periods.
Step A generates prediction model：
Step A1, marine environment data is extracted：Using the existing remote sensing satellite data in the whole world, it is finally inversed by marine surface temperature SST data, precision are 15 ' × 15 ' (longitudes × latitude), daily；
Step A2, extraction time arranges：According to history SST data, the time (natural day) of first time extraction SST is set as 1, Next nature day is 2, and so on, gradually add 1 according to natural day sequence, generated time row；
Step A3, z-score standardizations are carried out to the history SST that step A1 is obtained, will arranges the time and is marked with z-score The SST of standardization is 1 as LSTM mode input data, input dimension；
Step A31：Extract the mean value and standard deviation of history SST in 15 ' × 15 ' ranges.
Step A32：History SST is standardized using z-score standardized methods, z-score standardization is public Formula is：
X '=(x- μ)/σ (1)
Wherein, x ' is the z-score standardized values of x, and μ is x array mean values, and σ is x array standard deviations.
Step A4, LSTM models are built and trained, by continuous adjusting parameter, are preferentially chosen using the model degree of convergence as standard Prediction model of the gain of parameter based on LSTM；
Step B predicts that the sea-surface temperature of future time period includes：
Step B1, the z-score standardized method consistent in step A3 of same day SST data is handled, by the same day Time row and standardized SST input steps A4 obtain sea-surface temperature prediction model, setting prediction step number, obtain future The output of period is as a result, output dimension is 1；LSTM models can utilize current t moment data prediction future t+1 time datas.It goes through History data are mainly used to training pattern, are the data of a period of time sequence, same day data be then for predicting future, be it is current this The data at one moment.Usual same day data are included in historical data, but while being not included in interior also can be predicted.
Step B2, the anti-z-score standardizations of result, anti-z-score standardized methods and step A3 are exported to model Middle z-score standardized methods are corresponding, and processing obtains the SST predicted values of future time period, and precision of prediction is 15 ' × 15 ', often It；
Step A4 further comprises the following contents：
Step A41：LSTM models are built under Linux system；
Step A42：The input dimension of LSTM and the time step of input data are set.It is 1 that dimension is inputted in the present invention, when Spacer step a length of 1 (day)；
Step A43：LSTM input datas are set and read batch sizes and length of window.It uses and criticizes in model training (batch) processing mode reads data, randomly selects the sequence of length of window (window size) in input data, and wrap Dress up the batch data of batch sizes (batch size).That is batchsize sequence is shared in a batch, each sequence Length is windowsize.The setting of batch sizes and window size is mainly imitated for balance computer memory size and operation Rate, it is related with computer operational performance itself；
Step A44：LSTM model optimizers and learning rate are set.This method uses Adam algorithm optimization devices, study speed Rate is 0.001；
Step A45：Hidden layer neuromere is arranged to count；
Step A46：Model iterations are set；
Step A47：Hidden layer neuromere is adjusted under same iterations to count, is whole in the points downward of same hidden layer neuromere Iterations check model degree of convergence with model whole loss and iteration loss, preferentially choose high degree of convergence parameter, form base In the sea-surface temperature prediction model of LSTM.
The control of LSTM recurrent neural network location modes is as shown in Figure 2.The core of LSTM is control unit state c, control System includes forgeing door ft, input gate it, out gate ot.Current t moment forgets door ftIt is responsible for the c of control last momentt-1How many It is saved in the c at current timet；Input gate itIt is responsible for the immediate status at control current timeHow many is input to active cell shape State ct；Out gate otIt is responsible for control active cell state ctHow many exports h as the hidden layer at current timet.Its calculation formula point It is not：
ft=σ (wf·[xt,ht-1]+bf) (2)
it=σ (wi·[xt,ht-1]+bi) (3)
ot=σ (wo·[xt,ht-1]+bo) (4)
Wherein, wf、wi、woIt is the weight matrix for forgeing door, input gate and out gate, b respectivelyf、bi、boIt is to forget respectively The bias term of door, input gate and out gate, σ are sigmoid functions
LSTM Recursive Neural Network Structures are as shown in Figure 3.The input of LSTM includes：The location mode c of last momentt-1, on One moment LSTM hidden layer output valve ht-1, current t moment network input value xt；The output of LSTM includes：The unit at current time State ctWith the hidden layer output valve h of current time LSTMt。
Wherein, current input unit stateBy the input x of current t moment networkt, last moment LSTM hidden layer output valve ht-1It codetermines, calculation formula is：
Wherein, wcIt is the weight matrix of input unit state, bcThe bias term of input unit state, tanh be hyperbolic just Cut function
Active cell state ctBy forgetting door ft, last moment location mode ct-1, input gate itThe unit currently inputted StateIt codetermines, calculation formula is：
Wherein, symbol ⊙ expressions are multiplied by element.
The hidden layer output valve h of current time LSTMtBy out gate otWith active cell state ctIt codetermines, calculates public Formula is：
ht=ot⊙tanh(ct) (7)
And LSTM neural networks export Its calculation formula is：
Using mean square error (MSE) as loss function (loss) in the present invention, calculation formula is：
Wherein, N is sample number, and x is observed value, and x ' is predicted value.
Experimental example
It will be exemplified below the sea-surface temperature prediction technique effect based on LSTM.With 45 ° 7 ' 30 " N, 155 ° 7 ' 30 " for 15 ' × 15 ' range marine sites centered on E, which is located at northwest Pacific, and originally experience Kuroshio warm current and Oyashio are cold Joint effect is flowed, within the scope of northwest Pacific saury fishing ground, has and centainly represents meaning.
The history SST data in above range are extracted, time range is 1982-01-01 to 2015-12-31, is amounted to 34 years 12418 days, wherein data available 12418 days, and corresponding time row are generated according to the history SST times, as shown in table 1.
1 45 ° 7 ' 30 of the table " marine sites E N, 155 ° 7 ' 30 " history SST data
Serial number Time Time arranges SST/℃
1 19820101 1 3.36
2 19820102 2 3.48
3 19820103 3 3.57
4 19820104 6 3.52
5 19820105 8 3.31
6 19820106 9 3.00
7 19820107 11 3.04
… … … …
998 19840924 998 13.38
999 19840925 999 13.39
1000 19840926 1000 13.31
… … … …
12417 20151230 12417 3.50
12418 20151231 12418 3.36
Z-score standardizations are carried out to SST.The mean value of history SST data is 6.459004 DEG C, and standard deviation is 4.248163, using time row and the standardized SST of z-score as the input of LSTM models, input data is as shown in table 2.
2 LSTM mode input data of table
If batch sizes are 12, length of window 30.In iteration 300 times, the number of hidden nodes is constantly adjusted, with mould Type whole loss checks the LSTM sea-surface temperature prediction model degrees of convergence, and the results are shown in Table 3.
LSTM whole loss when 3 difference the number of hidden nodes of table
As can be seen from Table 3, when the number of hidden nodes is 300 or more, LSTM model whole loss starts to tend towards stability.When Whole loss is minimum when hidden layer number of nodes is 400, is 0.00054356.
If the number of hidden nodes is 400, iteration checks LSTM models convergent under different iterations 2500 times, with this time The LSTM sea-surface temperature prediction model degrees of convergence are checked in iteration loss, and the results are shown in Table 4.
Secondary iteration loss when 4 difference iterations of table
As can be seen from Table 4, in the case of the number of hidden nodes is 400, when iterations are more than 300 times, iteration loses In stabilization.When wherein iterations are 601, iteration loss reduction is 0.00130815.
It should be noted that convergence direction is inconsistent when LSTM models are trained every time, each run result is caused all to slightly have Difference, but difference is little, and global convergence trend is consistent.LSTM hidden layer neuromeres points are set as 400, iteration 600 Secondary to be trained, setting prediction step number is 7, i.e. the SST data of following one week of prediction, modeling prediction effect as shown in figure 4, Wherein black represents SST actual values, and green represents the SST analogues value, and red represents SST predicted values.The analogue value is namely based on existing Truthful data, establish the simulation to data with existing after LSTM models, the analogue value with actual value registration is higher illustrates pattern die Quasi- effect is better.In this experimental example, the analogue value is overlapped with actual value height, is achieved preferable simulation effect, is based on height weight Reason is closed, uses colored picture of drawing with display model effect.As a result display model whole loss is 0.00041835, SST moulds Quasi- precision is 94.72%, model export result be [- 0.7402, -0.7501, -0.7590, -0.7680, -0.7773, - 0.7865, -0.7958], obtained after anti-z-score standardizations one week i.e. 2016-01-01 to 2016-01-07 following Sea-surface temperature predicted value be respectively 3.3145 DEG C, 3.2724 DEG C, 3.2346 DEG C, 3.1964 DEG C, 3.1569 DEG C, 3.1178 DEG C, 3.0783 DEG C.According to existing satellite data, this week SST actual value is respectively 3.36 DEG C, 3.24 DEG C, 3.11 DEG C, 3.16 DEG C, 3.23 DEG C, 3.01 DEG C, 2.89 DEG C, obtain following one day, three days, five days, one week precision of prediction be respectively 98.64%, 97.88%, 98.04%, 97.16%.
It should be noted that when predict step number be more than or equal to 2 when, the t+2 moment (predicted value of current time t) be It is predicted on the basis of t+1 moment predicted values, but the t+1 moment not yet occurs, lacks actual value, therefore predicts step number Longer, prediction error is bigger.Sea-surface temperature variational regularity itself is strong, 7 days following using LSTM model predictions in the present invention Sea-surface temperature value can still keep degree of precision.
The above description is merely a specific embodiment is illustrated, but protection scope of the present invention is not Be confined to this, the prediction of the sea-surface temperature of any ocean geography position based on LSTM, should all cover the scope of the present invention it It is interior.

Claims (2)

1. a kind of sea-surface temperature prediction technique based on LSTM, which is characterized in that include the following steps：Step A production forecasts Model；Step B forecasts future time period sea-surface temperature；
The step A includes the following steps：
A1 extracts marine environment data, using the existing remote sensing satellite data in the whole world, is finally inversed by marine surface temperature SST data, Precision is 15 ' × 15 ', daily；
A2 extraction times arrange, and according to history SST data, the time of first time extraction SST is set as 1, and next nature day is 2, according to It is secondary to analogize, smoothly gradually add 1 according to natural day, generated time row；
The history SST data that A3 obtains step A1 carry out z-score standardizations, the time row and z- that step A2 is obtained Input datas of the standardized history SST of score as LSTM models, input dimension are 1；
A4 builds the LSTM models obtained by simultaneously training step A3, and continuous adjusting parameter, preferentially Selecting All Parameters acquisition is based on LSTM's Sea-surface temperature prediction model；
The step B includes the following steps：
Same day SST data handle with the z-score standardized methods used in step A3 by B1, and the time on the same day is arranged and marked The SST data categories of standardization enter fishing ground forecasting model, and Prediction Parameters are arranged, and obtain the output of future time period as a result, output dimension is 1；
B2 carries out anti-z-score standardizations, the anti-z-score standardized methods and step to the output result that B1 is obtained Z-score standardization is corresponding in A3, and processing obtains the SST predicted values of future time period, and precision of prediction is 15 ' × 15 ', daily.
2. a kind of sea-surface temperature prediction technique based on LSTM according to claim 2, which is characterized in that the step A4 includes as follows：
A41：LSTM models are built under Linux system；
A42：The input dimension of LSTM and the time step of input data are set；
A43：LSTM input datas are set and read batch sizes and length of window；
A44：LSTM model optimizers and learning rate are set；
A45：Hidden layer neuromere is arranged to count；
A46：Model iterations are set；
A47：Continuous adjusting parameter checks model degree of convergence with model loss, preferentially chooses high degree of convergence parameter, formation is based on The fishing ground prediction model of LSTM.
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Application publication date: 20180907