CN111680784A - Sea surface temperature deep learning prediction method based on time-space multidimensional influence - Google Patents

Sea surface temperature deep learning prediction method based on time-space multidimensional influence Download PDF

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CN111680784A
CN111680784A CN202010461315.1A CN202010461315A CN111680784A CN 111680784 A CN111680784 A CN 111680784A CN 202010461315 A CN202010461315 A CN 202010461315A CN 111680784 A CN111680784 A CN 111680784A
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谢江
张吉缘
徐凌宇
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Abstract

The invention provides a sea surface temperature deep learning prediction method based on space-time multidimensional influence. The method comprises three steps: 1. quantification of spatial influence: and quantifying the spatial influence on the target observation point by using the sea surface temperature data of the neighbor observation point of the target observation point, and constructing a multi-dimensional space-time sea surface temperature data set on the target sea area. 2. And (3) data completion processing: and performing data completion processing on the data set by using a neighbor data averaging method. 3. Establishing a prediction model: a sea surface temperature deep learning prediction model based on space-time multidimensional influence, namely a Convolutional gated cyclic unit (Convolutional gated loop) Multilayer Perceptron (CGMP), is established by combining deep learning technologies such as GRU, CNN and MLP. The method integrates space-time multidimensional influence, combines a deep learning technology mode, establishes a high-precision sea surface temperature prediction model, and can be widely applied to sea surface temperature prediction of different sea areas and different scales.

Description

Sea surface temperature deep learning prediction method based on time-space multidimensional influence
Technical Field
The invention relates to a sea surface temperature prediction method.
Background
Sea Surface Temperature (SST), also known as Surface Temperature, refers to the Temperature of water near the Surface of the ocean. The exact meaning of the sea surface will vary depending on the measurement method used, but is typically between 1 mm and 20 meters below the sea surface. The sea surface temperature plays a fundamental role in the energy exchange between the sea and the atmosphere, and is also a very important parameter in marine dynamics and climate change. Small changes in sea surface temperature have profound effects on global climate, marine ecosystems and even marine life, and the well-known phenomena of el nino and raney are caused by changes in sea surface temperature anomaly (SST). Climate models driven by observed or simulated sea surface temperatures or sea surface temperature anomalies are often used as standard tools for seasonal climate prediction. Sea surface temperature is also often used as a key factor in the study of marine ecosystems and marine life. Therefore, the method has important significance in accurately and effectively observing and predicting the sea surface temperature. It not only makes human know the global climate and marine ecosystem better, but also is an important component for the application of marine related fields, such as extreme weather forecast, seasonal climate forecast, marine organism research and sustainable development fishery. However, various physical and environmental factors (heat flux, radiation, and the weather near the sea surface) affect the sea surface temperature variations, making them highly random and uncertain. Therefore, it is still a challenge to provide a high-precision sea surface temperature prediction method.
The sea surface temperature prediction method can be classified into two types. One is a numerical method and the other is a data-driven method. The traditional numerical method does not easily model the changes in the sea surface temperature because it requires much knowledge of the sea and atmospheric domains and predicts the sea surface temperature with relatively low resolution, usually on a marine or even global scale. Compared with a numerical method, a data-driven method, particularly a deep learning method, requires less knowledge of the sea and atmosphere fields and can predict the sea surface temperature with high resolution on a smaller scale, so it is easier to model to accurately predict the sea surface temperature of the target sea area. However, the sea surface temperature deep learning prediction method based on the time influence generally improves the prediction performance by enhancing the capability of processing time information, and such methods usually focus on capturing the time influence of the historical sea surface temperature, but neglect the overall spatial influence of the target sea area on the target observation point, so that such methods have obvious performance bottlenecks.
Disclosure of Invention
The sea surface temperature deep learning prediction model based on the space-time multidimensional influence is established by integrating the space-time multidimensional influence and combining deep learning technologies such as a Recurrent Neural Network (RNN), a Convolutional Neural Network (CNN) and a Multilayer Perceptron (MLP), so that the performance bottleneck problem is solved, and the accuracy of sea surface temperature prediction is further improved.
The invention is realized by the following technical scheme:
a sea surface temperature deep learning prediction method based on space-time multidimensional influence builds a high-precision sea surface temperature prediction model, namely a convolution gating circulation unit (CGMP) Multilayer Perceptron, by means of fusion of the space-time multidimensional influence and combination of a deep learning technology. The model can overcome the performance bottleneck of the sea surface temperature deep learning prediction model based on the time influence, and can further improve the accuracy of sea surface temperature prediction. The method comprises the following specific steps:
A. quantification of spatial influence: quantifying the space influence on the target observation point by using the sea surface temperature data of the neighbor observation point of the target observation point, and constructing a multi-dimensional space-time sea surface temperature data set on a target sea area;
B. and (3) data completion processing: performing data completion processing on the data set by using a neighbor data averaging method;
C. establishing a prediction model: and (3) establishing a sea surface temperature deep learning prediction model based on the time-space multi-dimensional influence by combining deep learning technologies such as GRU, CNN and MLP, namely the multilayer perceptron CGMP of the convolution gating circulation unit.
The method comprises the following steps of A, quantifying the space influence on the target observation point by using the neighbor observation point sea surface temperature data of the target observation point, and constructing a multi-dimensional space-time sea surface temperature data set on a target sea area, wherein the specific steps are as follows:
a1: the sea surface may be divided into individual grids according to longitude, latitude, and resolution information. One grid is a sea surface temperature observation point. The surface temperature prediction task may be expressed simply as using historical surface temperature data to predict future surface temperature data at the target surface temperature observation. In order to quantify the spatial influence of the target sea surface temperature observation point quantitatively, the spatial influence of the target observation point is assumed to be totally from eight adjacent sea surface temperature observation points, which are called neighbor points, so that a multi-dimensional space-time sea surface temperature data set can be constructed in a target sea area, namely a sea surface temperature (daily average value/weekly average value/monthly average value) complete data set and a sea surface temperature (daily average value/weekly average value/monthly average value) ocean data set, wherein the complete data set comprises data with missing spatial information, and the ocean data set excludes data with missing spatial information.
And B, performing data completion processing on the data set by using a neighbor data averaging method, and specifically comprising the following steps of:
and B1, performing data completion processing on the complete data set by using a neighbor data averaging method. For some target observation points close to the coast, some of their neighbors may be located in the land area, and therefore there is no sea surface temperature observation data at these neighbors, which are called land sites. Therefore, in order to unify and standardize the input of the prediction model, data completion processing is performed on the land points located in the land area. For a land site, the complemented sea surface temperature data is the average value of the real sea surface temperature observation data of the eight neighbor points, namely, a neighbor data averaging method. The data completion method has the advantages of strong practicability and suitability for all different deficiency conditions. If other land points exist in the eight neighbor points, the average value of the remaining sea surface points is calculated after the eight neighbor points are removed.
And C, establishing a sea surface temperature deep learning prediction model based on the space-time multidimensional influence by combining deep learning technologies such as GRU, CNN and MLP, namely a convolution gating cyclic unit multilayer perceptron CGMP, and specifically comprising the following steps:
c1, CGMP is an end-to-end sea surface temperature prediction model, the input of which is a historical SST sequence and the output of which is a predicted future SST sequence. Wherein the historical SST sequence comprises neighbor information and historical information of the observation point of the target sea surface temperature. The CGMP firstly processes the neighbor information of a target observation point on the spatial dimension through a convolution layer to obtain an intermediate vector, namely an empty SST sequence. The CGMP then processes the historical information of the spatio-temporal SST sequence in the time dimension through a GRU, resulting in a hidden state vector. Finally, the multi-layered perceptron maps the hidden state vector onto the prediction result, i.e., the future SST sequence. Convolutional layers are good at processing grid-like structure data and GRUs are good at processing sequence data, so the combinatorial collocation of convolutional layers and GRUs can sufficiently and effectively mine neighbor and historical information in the spatio-temporal dimension. Wherein:
c11, historical SST sequence X is a tensor with one dimension k × 3 × 3, and includes neighbor information (matrix with dimension 3 × 3) and historical information of k time units of the observation point of the target sea surface temperature. Since convolutional neural networks are good at handling grid-like structured data, CGMP first processes the neighbor information of the target observation point in the spatial dimension through one convolutional layer. The depth of the convolutional layer is 1, the size of the convolutional kernel K is 3 × 3, the step size is 1, and the padding is 0. After X is subjected to convolution operation, a space-time SST sequence S with the dimensionality of k multiplied by 1 is obtained, and the operation process is defined as follows:
S＝(s1，s2，...，sk)
si＝Xi★K+bs，i∈{1，2，...，k}
Figure BDA0002511078530000031
wherein s isiRepresenting the processed spatio-temporal history information, ★ representing a two-dimensional convolution operation, wk,1–wk,9Representing the weight of the convolution kernel K, bsIndicating the corresponding bias.
C12, the spatiotemporal SST sequence S contains historical information of k time units of the target sea surface temperature observation point. CGMP is subsequently communicated because GRU is good at processing sequence dataHistorical information of the target observation point is processed by one GRU in a time dimension. The GRU receives two inputs at each time step: one is spatio-temporal history information S from a spatio-temporal SST sequence SiThe other is the hidden state vector H from the previous time step(i-1)Or an initial hidden state vector H0. After the history information is processed, the GRU generates a new hidden state vector H at each time stepi. So GRUs generate k hidden state vectors in total, i.e., H1–Hk. The process is defined as follows:
ri＝σ(Wrssi+WrHH(i-1)+br)
zi＝σ(Wzssi+WzHH(i-1)+bz)
ni＝tanh(Wnssi+bns+ri*(WnHH(i-1)+bnH))
Hi＝(1-zi)*ni+zi*H(i-1)
wherein HiRepresenting the hidden state vector, s, of the GRU at time step iiRepresenting spatio-temporal history information at time step i, H(i-1)Representing the hidden state vector at time step i-1 or representing the initial hidden state vector at time step 0, ri、ziAnd niRespectively representing the reset, update and new gates of the GRU. σ denotes a sigmoid function, and σ denotes a Hadamard product. W represents the corresponding weight and b represents the corresponding bias.
Finally, CGMP uses a multi-layer perceptron to hide the state vector HkMapped onto the prediction, i.e. the future SST sequence Y. The mapping process is defined as follows:
Y＝fMLP(WYHk+bY)
where WY represents the corresponding weight and bY represents the corresponding bias.
The invention has the beneficial effects that:
1. the traditional sea surface temperature deep learning and prediction method based on the time influence usually focuses on capturing the time influence of historical sea surface temperature, but neglects the overall spatial influence of a target sea area on a target observation point, so that the method has obvious performance bottleneck. The newly proposed sea surface temperature deep learning prediction method based on the space-time multidimensional influence solves the performance bottleneck problem by combining the space-time multidimensional influence and deep learning technologies such as RNN, CNN and MLP, and further improves the accuracy of sea surface temperature prediction.
2. According to the method, the spatial influence on the target observation point is quantified by using the sea surface temperature data of the neighbor observation point of the target observation point. The method carries out data completion processing on the spatial information by using a neighbor data averaging method.
3. A high-precision sea surface temperature prediction model is established by combining the space-time multidimensional influence and a deep learning technology. The model can overcome the performance bottleneck of the sea surface temperature deep learning prediction model based on the time influence, and can be widely applied to the sea surface temperature prediction of different sea areas and different scales.
Drawings
Fig. 1 is a general flowchart of the sea surface temperature deep learning prediction method based on the spatio-temporal multidimensional influence of the present invention.
Fig. 2 is a schematic diagram of the principle of quantifying the spatial influence on the target observation point by using the sea surface temperature data of the neighbor observation points of the target observation point, which is described in step a in fig. 1.
Fig. 3 is a schematic diagram of the data completion process performed on the data set by using the neighbor data averaging method in step B in fig. 1.
Fig. 4 is a schematic model principle diagram of the convolution gating cyclic unit multi-layered perceptron CGMP described in step C of fig. 1.
Fig. 5 lists the performance comparison of CGMP, NGMP, CLMP and NLMP on the bohai sea surface temperature daily mean complete data set.
Fig. 6 lists the performance comparison of CGMP, NGMP, CLMP and NLMP on the bohai sea surface temperature cycle mean complete data set.
FIG. 7 lists the comparison of CGMP, FC-LSTM and GED performance on the Bohai sea surface temperature complete data set.
FIG. 8 lists the performance comparison of CGMP, FC-LSTM and GED on the Bohai sea surface temperature ocean data set.
FIG. 9 lists the performance of CGMP, FC-LSTM and GED on a south sea surface temperature integrity data set.
FIG. 10 lists the performance of CGMP, FC-LSTM and GED on the south sea surface temperature ocean data set versus the time of day.
Detailed Description
Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
The conception of the invention is as follows: firstly, the space influence on a target sea surface temperature observation point is quantified, and a multi-dimensional space-time sea surface temperature data set is constructed on a target sea area. And then carrying out data preprocessing and data completion processing on the data set. And finally, establishing a sea surface temperature deep learning prediction model based on the space-time multi-dimensional influence by combining deep learning technologies such as RNN (LSTM or GRU), CNN and MLP.
In this embodiment, the experiments are all completed on a machine learning platform of Shanghai university, which has two Intel Xeon Gold 6130 CPUs, 192GB RAMs, four Nvidia Tesla V100 GPUs, and eighteen Nvidia Tesla P100 GPUs. In the aspect of establishing the model, the CGMP and the comparison model are realized based on an open source deep learning tool PyTorch. In terms of experimental setup, Adam was chosen as the optimization algorithm for the experiment, the batch size was set to 256, and the number of iterations was set to 1000. In terms of performance evaluation, the experiment evaluated the sea surface temperature prediction performance of the model by calculating Mean Square Error (MSE) and Mean Absolute Error (MAE). The smaller the mean square error or mean absolute error obtained by the model is, the better the prediction performance of the model is represented. The detailed definitions of MSE and MAE are as follows:
Figure BDA0002511078530000051
Figure BDA0002511078530000052
wherein Y represents a predicted future SST sequence, Y 'represents a corresponding observed SST sequence, Y represents a predicted future SST, Y' represents a corresponding true value, and l represents the size of the prediction scale.
The sea surface temperature deep learning prediction method based on the space-time multidimensional influence disclosed by the invention is shown in figures 1-4 and comprises the following steps:
A. quantification of spatial influence: and quantifying the spatial influence on the target observation point by using the sea surface temperature data of the neighbor observation point of the target observation point, and constructing a multi-dimensional space-time sea surface temperature data set on the target sea area. The method comprises the following specific steps:
a1, the sea surface can be divided into individual grids according to longitude, latitude and resolution information. One grid is a sea surface temperature observation point. The surface temperature prediction task may be expressed simply as using historical surface temperature data to predict future surface temperature data at the target surface temperature observation. In order to quantify the spatial influence of the target sea surface temperature observation point quantitatively, the spatial influence of the target observation point is assumed to be totally from eight adjacent sea surface temperature observation points, called neighbor points, so that a multi-dimensional space-time sea surface temperature data set can be constructed on the Bohai sea and the south sea area, namely a Bohai sea surface temperature (daily average/weekly average/monthly average) complete data set, a Bohai sea surface temperature (daily average/weekly average/monthly average) marine data set, a south sea surface temperature (daily average/weekly average/monthly average) complete data set and a south sea surface temperature (daily average/weekly average/monthly average) marine data set. The complete data set contains data with missing spatial information, and the ocean data set excludes data with missing spatial information.
B. And (3) data completion processing: and performing data completion processing on the data set by using a neighbor data averaging method. The method comprises the following specific steps:
and B1, performing data completion processing on the complete data set by using a neighbor data averaging method. For some target observation points close to the coast, some of their neighbors may be located in the land area, and therefore there is no sea surface temperature observation data at these neighbors, which are called land sites. Therefore, in order to unify and standardize the input of the prediction model, this chapter performs data completion processing on the land points located in the land area. For a land site, the complemented sea surface temperature data is the average value of the real sea surface temperature observation data of the eight neighbor points, namely, a neighbor data averaging method. The data completion method has the advantages of strong practicability and suitability for all different deficiency conditions.
C. Establishing a prediction model: and (3) establishing a sea surface temperature deep learning prediction model based on the time-space multi-dimensional influence by combining deep learning technologies such as GRU, CNN and MLP, namely the multilayer perceptron CGMP of the convolution gating circulation unit. The method comprises the following specific steps:
c1, CGMP is an end-to-end sea surface temperature prediction model, the input of which is a historical SST sequence and the output of which is a predicted future SST sequence. Wherein the historical SST sequence comprises neighbor information and historical information of the observation point of the target sea surface temperature. The CGMP firstly processes the neighbor information of a target observation point on the spatial dimension through a convolution layer to obtain an intermediate vector, namely an empty SST sequence. The CGMP then processes the historical information of the spatio-temporal SST sequence in the time dimension through a GRU, resulting in a hidden state vector. Finally, the multi-layered perceptron maps the hidden state vector onto the prediction result, i.e., the future SST sequence. Convolutional layers are good at processing grid-like structure data and GRUs are good at processing sequence data, so the combinatorial collocation of convolutional layers and GRUs can sufficiently and effectively mine neighbor and historical information in the spatio-temporal dimension. Wherein:
c11, historical SST sequence X is a tensor with one dimension k × 3 × 3, and includes neighbor information (matrix with dimension 3 × 3) and historical information of k time units of the observation point of the target sea surface temperature. Since convolutional neural networks are good at handling grid-like structured data, CGMP first processes the neighbor information of the target observation point in the spatial dimension through one convolutional layer. The depth of the convolutional layer is 1, the size of the convolutional kernel K is 3 × 3, the step size is 1, and the padding is 0. After X is subjected to convolution operation, a space-time SST sequence S with the dimensionality of k multiplied by 1 is obtained, and the operation process is defined as follows:
S＝(s1，s2，...，sk)
si＝Xi★K+bs，i∈{1，2，...，k}
Figure BDA0002511078530000061
wherein s isiRepresenting the processed spatio-temporal history information, ★ representing a two-dimensional convolution operation, wk,1–wk,9Representing the weight of the convolution kernel K, bsIndicating the corresponding bias.
C12, the spatiotemporal SST sequence S contains historical information of k time units of the target sea surface temperature observation point. Since GRUs are good at processing sequence data, CGMP then processes the historical information of the target observation point in the time dimension through one GRU. The GRU receives two inputs at each time step: one is spatio-temporal history information S from a spatio-temporal SST sequence SiThe other is the hidden state vector H from the previous time step(i-1)Or an initial hidden state vector H0. After the history information is processed, the GRU generates a new hidden state vector H at each time stepi. So GRUs generate k hidden state vectors in total, i.e., H1–Hk. The process is defined as follows:
ri＝σ(Wrssi+WrHH(i-1)+br)
zi＝σ(Wzssi+WzHH(i-1)+bz)
ni＝tanh(Wnssi+bns+ri*(WnHH(i-1)+bnH))
Hi＝(1-zi)*ni+zi*H(i-1)
wherein HiRepresenting the hidden state vector, s, of the GRU at time step iiRepresenting spatio-temporal history information at time step i, H(i-1)Hidden state at time step i-1The state vector either represents the initial hidden state vector at time step 0, ri、ziAnd niRespectively representing the reset, update and new gates of the GRU. σ denotes a sigmoid function, and σ denotes a Hadamard product. W represents the corresponding weight and b represents the corresponding bias.
Finally, CGMP uses a multi-layer perceptron to hide the state vector HkMapped onto the prediction, i.e. the future SST sequence Y.
The mapping process is defined as follows:
Y＝fMLP(WYHk+bY)
wherein, WYRepresenting the corresponding weight, bYIndicating the corresponding bias.
Referring to fig. 5 and fig. 6, the performance comparison of CGMP, NGMP, CLMP and NLMP on the bohai sea surface temperature daily mean and weekly mean complete data sets is shown. In the experiment, the convolution layer and the full link layer are compared in spatial information extraction performance, and the GRU and the LSTM are compared in temporal information extraction performance. Therefore, in addition to CGMP, there are three other corresponding comparison methods for experiments, namely Neighbor Full-Connected gated round-robin unit Multilayer Perceptron (NGMP), Convolutional long-short term memory Multilayer Perceptron (CLMP), and Neighbor Full-Connected long-short term memory Multilayer Perceptron (NLMP). Meanwhile, the influence of the depth of the multilayer perceptron on the prediction performance is also studied through experiments. Comparing the experimental results of the CGMP and the NGMP, it can be seen that under the same depth of the multilayer perceptron, both the MSE and the MAE of the CGMP are less than those of the NGMP, that is to say, the prediction performance of the CGMP on the Bohai sea surface temperature daily average complete data set is superior to that of the NGMP. This also indirectly demonstrates that convolutional layers have better ability to capture spatial influences in the spatial dimension than fully connected layers. Comparing the CGMP and the CLMP, the experimental result shows that under the same depth of the multilayer perceptron, both the MSE and the MAE of the CGMP are smaller than those of the CLMP, namely the prediction performance of the CGMP on a complete data set of the Bohai sea surface temperature daily average is superior to that of the CLMP. This also indirectly demonstrates that the GRU's ability to process historical information in the time dimension is superior to LSTM. By comparing the four methods together, the CGMP using the convolution layer and the GRU at the same time shows the optimal prediction performance on the depth of the multilayer sensor of 3. In summary, convolutional layers may capture spatial impact in the spatial dimension more efficiently than fully-connected layers, and GRUs may process historical information in the temporal dimension more efficiently than LSTM.
Referring to fig. 7 and 8, the CGMP, FC-LSTM and GED performance comparison on the bohai sea surface temperature integrity and marine data set is shown. As is evident from the table, the prediction performance of CGMP compared to FC-LSTM and GED remains optimal in all daily, weekly and monthly mean sea surface temperature predictions
Referring to FIGS. 9 and 10, a comparison of CGMP, FC-LSTM and GED performance on south sea ocean surface temperature integer and ocean datasets is shown. As is evident from the table, the prediction performance of CGMP compared to FC-LSTM and GED remains optimal in all daily, weekly and monthly mean sea surface temperature predictions.
In summary, the sea surface temperature deep learning prediction method based on the time-space multidimensional influence establishes a high-precision sea surface temperature prediction model by combining the time-space multidimensional influence and the deep learning technology. The model can overcome the performance bottleneck of the sea surface temperature deep learning prediction model based on the time influence, and can be widely applied to the sea surface temperature prediction of different sea areas and different scales.
The accompanying drawings and the detailed description are included to provide a further understanding of the invention. The method of the present invention is not limited to the examples described in the specific embodiments, and other embodiments derived from the method and idea of the present invention by those skilled in the art also belong to the technical innovation scope of the present invention. The description is not to be construed as limiting the invention.

Claims (7)

1. A sea surface temperature deep learning prediction method based on space-time multidimensional influence is characterized by comprising the following steps:
A. quantification of spatial influence: quantifying the space influence on the target observation point by using the sea surface temperature data of the neighbor observation point of the target observation point, and constructing a multi-dimensional space-time sea surface temperature data set on a target sea area;
B. and (3) data completion processing: performing data completion processing on the data set by using a neighbor data averaging method;
C. establishing a prediction model: a sea surface temperature deep learning prediction model based on space-time multidimensional influence is established by combining deep learning technologies such as convolution operation, a gated circulation unit, a multilayer perceptron and the like, namely a convolution gated circulation unit multilayer perceptron is called CGMP for short.
2. The sea surface temperature deep learning prediction method based on the spatiotemporal multidimensional influence is characterized by comprising the following steps of: in the step A, the sea surface is divided into grids according to longitude, latitude and resolution information, one grid is a sea surface temperature observation point, eight adjacent sea surface temperature observation points of the target observation point are called neighbor points, and a multi-dimensional space-time sea surface temperature data set of the nine points can be constructed in the target sea area.
3. The sea surface temperature deep learning prediction method based on the spatiotemporal multidimensional influence is characterized by comprising the following steps of: in the step B, the data of the missing point in the nine points is completed, the average value of the real sea surface temperature observation data of 8 neighbor points around the missing point is used as the missing point, and if the neighbor point of the missing point also has data missing, the point is excluded during calculation.
4. The sea surface temperature deep learning prediction method based on the space-time multidimensional influence is characterized by comprising the following steps of: in step C, the CGMP is an end-to-end sea surface temperature prediction model, the input of the model is a historical SST sequence, and the output of the model is a predicted future SST sequence, wherein the historical SST sequence includes historical sea surface temperature data of a target sea surface temperature observation point and neighbor points, the CGMP processes the neighbor information of the target observation point in a spatial dimension through a convolution layer to obtain an intermediate vector, i.e., a space SST sequence, the CGMP processes the historical information of the space SST sequence in a temporal dimension through a gated cycle unit to obtain a hidden state vector, and finally, the hidden state vector is mapped to a prediction result, i.e., the future SST sequence, by using a multi-layer sensor.
5. The sea surface temperature deep learning prediction method based on the space-time multidimensional influence is characterized in that: the historical SST sequence X is a tensor with one dimension of K multiplied by 3, and comprises neighbor information of K time units of a target sea surface temperature observation point and historical data information, the CGMP firstly processes the neighbor information of the target observation point on the spatial dimension through a convolution layer, the depth of the convolution layer is 1, the size of a convolution kernel K is 3 multiplied by 3, the step length is 1, and the filling is 0. After X is subjected to convolution operation, a space-time SST sequence S with the dimensionality of k multiplied by 1 is obtained, and the operation process is defined as follows:
S＝(S1，S2，...，Sk)
si＝Xi★K+bs，∈{1，2，...，k}
Figure FDA0002511078520000021
wherein s isiRepresenting the processed spatio-temporal history information, ★ representing a two-dimensional convolution operation, wk,1–wk,9Representing the weight of the convolution kernel K, bsIndicating the corresponding bias.
6. The sea surface temperature deep learning prediction method based on the spatiotemporal multidimensional influence is characterized by comprising the following steps of: a gated round robin unit, GRU, receives two inputs at each time step: one is spatio-temporal history information S from a spatio-temporal SST sequence SiThe other is the hidden state vector H from the previous time step(i-1)Or an initial hidden state vector H0History information is passed throughAfter processing, the GRU generates a new hidden state vector H at each time stepiThe GRUs generate a total of k hidden state vectors, i.e., H1–HkThe process is defined as follows:
ri＝σ(Wrssi+WrHH(i-1)+br)
zi＝σ(Wzssi+WzHH(i-1)+bz)
ni＝tanh(Wnssi+bns+ri*(WnHH(i-1)+bnH))
Hi＝(1-zi)*ni+zi*H(i-1)
wherein HiRepresenting the hidden state vector, s, of the GRU at time step iiRepresenting spatio-temporal history information at time step i, H(i-1)Representing the hidden state vector at time step i-1 or representing the initial hidden state vector at time step 0, ri、ziAnd niRespectively, a reset gate, an update gate and a new gate of the GRU, a represents a sigmoid function, a represents a Hadamard product, W represents a corresponding weight, and b represents a corresponding offset.
7. The sea surface temperature deep learning prediction method based on the space-time multidimensional influence is characterized by comprising the following steps: CGMP (China general packet radio service) enables hidden state vector H to pass through a multi-layer perceptronkMapping onto the prediction, i.e. future SST sequence Y, the mapping process is defined as follows:
Y＝fMLP(WYHk+bY)
wherein, WYRepresenting the corresponding weight, bYIndicating the corresponding bias.
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