CN114862035A - Combined bay water temperature prediction method based on transfer learning - Google Patents
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Abstract
The invention discloses a combined gulf water temperature prediction method based on transfer learning, which utilizes a B gulf with more historical observation data to construct an optimal source LSTM neural network model and then is based on the optimal source LSTM neural network model, the optimal migration LSTM neural network model of the A bay with less historical observation data is constructed through migration learning, then the pure difference regression prediction model and the mixed difference regression prediction model are combined, the three models are combined into the migration water temperature online prediction model of the A bay or the source water temperature online prediction model of the B bay in a dynamic weighting mode, not only can the source water temperature online prediction model be used for predicting the water temperature of the bay with more historical observation data, but also the migration water temperature online prediction model can be used for predicting the water temperature of the bay with less historical observation data, the prediction precision is high, the result is reliable, and the problem that the water temperature prediction precision is not high due to insufficient data volume in the prior art is solved.
Description
Technical Field
The invention relates to the technical field of water temperature prediction, in particular to a combined gulf water temperature prediction method based on transfer learning.
Background
Water temperature is one of the key factors influencing the growth condition and growth quality of fishes in aquaculture, and sudden change of water temperature can influence the pH value, dissolved oxygen, ammonia nitrogen and the like in a water body and influence the suitable living environment of the fishes.
The water temperature is easily influenced by multiple factors such as air temperature, aquatic life activities and the like in a cross mode, and has the characteristics of instability, nonlinearity and the like, so that the defects of low prediction precision, poor generalization performance and the like exist in a single prediction method, and the actual requirements of people on fishery production management are difficult to meet.
The existing water temperature prediction methods mainly comprise a traditional mathematical statistics method and an artificial intelligence method, the mathematical method based on the water temperature mechanism analyzes the mechanism and factors influencing the water temperature change, researches the influence of different water layer conditions, space-time distribution, flow velocity, flow and the like on the water temperature, and constructs a water temperature prediction model by using a hydrological meteorological theory, a two-dimensional model, a three-dimensional model and the like, and although the methods can effectively predict the water temperatures of a reservoir and a water tank, the prediction model is complex, has more parameters and is difficult to obtain; a non-mechanism method based on data mining generally constructs a mathematical model through a large amount of data, obtains the relation between water temperature and relevant influence factors, captures the change rule of the water temperature, and mostly uses a machine learning algorithm aiming at the mathematical model constructed by a large amount of samples, but a single machine learning algorithm can only predict the change trend of the water temperature and cannot effectively predict the water temperature mutation point. Machine learning often requires a large amount of training data to better develop its effects. However, in many practical scenarios, the target domain does not have enough training data, and the constructed general model is difficult to predict accurately for a specific scenario. At present, a water temperature prediction method capable of effectively predicting the water temperature change trend and the water temperature mutation point is still lacked, and meanwhile, the contradiction between machine learning and less training data and the contradiction between a general model and personalized requirements are faced.
Disclosure of Invention
The invention aims to provide a combined gulf water temperature prediction method based on transfer learning, which is high in precision and reliable in result, can better predict the water temperature value of 7 days in the future of a gulf, helps a target model to acquire knowledge learned by a source model through the transfer learning technology so as to improve the prediction performance of the model, solves the problem of low water temperature prediction precision caused by insufficient data volume in the prior art, and can provide data, information and service guarantee for marine fishery production, scientific research and management.
In order to achieve the purpose, the invention adopts the following technical scheme:
a combined bay water temperature prediction method based on transfer learning comprises the following steps:
s1, collecting water temperature data and meteorological data of an A bay and a B bay respectively, and preprocessing the data respectively, wherein the historical observation data of the B bay is more than that of the A bay;
s2, respectively constructing pure differential regression prediction models of the A bay and the B bay based on the respective forecast air temperature differential values and the actual measured water temperature values; respectively constructing a mixed differential regression prediction model of the A bay and the B bay based on the respective forecast air temperature differential value, the actual measurement water temperature differential value and the actual measurement water temperature value;
s3, aiming at the Bay B, constructing an LSTM neural network model, and selecting an LSTM neural network prediction model of an optimal source through training and testing; combining the optimal source LSTM neural network prediction model, the pure differential regression prediction model of the B bay and the mixed differential regression prediction model into a source water temperature online prediction model based on a dynamic weighting mode; predicting the water temperature of the bay B according to the source water temperature online prediction model;
s4, aiming at the gulf A, selecting the optimal source LSTM neural network prediction model of the gulf B, and selecting the optimal migration LSTM neural network prediction model through migration learning and testing; combining the optimal migration LSTM neural network prediction model, the pure differential regression prediction model of the A bay and the mixed differential regression prediction model into a migration water temperature online prediction model based on a dynamic weighting mode; and predicting the water temperature of the A bay according to the online migration water temperature prediction model.
Preferably, the water temperature data in step S1 includes daily maximum water temperature, daily minimum water temperature, and daily average water temperature; the meteorological data comprises meteorological observation data and meteorological forecast data; the meteorological observation data comprise air temperature statistics, air pressure statistics, relative humidity statistics, wind speed statistics, wind direction statistics and precipitation, and the time resolution is 1 day; the weather forecast data includes future 7-day weather forecast data updated every day, and the future 7-day weather forecast data includes day highest temperature and day lowest temperature of the future 7 days.
Preferably, the method for constructing the pure differential regression prediction model in step S2 is as follows:
setting the predicted temperature values of the weather forecast released on the k day as
Measured value of water temperature is x k If the current time is t, the predicted value of the water temperature of the day 1 is
The calculation formula of (2) is as follows:
predicted value of water temperature of nth (n is more than or equal to 2 and less than or equal to 7) day
The calculation formula of (2) is as follows:
wherein x is t-1 The measured value of the water temperature on the t-1 day is measured;
the air temperature forecast value of the day is acquired for the t +1-i days;
obtaining the air temperature forecast value of the day for the t-i days;
an air temperature forecast for the nth day in the future acquired for the t-th day;
the temperature forecast value of the day is acquired on the t day; w is a p1 ,w p2 ,w p3 And
and selecting the weight by constructing a linear regression equation.
Preferably, the construction method of the mixed difference regression prediction model in step S2 is:
setting the predicted temperature values of the weather forecast released on the k day as
Measured value of water temperature is x k If the current time is t, the predicted value of the water temperature of the day 1 is
The calculation formula of (2) is as follows:
predicted value of water temperature of nth (n is more than or equal to 2 and less than or equal to 7) day
The calculation formula of (2) is as follows:
wherein x is t-1 The measured value of the water temperature on the t-1 day is measured; x is the number of t-2 The measured value of the water temperature on the t-2 th day;
obtaining the temperature forecast value of the day for the t day;
obtaining the air temperature forecast value of the day for the t-1 day;
obtaining an air temperature forecast value for the nth day in the future for the t day; w is a m1 ,w m2 And
and selecting the weight by constructing a linear regression equation.
Preferably, w is chosen by means of a linear regression equation p1 ,w p2 ,w p3 ,
w m1 ,w m2 And
the concrete mode is as follows:
the predicted temperature values of the weather forecast released on the kth day are respectively set as
Measured value of water temperature is x k When the current time is t, according to the historical water temperature and meteorological forecast data of the bay, a linear regression equation is established according to the following formulas, and w is calculated p1 ,w p2 ,w p3 ,
w m1 ,w m2 And
wherein x is t+1-n The measured value of the water temperature on the t +1-n days.
Preferably, the specific process of step S3 is:
s21, adopting water temperature data and meteorological data of the Bay B, carrying out normalization processing on the water temperature data, and dividing the water temperature data and meteorological observation data into a training set and a test set according to the proportion of 0.8: 0.2;
s22, combining meteorological observation data and water temperature data of a plurality of past days to obtain combined data based on a training set part of the water temperature data, and constructing and obtaining a source LSTM neural network prediction model by using the combined data as an input factor and the water temperature data of 7 days in the future as an output factor, wherein the water temperature data of the plurality of past days refers to water temperature data with different time lengths; the combined data refers to the combination of water temperature data serving as fixed input parameters and other meteorological observation elements in a traversing mode;
s23, inputting the combined data into a trained network based on the test set part of the water temperature data, and obtaining a daily frequency water temperature prediction result of 7 days in the future through inverse normalization;
s24, comparing the predicted result with the measured data, and giving an early warning of the water temperature according to the accuracy rate and the root mean squareError RMSE, mean absolute error MAE and coefficient of determination R 2 Counting the test result of the model as a test standard, and selecting an optimal source LSTM neural network prediction model;
s25, setting the initial weight ratio of the optimal source LSTM neural network prediction model and the pure differential regression prediction model and the mixed differential regression prediction model of the B bay to be 1:1:1, taking root mean square error RMSE as a statistical index, carrying out statistics on the prediction accuracy of the three models every day, enabling the weight to be larger when the accuracy is larger, and dynamically updating the weight of the combined model every day.
Preferably, the specific process of step S4 is:
s31, obtaining the optimal source LSTM neural network prediction model of the Bay B constructed in the steps S21 to S24;
s32, adopting the water temperature data and the meteorological data of the A bay to carry out normalization processing on the data of the A bay, and dividing the data into a training set and a testing set according to the proportion of 0.5: 0.5;
s33, freezing a partial network layer of the optimal source LSTM neural network prediction model, and finely adjusting the network based on the training set part of the A bay so as to realize transfer learning of the optimal source LSTM neural network prediction model;
s34, obtaining a daily frequency water temperature prediction result of 7 days in the future by reverse normalization based on the test set part of the gulf A;
s35, comparing the predicted result with the measured data, and determining the accuracy, Root Mean Square Error (RMSE), average absolute error (MAE) and coefficient R of the water temperature early warning level 2 As a test standard, counting the test result of the model, and selecting an optimal migration LSTM neural network prediction model;
s36, setting the initial weight ratio of the optimal migration LSTM neural network prediction model and the pure differential regression prediction model and the mixed differential regression prediction model of the A gulf as 1:1:1, taking root mean square error RMSE as a statistical index, carrying out statistics on the prediction accuracy of the three models every day, enabling the weight to be larger when the accuracy is larger, and dynamically updating the weight of the combined model every day.
Preferably, the calculation formula of the normalization processing in step S21 and step S32 is:
wherein X is the original measured data, X normal Is normalized data, X mean And X std Mean and variance, respectively, for bay B.
Preferably, the denormalization formula in step S23 and step 34 is:
X final ＝X pre *X std +X mean
wherein, X pre Is the original predicted value, X final Is the final predicted value of inverse normalization, X mean And X std Mean and variance, respectively, for bay B;
the calculation formula of the accuracy of the water temperature early warning grade in the steps S24 and S35 is as follows:
wherein, the division of the water temperature grades refers to the suitable growth temperature of the large yellow croaker, and the total number of the grades is 7;
and testing all the trained models by taking the root mean square error RMSE as the most important index, and selecting the model with the highest precision in the verification set and the test set as the optimal LSTM neural network prediction model or the migration LSTM neural network prediction model.
After adopting the technical scheme, compared with the background technology, the invention has the following advantages: in order to predict the change trend of water temperature, an optimal source LSTM neural network model is constructed by using a B bay with more historical observation data, and an optimal migration LSTM neural network model of an A bay with less historical observation data is constructed by migration learning based on the optimal source LSTM neural network model; in order to accurately forecast the mutation point of the water temperature, the actually measured water temperature and the forecast air temperature data are differentiated, a pure differential regression prediction model is constructed based on the forecast air temperature difference value and the actually measured water temperature value, a mixed differential regression prediction model is constructed based on the forecast air temperature difference value, the actually measured water temperature difference value and the actually measured water temperature value, and the optimal source LSTM neural network model or the optimal migration LSTM neural network model is combined with the pure differential regression prediction model and the mixed differential regression prediction model; the three models are combined into a migration water temperature online prediction model of the gulf A or a source water temperature online prediction model of the gulf B in a dynamic weighting mode, water temperature prediction can be carried out on the gulf with more historical observation data through the source water temperature online prediction model, water temperature prediction can be carried out on the gulf with less historical observation data through the migration water temperature online prediction model, the prediction precision is high, the result is reliable, water temperature values of the gulf in the future 7 days can be well predicted, the target model is helped to obtain knowledge learned by the source model through a migration learning technology to improve the prediction performance of the model, the problem that the water temperature prediction precision is low due to insufficient data volume in the prior art is solved, and data, information and service guarantee can be provided for marine fishery production, scientific research and management.
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FIG. 1 is a flow chart of the present invention;
FIG. 2 is a block flow diagram of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1 to 2, a combined gulf water temperature prediction method based on transfer learning includes the following steps:
s1, collecting water temperature data and meteorological data of an A bay and a B bay respectively, and preprocessing the data respectively, wherein the historical observation data of the B bay is more than that of the A bay;
the water temperature data in the step S1 includes daily maximum water temperature, daily minimum water temperature, and daily average water temperature; the meteorological data comprises meteorological observation data and meteorological forecast data; the meteorological observation data comprise air temperature statistics, air pressure statistics, relative humidity statistics, wind speed statistics, wind direction statistics and precipitation, and the time resolution is 1 day; the weather forecast data comprises future 7-day weather forecast data updated every day, and the future 7-day weather forecast data comprises highest day temperature and lowest day temperature of 7 days in the future;
s2, respectively constructing pure differential regression prediction models of the A bay and the B bay based on the respective forecast air temperature differential values and the actual measurement water temperature values; respectively constructing a mixed differential regression prediction model of the A bay and the B bay based on the respective forecast air temperature difference value, the actual measurement water temperature difference value and the actual measurement water temperature value;
the method for constructing the pure differential regression prediction model in the step S2 includes:
setting the predicted temperature values of the weather forecast released on the k day as
Measured value of water temperature is x k If the current time is t, the predicted value of the water temperature of the day 1 is
The calculation formula of (2) is as follows:
predicted value of water temperature of nth (n is more than or equal to 2 and less than or equal to 7) day
The calculation formula of (2) is as follows:
wherein x is t-1 The measured value of the water temperature on the t-1 day is measured;
the air temperature forecast value of the day is acquired for the t +1-i days;
obtaining the air temperature forecast value of the day for the t-i days;
an air temperature forecast for the nth day in the future acquired for the t-th day;
obtaining the temperature forecast value of the day for the t day; w is a p1 ,w p2 ,w p3 And
selecting the weight by constructing a linear regression equation;
the construction method of the mixed difference regression prediction model in the step S2 comprises the following steps:
setting the predicted temperature values of the weather forecast released on the k day as
Measured value of water temperature is x k If the current time is t, the predicted value of the water temperature of the day 1 is
The calculation formula of (2) is as follows:
predicted value of water temperature of nth (n is more than or equal to 2 and less than or equal to 7) day
The calculation formula of (2) is as follows:
wherein x is t-1 Measured value of water temperature on t-1 day；x t-2 The measured value of the water temperature on the t-2 th day;
obtaining the temperature forecast value of the day for the t day;
obtaining the air temperature forecast value of the day for the t-1 day;
obtaining an air temperature forecast value for the nth day in the future for the t day; w is a m1 ,w m2 And
selecting the weight by constructing a linear regression equation;
selecting w by means of a linear regression equation p1 ,w p2 ,w p3 ,
w m1 ,w m2 And
the specific mode is as follows:
setting the predicted temperature values of the weather forecast released on the k day as
Measured value of water temperature is x k When the current time is t, according to the historical water temperature and meteorological forecast data of the bay, a linear regression equation is established according to the following formulas, and w is calculated p1 ,w p2 ,w p3 ,
w m1 ,w m2 And
wherein x is t+1-n The measured value of the water temperature on the t +1-n days is obtained;
s3, aiming at the Bay B, constructing an LSTM neural network model, and selecting an LSTM neural network prediction model of an optimal source through training and testing; combining the optimal source LSTM neural network prediction model, the pure differential regression prediction model of the Bay B and the mixed differential regression prediction model into a source water temperature online prediction model based on a dynamic weighting mode; predicting the water temperature of the bay B according to the source water temperature online prediction model;
the specific process of step S3 is:
s21, adopting water temperature data and meteorological data of the Bay B, carrying out normalization processing on the water temperature data, and dividing the water temperature data and meteorological observation data into a training set and a test set according to the proportion of 0.8: 0.2;
s22, combining meteorological observation data and water temperature data of a plurality of past days to obtain combined data based on a training set part of the water temperature data, and constructing and obtaining a source LSTM neural network prediction model by using the combined data as an input factor and the water temperature data of 7 days in the future as an output factor, wherein the water temperature data of the plurality of past days refers to water temperature data with different time lengths; the combined data is combined with other meteorological observation elements in a traversing mode by taking water temperature data as a fixed input parameter;
s23, inputting the combined data into a trained network based on the test set part of the water temperature data, and obtaining a daily frequency water temperature prediction result of 7 days in the future through inverse normalization;
s24, comparing the predicted result with the measured data, and determining the accuracy, Root Mean Square Error (RMSE), average absolute error (MAE) and coefficient R of the water temperature early warning level 2 Counting the test result of the model as a test standard, and selecting an optimal source LSTM neural network prediction model;
s25, setting the initial weight ratio of the optimal source LSTM neural network prediction model and the pure differential regression prediction model and the mixed differential regression prediction model of the B bay to be 1:1:1, taking root mean square error RMSE as a statistical index, carrying out statistics on the prediction accuracy of the three models every day, enabling the weight to be larger when the accuracy is larger, and dynamically updating the weight of the combined model every day. If the prediction accuracy of the LSTM neural network prediction model is the highest on the next day, the weight of the LSTM neural network prediction model is 1/2 when the weight is (1+1)/(3+1), 1/4 when the weight of the pure differential regression prediction model and the weight of the mixed differential regression prediction model are both 1/(3+1), and the prediction results of the combination model on the next day on the water temperature in the seven days in the future are obtained by multiplying the model prediction values by the corresponding weights and adding the weights; if the prediction accuracy of the mixed differential regression prediction model on the third day is the highest, the weight of the mixed differential regression prediction model is 2/5 (1+1)/(3+2), the weight of the pure differential regression prediction model is 1/5 (1/(3 + 2)), the weight of the LSTM neural network prediction model is 2/5 (2/(3 + 2)), the predicted value of the model is multiplied by the corresponding weights and added, and the prediction result of the combined model on the water temperature on the seventh day in the future on the third day is obtained, and the rest is done;
s4, aiming at the gulf A, selecting the optimal source LSTM neural network prediction model of the gulf B, and selecting the optimal migration LSTM neural network prediction model through migration learning and testing; combining the optimal migration LSTM neural network prediction model, the pure differential regression prediction model of the A bay and the mixed differential regression prediction model into a migration water temperature online prediction model based on a dynamic weighting mode; predicting the water temperature of the gulf A according to the migration water temperature online prediction model;
the specific process of step S4 is:
s31, obtaining the optimal source LSTM neural network prediction model of the Bay B constructed in the steps S21 to S24;
s32, adopting the water temperature data and the meteorological data of the A bay to carry out normalization processing on the data of the A bay, and dividing the data into a training set and a testing set according to the proportion of 0.5: 0.5;
s33, freezing partial network layers of the LSTM neural network prediction model of the optimal source (the LSTM neural network prediction model has two network layers, and the mode of freezing the first network layer is adopted), and finely tuning the network based on the training set part of the A bay so as to realize transfer learning of the LSTM neural network prediction model of the optimal source;
s34, obtaining a daily frequency water temperature prediction result of 7 days in the future by reverse normalization based on the test set part of the gulf A;
s35, comparing the predicted result with the measured data, and determining the accuracy, Root Mean Square Error (RMSE), average absolute error (MAE) and coefficient R of the water temperature early warning level 2 Counting the test result of the model as a test standard, and selecting an optimal migration LSTM neural network prediction model;
s36, setting the initial weight ratio of the optimal migration LSTM neural network prediction model and the pure differential regression prediction model and the mixed differential regression prediction model of the A gulf as 1:1:1, taking root mean square error RMSE as a statistical index, carrying out statistics on the prediction accuracy of the three models every day, enabling the weight to be larger when the accuracy is larger, and dynamically updating the weight of the combined model every day. If the prediction accuracy of the LSTM neural network prediction model is the highest on the next day, the weight of the LSTM neural network prediction model is 1/2 when the weight is (1+1)/(3+1), 1/4 when the weight of the pure differential regression prediction model and the weight of the mixed differential regression prediction model are both 1/(3+1), and the prediction results of the combination model on the next day on the water temperature in the seven days in the future are obtained by multiplying the model prediction values by the corresponding weights and adding the weights; if the prediction accuracy of the mixed differential regression prediction model on the third day is the highest, the weight of the mixed differential regression prediction model is 2/5 (1+1)/(3+2), the weight of the pure differential regression prediction model is 1/5 (1/(3 + 2)), the weight of the LSTM neural network prediction model is 2/5 (2/(3 + 2)), the predicted value of the model is multiplied by the corresponding weights and added, and the prediction result of the combined model on the water temperature on the seventh day in the future on the third day is obtained, and the rest is done;
the calculation formula of the normalization processing in step S21 and step S32 is:
wherein X is the original measured data, X normal Is normalized data, X mean And X std Mean and variance, respectively, for bay B;
the inverse normalization formula in step S23 and step S34 is:
X final ＝X pre *X std +X mean
wherein, X pre Is the original predicted value, X final Is the final predicted value of inverse normalization, X mean And X std Mean and variance, respectively, for bay B;
the calculation formula of the water temperature early warning grade accuracy in the steps S24 and S35 is as follows:
wherein, the water temperature grades are divided according to the suitable growth temperature of the large yellow croaker, and 7 grades are provided in total, which is shown in table 1;
TABLE 1 Water temperature ratings
The results of the grade prediction are illustrated in table 2.
TABLE 2 grade Warning results Explanation
Note: red low: red low temperature; orange low: orange low temperature; low yellow: yellow and low temperature; yellow height: yellow high temperature;
orange juice: orange high temperature; red height: red high temperature
And testing all the trained models by taking the root mean square error RMSE as the most important index, and selecting the model with the highest precision in the verification set and the test set as the optimal LSTM neural network prediction model or the migration LSTM neural network prediction model.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (9)
1. A combined bay water temperature prediction method based on transfer learning is characterized by comprising the following steps:
s1, collecting water temperature data and meteorological data of an A bay and a B bay respectively, and preprocessing the data respectively, wherein the historical observation data of the B bay is more than that of the A bay;
s2, respectively constructing pure differential regression prediction models of the A bay and the B bay based on the respective forecast air temperature differential values and the actual measurement water temperature values; respectively constructing a mixed differential regression prediction model of the A bay and the B bay based on the respective forecast air temperature differential value, the actual measurement water temperature differential value and the actual measurement water temperature value;
s3, aiming at the Bay B, constructing an LSTM neural network model, and selecting an LSTM neural network prediction model of an optimal source through training and testing; combining the optimal source LSTM neural network prediction model, the pure differential regression prediction model of the Bay B and the mixed differential regression prediction model into a source water temperature online prediction model based on a dynamic weighting mode; predicting the water temperature of the bay B according to the source water temperature online prediction model;
s4, aiming at the gulf A, selecting the optimal source LSTM neural network prediction model of the gulf B, and selecting the optimal migration LSTM neural network prediction model through migration learning and testing; combining the optimal migration LSTM neural network prediction model, the pure differential regression prediction model of the A bay and the mixed differential regression prediction model into a migration water temperature online prediction model based on a dynamic weighting mode; and predicting the water temperature of the A bay according to the online migration water temperature prediction model.
2. The combined gulf water temperature prediction method of claim 1 based on transfer learning, wherein: the water temperature data in the step S1 includes daily maximum water temperature, daily minimum water temperature, and daily average water temperature; the meteorological data comprises meteorological observation data and meteorological forecast data; the meteorological observation data comprise air temperature statistics, air pressure statistics, relative humidity statistics, wind speed statistics, wind direction statistics and precipitation, and the time resolution is 1 day; the weather forecast data includes future 7-day weather forecast data updated every day, and the future 7-day weather forecast data includes day highest temperature and day lowest temperature of the future 7 days.
3. The combined gulf water temperature prediction method based on transfer learning of claim 1, wherein the pure differential regression prediction model in step S2 is constructed by:
setting the predicted temperature values of the weather forecast released on the k day as
Measured value of water temperature is x k If the current time is t, the predicted value of the water temperature of the day 1 is
The calculation formula of (2) is as follows:
predicted value of water temperature of nth (n is more than or equal to 2 and less than or equal to 7) day
Is calculated byThe formula is as follows:
wherein x is t-1 The measured value of the water temperature on the t-1 day is measured;
the temperature forecast value of the day is acquired on the t +1-i days;
obtaining the air temperature forecast value of the day for the t-i days;
obtaining an air temperature forecast value for the nth day in the future for the t day;
obtaining the temperature forecast value of the day for the t day; w is a p1 ,w p2 ,w p3 And
and selecting the weight by constructing a linear regression equation.
4. The combined gulf water temperature prediction method based on transfer learning of claim 3, wherein the mixed difference regression prediction model in step S2 is constructed by:
setting the predicted temperature values of the weather forecast released on the k day as
Measured value of water temperature is x k If the current time is t, the predicted value of the water temperature of the day 1 is
Is calculated byThe formula is as follows:
predicted value of water temperature of nth (n is more than or equal to 2 and less than or equal to 7) day
The calculation formula of (2) is as follows:
wherein x is t-1 The measured value of the water temperature on the t-1 day is measured; x is the number of t-2 The measured value of the water temperature on the t-2 days is shown;
obtaining the temperature forecast value of the day for the t day;
obtaining the air temperature forecast value of the day for the t-1 day;
obtaining an air temperature forecast value for the nth day in the future for the t day; w is a m1 ,w m2 And
and selecting the weight by constructing a linear regression equation.
5. The combined gulf water temperature prediction method of claim 4, wherein w is selected by linear regression equation p1 ,w p2 ,w p3 ,
w m1 ,w m2 And
the specific mode is as follows:
setting the predicted temperature values of the weather forecast released on the k day as
Measured value of water temperature is x k When the current time is t, according to the historical water temperature and meteorological forecast data of the bay, a linear regression equation is established according to the following formulas, and w is calculated p1 ,w p2 ,w p3 ,
w m1 ,w m2 And
wherein x is t+1-n The measured value of the water temperature on the t +1-n days.
6. The combined gulf water temperature prediction method based on transfer learning of claim 2, wherein the specific process of step S3 is as follows:
s21, adopting water temperature data and meteorological data of the Bay B, carrying out normalization processing on the water temperature data, and dividing the water temperature data and meteorological observation data into a training set and a test set according to the proportion of 0.8: 0.2;
s22, combining meteorological observation data and water temperature data of a plurality of past days to obtain combined data based on a training set part of the water temperature data, and constructing and obtaining a source LSTM neural network prediction model by using the combined data as an input factor and the water temperature data of 7 days in the future as an output factor, wherein the water temperature data of the plurality of past days refers to water temperature data with different time lengths; the combined data is combined with other meteorological observation elements in a traversing mode by taking water temperature data as a fixed input parameter;
s23, inputting the combined data into a trained network based on the test set part of the water temperature data, and obtaining a daily frequency water temperature prediction result of 7 days in the future through inverse normalization;
s24, comparing the predicted result with the measured data, and determining the accuracy, Root Mean Square Error (RMSE), average absolute error (MAE) and coefficient R of the water temperature early warning level 2 Counting the test result of the model as a test standard, and selecting an optimal source LSTM neural network prediction model;
s25, setting the initial weight ratio of the optimal source LSTM neural network prediction model and the pure differential regression prediction model and the mixed differential regression prediction model of the B bay to be 1:1:1, taking root mean square error RMSE as a statistical index, carrying out statistics on the prediction accuracy of the three models every day, enabling the weight to be larger when the accuracy is larger, and dynamically updating the weight of the combined model every day.
7. The combined gulf water temperature prediction method based on transfer learning of claim 6, wherein the specific process of step S4 is as follows:
s31, obtaining the optimal source LSTM neural network prediction model of the Bay B constructed in the steps S21 to S24;
s32, adopting the water temperature data and the meteorological data of the A bay to carry out normalization processing on the data of the A bay, and dividing the data into a training set and a testing set according to the proportion of 0.5: 0.5;
s33, freezing a partial network layer of the optimal source LSTM neural network prediction model, and finely adjusting the network based on the training set part of the A bay so as to realize transfer learning of the optimal source LSTM neural network prediction model;
s34, obtaining a daily frequency water temperature prediction result of 7 days in the future by reverse normalization based on the test set part of the gulf A;
s35, comparing the predicted result with the measured data, and determining the accuracy, Root Mean Square Error (RMSE), average absolute error (MAE) and coefficient R of the water temperature early warning level 2 Counting the test result of the model as a test standard, and selecting an optimal migration LSTM neural network prediction model;
s36, setting the initial weight ratio of the optimal migration LSTM neural network prediction model and the pure differential regression prediction model and the mixed differential regression prediction model of the A gulf as 1:1:1, taking root mean square error RMSE as a statistical index, carrying out statistics on the prediction accuracy of the three models every day, enabling the weight to be larger when the accuracy is larger, and dynamically updating the weight of the combined model every day.
8. The migration learning-based combined bay water temperature prediction method of claim 7, wherein the calculation formula of the normalization process in steps S21 and S32 is:
wherein X is the original measured data, X normal Is normalized data, X mean And X std Mean and variance, respectively, for bay B.
9. The combined gulf water temperature prediction method of claim 7, wherein the denormalization formula in steps S23 and 34 is:
X final ＝X pre *X std +X mean
wherein, X pre Is the original predicted value, X final Is the final predicted value of the inverse normalization, X mean And X std Mean and variance, respectively, for bay B;
the calculation formula of the water temperature early warning grade accuracy in the steps S24 and S35 is as follows:
wherein, the division of the water temperature grades refers to the suitable growth temperature of the large yellow croaker, and the total number of the grades is 7;
and testing all the trained models by taking the root mean square error RMSE as the most important index, and selecting the model with the highest precision in the verification set and the test set as the optimal LSTM neural network prediction model or the migration LSTM neural network prediction model.
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