Regional sea surface temperature prediction method based on CNN-LSTM

Abstract

The invention discloses a regional sea surface temperature prediction method based on CNN-LSTM, and relates to the field of physical ocean, computer graphic image processing and deep learning. The ,method comprises three steps of training sample establishment, model construction and model algorithm adjustment: firstly, carrying out segmentation processing on regional sea surface temperature data by adopting a set-aside method, and setting a predicted time window; training the sea surface temperature training sample and establishing a sea surface temperature prediction model by adopting an algorithm based on combination of a convolutional neural network CNN and a long short-term memory neural network LSTM; and finally, adjusting and training parameters of the model by adopting a trial-and-error method according to the error of the model, and determining parameters of the prediction model, thereby realizing efficient prediction of the regional sea surface temperature. Practice proves that the method can extract the spatial features of the sea surface temperature through the CNN, and then extract the time sequence features through the LSTM, thereby improving the prediction precision and efficiency of the sea surface temperature, and expanding the application of the deep learning method in regional sea surface temperature prediction.

Description

technical field [0001] The invention relates to the technical field of marine information, in particular to a CNN-LSTM-based regional sea surface temperature prediction method. Background technique [0002] Sea surface temperature is the comprehensive result of solar radiation, ocean thermal force, dynamic process, and air-sea interaction, and is an important physical parameter of water vapor and heat exchange on the sea surface. In recent years, more and more studies have begun to pay attention to sea surface temperature, and the prediction of sea surface temperature has become a research hotspot. Traditional sea temperature forecasting generally adopts a forecast method combining statistics and experience. The limitation is that most statistical models use linear correlation to predict non-linear changes in sea temperature. At the same time, it is also limited by people's cognitive level and the bottleneck problem of solving physical mechanisms. , and deep learning has th...

AI Technical Summary

Problems solved by technology

[0006] After analysis, the sea surface temperature prediction method in the above-mentioned published patent is quite different from the present application in terms of modeling methods and deep learning methods, and the above-mentioned patent still uses the first k analogy models obtained from the Spark working cluster as the training set , the prediction result is still linear

Images