Research On Prediction Method Of Ocean And Ionospheric Parameter Model

The ocean and the ionosphere have always played a very important role in social development.As an important part of the earth,the research on the ocean and the ionosphere has already begun very early.While the ocean brings national and social economic benefits,it is also accompanied by severe natural disasters such as typhoons and tsunamis,which pose a major threat to the economic development of coastal areas and the safety of people's lives.Ocean parameters are an important means to describe the characteristics of the ocean.By predicting ocean-related parameters,we can achieve the purpose of detecting abnormal changes in advance for early warning.In addition,when typhoons and tsunami disasters occur,the parameters of the ionosphere will also undergo certain changes.Therefore,the prediction of ocean and ionospheric parameters and analysis of their changes and trends are of great significance for early warning of natural disasters and daily marine activities.The main research content of this dissertation is to analyze the acquired ocean and ionospheric parameter data,deeply study the characteristics of ocean and ionospheric parameters,and establish prediction models of ocean and ionospheric parameters after selecting appropriate relevant parameters.Achieve reliable predictions of ocean and ionospheric parameters.Knowing the parameter changes in the next period of time through the prediction results can not only be used for disaster warnings such as tsunamis and typhoons,but also provide refe rence information for ship travel and maritime rescue.The specific work of this paper is as follows:First,a systematic theoretical analysis of the methods and principles of time series analysis and deep learning is carried out,and the main advantages,necessary conditions and main models of time series modeling are explained.The related concepts of deep learning and the propagation process and existing shortcomings of the recurrent neural network are analyzed.In addition,the basic structure of the ocean and the ionosphere is described,the relevant parameters describing the characteristics of the ocean and the ionosphere are analyzed,and the parameters are selected for prediction.Secondly,this dissertation proposes the use of differential auto-regressive moving average model and deep learning long short-term memory network to model the ocean and ionospheric parameters.The specific modeling steps of the two models are analyzed in detail,and then the parameter data is analyzed.Preprocessing,after the model is successfully established,the reliability of the model is tested by means of model residual testing,and at the same time,the model prediction accuracy is evaluated and analyzed by the root mean square error.Finally,this dissertation compares and analyzes the two established ocean parameter prediction models and the two ionospheric parameter prediction models.The simulation results show that the ocean parameter prediction model and the ionospheric parameter prediction model established by the deep learning long and short-term memory network are better than the differential autoregressive moving average prediction model in terms of trend and prediction accuracy.