Meteorological Satellite Operation Prediction Using a BiLSTM Deep Learning Model

Abstract

The current satellite management system mainly relies on manual work. If small faults cannot be found in time, it may cause systematic fault problems and then affect the accuracy of satellite data and the service quality of meteorological satellite. If the operation trend of satellite will be predicted, the fault can be avoided. However, the satellite system is complex, and the telemetry signal is unstable, nonlinear, and time-related. It is difficult to predict through a certain model. Based on these, this paper proposes a bidirectional long short-term memory (BiLSTM) deep leaning model to predict the operation trend of meteorological satellite. In the method, the layer number of the model is designed to be two, and the prediction results, which are forecasted by LSTM network as the future trend data and historical data, are both taken as the input of BiLSTM model. The dataset for the research is generated and transmitted from Advanced Geostationary Radiation Imager (AGRI), which is the load of FY4A meteorological satellite. In order to demonstrate the superiority of the BiLSTM prediction model, it is compared with LSTM based on the same dataset in the experiment. The result shows that the BiLSTM method reports a state-of-the-art performance on satellite telemetry data.

Machine Learning and Deep Learning powered satellite communications: Enabling technologies, applications, open challenges, and future research directions

“…• In Peng et al 41 a comparison between BiLSTM, LSTM and RNN models is shown, the resultant RMSE score values of the BiLSTM model are consistently smaller than those of LSTM and RNN, leading to the conclusion that the BiLSTM model can predict the operation trends of a meteorological satellite with greater effectiveness.…”

“…The given factors first indivialually then in combinations using a sliding window technique for leak classification. The precision, recall and accuracy was measured to be 86%, 86%, and 85%, respectively. In Peng et al 41 a comparison between BiLSTM, LSTM and RNN models is shown, the resultant RMSE score values of the BiLSTM model are consistently smaller than those of LSTM and RNN, leading to the conclusion that the BiLSTM model can predict the operation trends of a meteorological satellite with greater effectiveness.…”

A Novel Nonferrous Metals Price Prediction Model Based on BiLSTM-ResNet with Grey Wolf Optimization and Wavelet Transform

“…Notwithstanding, price data is not only dependent on information from the past, but also related to future financial conditions. BiLSTM is the expansion of LSTM that combines the forward and backward processing to capture useful information [25,26]. Hence, it can be performed to make the forecast more precise.…”

Short-Term Trajectory Prediction Based on Hyperparametric Optimisation and a Dual Attention Mechanism

“…The BiLSTM [34] network is a two-layer LSTM network consisting of a combination of forward and reverse LSTM layers. The network structure of the BiLSTM is shown in Figure 4.…”