Using Bidirectional Long Short-Term Memory Method for the Height of F2 Peak Forecasting from Ionosonde Measurements in the Australian Region

Abstract

The height of F2 peak (hmF2) is an essential ionospheric parameter and its variations can reflect both the earth magnetic and solar activities. Therefore, reliable prediction of hmF2 is important for the study of space, such as solar wind and extreme weather events. However, most current models are unable to forecast the variation of the ionosphere effectively since real-time measurements are required as model inputs. In this study, a new Australian regional hmF2 forecast model was developed by using ionosonde measurements and the bidirectional Long Short-Term Memory (bi-LSTM) method. The hmF2 value in the next hour can be predicted using the data from the past five hours at the same location. The inputs chosen from a location of interest include month of the year, local time (LT), K p , F 10 . 7 and hmF2 as an independent variable vector. The independent variable vectors in the immediate past five hours are considered as an independent variable set, which is used as an input of the new Australian regional hmF2 forecast model developed for the prediction of hmF2 in the hour to come. The performance of the new model developed is evaluated by comparing with those from other popular models, such as the AMTB, Shubin, ANN and LSTM models. Results showed that: (1) the new model can substantially outperform all the other four models. (2) Compared to the LSTM model, the new model is proven to be more robust and rapidly convergent. The mew model also outperforms that of the ANN model by around 30%. (3) the minimum sample number for the bi-LSTM method (i.e., 2000) to converge is about 50% less than that is required for the LSTM method (i.e., 3000). (4) Compared to the Shubin model, the bi-LSTM method can effectively forecast the hmF2 values up to 5 h. This research is a first attempt at using the deep learning-based method for the application of the ionospheric prediction.

A New Artificial Neural Network‐Based Global Three‐Dimensional Ionospheric Model (ANNIM‐3D) Using Long‐Term Ionospheric Observations: Preliminary Results

“…The utilization of ANNs in ionospheric and magnetospheric studies is steadily increasing with time. For example, the prediction of ionospheric peak height ( h m F 2), peak density/critical frequency of ionosphere ( N m F 2/ foF 2), total electron content, sunspot number, and interplanetary conditions using ANNs can be found in the literature (Athieno et al, ; Clausen & Nickisch, ; Hu & Zhang, ; Huang & Yuan, ; Kumluca et al, ; Lamming & Cander, ; Oyeyemi et al, ; Oyeyemi & Poole, ; Poole & Poole, ; Watthanasangmechai et al, ; Wintoft, ; Xenos, ; Zhao et al, ; Zhelavskaya et al, ). These studies demonstrate the ability of ANNs to model and predict the ionospheric and the magnetospheric parameters under various geomagnetic conditions.…”

Short-term rainfall forecast model based on the improved BP–NN algorithm

“…The mathematical principle of the forward propagation of BP–NN is as follows 37 :where X i is the input vector, M is the number of input layer nodes and , is the weighted value between the i th neurons in the input layer and the j th neurons in the hidden layer, f 1 is the threshold parameter of the hidden layer, Y j is the node input value of the hidden layer and , and N is the number of hidden layer nodes. The input value of each hidden layer node is converted to the output value L j of the corresponding hidden layer node through the nonlinear transfer function.…”

Variations of TEC Over Iberian Peninsula in 2015 Due to Geomagnetic Storms and Solar Flares

“…In particular, the method called principal component analysis (PCA) showed good potential for the analysis and modeling of ionospheric regular variations and disturbances (Chen et al, 2015; Li et al, 2019; Morozova et al, 2019). The advantage of the regional models (e.g., Hu & Zhang, 2018; Mukhtarov et al, 2018; Petry et al, 2014; Tebabal et al, 2019; Tsagouri et al, 2018) over the global ones is in their better reflection of specific local behavior of the ionosphere.…”