Forecasting Ionospheric foF2 Based on Deep Learning Method
Abstract
:1. Introduction
2. Models and Data
2.1. Neural Network Models
2.1.1. Methodology Description
2.1.2. Input and Output Parameters
2.1.3. Configuration and Training
2.2. Deep Learning LSTM Model
2.2.1. Methodology Description
2.2.2. Input and Output Parameters
2.2.3. Configuration and Training
2.3. Data and Processing
2.3.1. Data Sets
2.3.2. Data Preprocess
2.4. Error Analysis
3. Results and Discussion
3.1. The Forecasting Performance
3.2. Diurnal Variations of Forecasting Models
3.3. Seasonal Variations of Forecasting Models
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
LSTM | Long-Short-Term Memory |
foF2 | the critical frequency of the ionosphere F2 layer |
LT | Local Time |
SSN | the Sunspot Number |
BPNN | a Back-Propagation Neural Network |
GABP | a Genetic Algorithm optimized Backpropagation neural network |
IRI | the International Reference Ionosphere model |
HF | High Frequency |
RNN | a Recurrent Neural Network |
GRU | Gated Recurrent Unit |
GA | Genetic Algorithm |
TEC | Total Electron Content |
URSI | Union of Radio Science International |
COSPAR | the Committee on Space Research |
RMSE | Root Mean Square Error |
PD | Percentage Deviation |
hmF2 | peak heights of the F2 layer |
M3000F2 | M factor of F2 layer |
NOAA | the National Oceanic and Atmospheric Administration |
WDC | the World Data System |
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No. | URSI | Station/Abbreviation | Country | Time Zone | Lat | Lon |
---|---|---|---|---|---|---|
01 | DW41K | Darwin (DAR) | Australia | UTC + 10 | −12.45 | 130.95 |
02 | SA418 | Sanya (SAY) | China | UTC + 8 | 18.34 | 109.62 |
03 | SH427 | Shaoyang (SHY) | China | UTC + 8 | 26.9 | 111.5 |
04 | BR52P | Brisbane (BRI) | Australia | UTC + 10 | −27.06 | 153.06 |
05 | WU430 | Wuhan (WUH) | China | UTC + 8 | 30.54 | 114.34 |
06 | CN53L | Camden (CAM) | Australia | UTC + 10 | −34.05 | 150.67 |
07 | CB53N | Canberra (CAN) | Australia | UTC + 10 | −35.32 | 149 |
08 | BP440 | Beijing (BEJ) | China | UTC + 8 | 39.98 | 116.37 |
09 | HO54K | Hobart (HOB) | Australia | UTC + 10 | −42.92 | 147.32 |
10 | MH453 | Mohe (MOH) | China | UTC + 8 | 53.49 | 122.34 |
Year | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 |
BEJ | 3973/8760 (45.35%) | 8637/8760 (98.60%) | 8327/8784 (94.80%) | 7215/8760 (82.36%) | 6567/8760 (74.97%) | 6935/8760 (79.17%) | 8012/8784 (91.21%) |
MOH | / | / | / | / | 2643/8760 (30.17%) | 8110/8760 (92.58%) | 8012/8784 (91.21%) |
WUH | / | / | / | / | 2845/8760 (32.48%) | 5873/8760 (67.04%) | 8523/8784 (97.03%) |
SAY | / | 1507/8760 (17.20%) | 7374/8784 (83.95%) | 6587/8760 (75.19%) | 7958/8760 (90.84%) | 8194/8760 (93.54%) | 6261/8784 (71.28%) |
SHY | / | / | / | / | / | / | 5641/8784 (64.22%) |
Year | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 |
BEJ | 8626/8760 (98.47%) | 8521/8760 (97.27%) | 8672/8760 (99.00%) | 8733/8784 (99.42%) | 7735/8760 (88.30%) | 8423/8760 (96.15%) | 6538/8760 (74.63%) |
MOH | 8332/8760 (95.11%) | 8628/8760 (98.49%) | 8132/8760 (92.83%) | 8649/8784 (98.46%) | 8635/8760 (98.57%) | 8585/8760 (98.00%) | 8422/8760 (96.14%) |
WUH | 8372/8760 (95.57%) | 8666/8760 (98.93%) | 7323/8760 (83.60%) | 8617/8784 (98.10%) | 8542/8760 (97.51%) | 8665/8760 (98.92%) | 8542/8760 (97.51%) |
SAY | 8273/8760 (94.44%) | 8461/8760 (96.59%) | 8593/8760 (98.09%) | 8590/8784 (97.79%) | 8595/8760 (98.12%) | 7168/8760 (81.83%) | 8669/8760 (98.96%) |
SHY | 8420/8760 (96.12%) | 8542/8760 (97.51%) | 8556/8760 (97.67%) | 8446/8784 (96.15%) | 8689/8760 (99.19%) | 7991/8760 (91.22%) | / |
DAR | / | 8574/8760 (97.88%) | 6959/8760 (79.44%) | 8367/8784 (95.25%) | 8492/8760 (96.94%) | 7520/8760 (85.84%) | 8439/8760 (96.34%) |
BRI | / | 8739/8760 (99.76%) | 7982/8760 (91.12%) | 8643/8784 (98.39%) | 8332/8760 (95.11%) | 8073/8760 (92.16%) | 8559/8760 (97.71%) |
CAM | / | 7962/8760 (90.89%) | 7557/8760 (86.27%) | 7245/8784 (82.48%) | 8407/8760 (95.97%) | 3509/8760 (40.06%) | 2816/8760 (32.15%) |
CAN | / | 8620/8760 (98.40%) | 7752/8760 (88.49%) | 7908/8784 (90.03%) | 8509/8760 (97.13%) | 6552/8760 (74.79%) | 7593/8760 (86.68%) |
HOB | / | 8014/8760 (91.48%) | 6934/8760 (79.16%) | 7261/8784 (82.66%) | 8177/8760 (93.34%) | 7757/8760 (88.55%) | 7945/8760 (90.70%) |
Station | Year | BPNN | GABP | IRI2016 | LSTM | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
RMSE (MHz) | ρ | PD (%) | RMSE (MHz) | ρ | PD (%) | RMSE (MHz) | ρ | PD (%) | RMSE (MHz) | ρ | PD (%) | ||
DAR | 2015 | 1.784 | 0.842 | 16.72 | 1.803 | 0.846 | 16.25 | 1.819 | 0.837 | 18.39 | 1.148 | 0.937 | 12.67 |
2019 | 1.054 | 0.873 | 17.24 | 1.095 | 0.869 | 17.44 | 1.575 | 0.860 | 25.56 | 0.913 | 0.898 | 14.65 | |
SAY | 2015 | 1.5 | 0.908 | 13.95 | 1.462 | 0.913 | 13.78 | 1.823 | 0.894 | 15.36 | 1.134 | 0.948 | 11.07 |
2019 | 1.497 | 0.882 | 25.90 | 1.425 | 0.893 | 24.65 | 1.758 | 0.887 | 27.56 | 0.937 | 0.945 | 14.39 | |
SHY | 2015 | 1.717 | 0.876 | 15.63 | 1.685 | 0.882 | 15.64 | 1.859 | 0.867 | 16.06 | 1.084 | 0.953 | 11.18 |
BRI | 2015 | 1.033 | 0.849 | 12.02 | 1.058 | 0.841 | 12.18 | 1.147 | 0.831 | 13.29 | 0.833 | 0.907 | 9.35 |
2019 | 0.768 | 0.728 | 13.18 | 0.785 | 0.722 | 13.80 | 0.980 | 0.757 | 16.59 | 0.676 | 0.757 | 11.44 | |
WUH | 2015 | 1.254 | 0.843 | 13 | 1.198 | 0.858 | 12.15 | 1.317 | 0.843 | 14.69 | 1.194 | 0.862 | 11.98 |
2019 | 0.786 | 0.878 | 14.44 | 0.791 | 0.885 | 14.55 | 1.392 | 0.859 | 25.13 | 0.648 | 0.919 | 11.68 | |
CAM | 2015 | 0.963 | 0.867 | 13.04 | 0.953 | 0.869 | 12.87 | 1.026 | 0.853 | 13.79 | 0.751 | 0.921 | 9.53 |
2019 | 0.656 | 0.746 | 12.06 | 0.648 | 0.749 | 11.92 | 1.033 | 0.760 | 20.13 | 0.593 | 0.790 | 10.12 | |
CAN | 2015 | 0.955 | 0.86 | 13.31 | 0.879 | 0.881 | 12.21 | 0.998 | 0.847 | 13.84 | 0.661 | 0.935 | 8.66 |
2019 | 0.652 | 0.772 | 12.72 | 0.670 | 0.768 | 13.19 | 0.802 | 0.776 | 15.68 | 0.529 | 0.827 | 10.19 | |
BEJ | 2015 | 0.852 | 0.914 | 10.67 | 0.832 | 0.919 | 10.41 | 0.875 | 0.91 | 11.28 | 0.666 | 0.949 | 8.07 |
2019 | 0.593 | 0.860 | 11.64 | 0.587 | 0.865 | 11.38 | 0.888 | 0.847 | 16.74 | 0.514 | 0.887 | 9.55 | |
HOB | 2015 | 0.976 | 0.85 | 14.17 | 0.918 | 0.869 | 13.47 | 1.031 | 0.826 | 15.23 | 0.803 | 0.903 | 10.89 |
2019 | 0.587 | 0.800 | 12.28 | 0.597 | 0.797 | 12.27 | 0.788 | 0.811 | 16.88 | 0.592 | 0.795 | 11.92 | |
MOH | 2015 | 0.913 | 0.892 | 13.76 | 0.918 | 0.893 | 13.7 | 0.966 | 0.879 | 14.35 | 0.625 | 0.952 | 9.03 |
2019 | 0.595 | 0.808 | 13.87 | 0.621 | 0.815 | 14.02 | 0.779 | 0.805 | 16.94 | 0.415 | 0.897 | 8.81 |
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Li, X.; Zhou, C.; Tang, Q.; Zhao, J.; Zhang, F.; Xia, G.; Liu, Y. Forecasting Ionospheric foF2 Based on Deep Learning Method. Remote Sens. 2021, 13, 3849. https://doi.org/10.3390/rs13193849
Li X, Zhou C, Tang Q, Zhao J, Zhang F, Xia G, Liu Y. Forecasting Ionospheric foF2 Based on Deep Learning Method. Remote Sensing. 2021; 13(19):3849. https://doi.org/10.3390/rs13193849
Chicago/Turabian StyleLi, Xiaojun, Chen Zhou, Qiong Tang, Jun Zhao, Fubin Zhang, Guozhen Xia, and Yi Liu. 2021. "Forecasting Ionospheric foF2 Based on Deep Learning Method" Remote Sensing 13, no. 19: 3849. https://doi.org/10.3390/rs13193849