A Combination Prediction Model of Long-Term Ionospheric foF2 Based on Entropy Weight Method
It is critically meaningful to accurately predict the ionospheric F2 layer critical frequency (foF2), which greatly limits the efficiency of communications, radar, and navigation systems. This paper introduced the entropy weight method to develop the combination prediction model (CPM) for long-term...
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MDPI AG
2020-04-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/22/4/442 |
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| author | Hongmei Bai Feng Feng Jian Wang Taosuo Wu |
| author_facet | Hongmei Bai Feng Feng Jian Wang Taosuo Wu |
| author_sort | Hongmei Bai |
| collection | DOAJ |
| description | It is critically meaningful to accurately predict the ionospheric F2 layer critical frequency (foF2), which greatly limits the efficiency of communications, radar, and navigation systems. This paper introduced the entropy weight method to develop the combination prediction model (CPM) for long-term foF2 at Darwin (12.4° S, 131.5° E) in Australia. The weight coefficient of each individual model in the CPM is determined by using the entropy weight method after completing the simulation of the individual model in the calibration period. We analyzed two sets of data to validate the method used in this study: One set is from 2000 and 2009, which are included in the calibration period (1998–2016), and the other set is outside the calibration cycle (from 1997 and 2017). To examine the performance, the root mean square error (RMSE) of the observed monthly median foF2 value, the proposed CPM, the Union Radio Scientifique Internationale (URSI), and the International Radio Consultative Committee (CCIR) are compared. The yearly RMSE average values calculated from CPM were less than those calculated from URSI and CCIR in 1997, 2000, 2009, and 2017. In 2000 and 2009, the average percentage improvement between CPM and URSI is 9.01%, and the average percentage improvement between CPM and CCIR is 13.04%. Beyond the calibration period, the average percentage improvement between CPM and URSI is 13.2%, and the average percentage improvement between CPM and CCIR is 12.6%. The prediction results demonstrated that the proposed CPM has higher precision of prediction and stability than that of the URSI and CCIR, both within the calibration period and outside the calibration period. |
| first_indexed | 2024-03-10T20:29:07Z |
| format | Article |
| id | doaj.art-8194f8441bc34a8f8ad77870c068eb5a |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T20:29:07Z |
| publishDate | 2020-04-01 |
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| series | Entropy |
| spelling | doaj.art-8194f8441bc34a8f8ad77870c068eb5a2023-11-19T21:32:51ZengMDPI AGEntropy1099-43002020-04-0122444210.3390/e22040442A Combination Prediction Model of Long-Term Ionospheric foF2 Based on Entropy Weight MethodHongmei Bai0Feng Feng1Jian Wang2Taosuo Wu3School of Microelectronics, Tianjin University, Tianjin 300072, ChinaDepartment of Electronics, Carleton University, Ottawa, ON K1S 5B6, CanadaSchool of Microelectronics, Tianjin University, Tianjin 300072, ChinaSchool of Microelectronics, Tianjin University, Tianjin 300072, ChinaIt is critically meaningful to accurately predict the ionospheric F2 layer critical frequency (foF2), which greatly limits the efficiency of communications, radar, and navigation systems. This paper introduced the entropy weight method to develop the combination prediction model (CPM) for long-term foF2 at Darwin (12.4° S, 131.5° E) in Australia. The weight coefficient of each individual model in the CPM is determined by using the entropy weight method after completing the simulation of the individual model in the calibration period. We analyzed two sets of data to validate the method used in this study: One set is from 2000 and 2009, which are included in the calibration period (1998–2016), and the other set is outside the calibration cycle (from 1997 and 2017). To examine the performance, the root mean square error (RMSE) of the observed monthly median foF2 value, the proposed CPM, the Union Radio Scientifique Internationale (URSI), and the International Radio Consultative Committee (CCIR) are compared. The yearly RMSE average values calculated from CPM were less than those calculated from URSI and CCIR in 1997, 2000, 2009, and 2017. In 2000 and 2009, the average percentage improvement between CPM and URSI is 9.01%, and the average percentage improvement between CPM and CCIR is 13.04%. Beyond the calibration period, the average percentage improvement between CPM and URSI is 13.2%, and the average percentage improvement between CPM and CCIR is 12.6%. The prediction results demonstrated that the proposed CPM has higher precision of prediction and stability than that of the URSI and CCIR, both within the calibration period and outside the calibration period.https://www.mdpi.com/1099-4300/22/4/442ionospherefoF2entropy weight methodcombination prediction model |
| spellingShingle | Hongmei Bai Feng Feng Jian Wang Taosuo Wu A Combination Prediction Model of Long-Term Ionospheric foF2 Based on Entropy Weight Method Entropy ionosphere foF2 entropy weight method combination prediction model |
| title | A Combination Prediction Model of Long-Term Ionospheric foF2 Based on Entropy Weight Method |
| title_full | A Combination Prediction Model of Long-Term Ionospheric foF2 Based on Entropy Weight Method |
| title_fullStr | A Combination Prediction Model of Long-Term Ionospheric foF2 Based on Entropy Weight Method |
| title_full_unstemmed | A Combination Prediction Model of Long-Term Ionospheric foF2 Based on Entropy Weight Method |
| title_short | A Combination Prediction Model of Long-Term Ionospheric foF2 Based on Entropy Weight Method |
| title_sort | combination prediction model of long term ionospheric fof2 based on entropy weight method |
| topic | ionosphere foF2 entropy weight method combination prediction model |
| url | https://www.mdpi.com/1099-4300/22/4/442 |
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