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LIN Ziyang, CHEN Longjiang, JIN Ruimin, OU Ming, YANG Huiyun, JI Guangwang, CUI Xiang, GU Mingyue. A random forest-based prediction model for ionospheric foF2 in China and surrounding regions[J]. GNSS World of China. doi: 10.12265/j.gnss.2024140
Citation: LIN Ziyang, CHEN Longjiang, JIN Ruimin, OU Ming, YANG Huiyun, JI Guangwang, CUI Xiang, GU Mingyue. A random forest-based prediction model for ionospheric foF2 in China and surrounding regions[J]. GNSS World of China. doi: 10.12265/j.gnss.2024140
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A random forest-based prediction model for ionospheric foF2 in China and surrounding regions

doi: 10.12265/j.gnss.2024140
  • 1.

    China Research Institute of Radiowave Propagation, Qingdao 266107, China

  • 2.

    National Key Laboratory of Electromagnetic Environment, Qingdao 266107, China

  • Received Date: 2024-08-13
    Available Online: 2024-11-01
    Abstract
  • The square of the critical frequency of the ionospheric F2 layer (foF2) is proportional to the peak electron density (NmF2) and serves as a crucial parameter affecting the performance of Global Navigation Satellite Systems (GNSS). Enhancing the prediction accuracy of foF2 is essential for optimizing GNSS broadcast ionospheric models, thereby improving the positioning accuracy of GNSS. This study develops an ionospheric foF2 prediction model for China and its surrounding regions using the random forest algorithm, based on data from 18 ionosonde stations of the China Research Institute of Radiowave Propagation and COSMIC occultation observations. The model incorporates multiple features, including Universal Time, day of the year, geographic location, solar, and geomagnetic activities. A comparative analysis with the International Reference Ionosphere (IRI-2020) model validates the prediction accuracy of our model. The results indicate that the random forest model reduces the mean absolute error by 14.81% and 17.11%, and the root mean square error by 11.21% and 13.14%, compared to the IRI CCIR and IRI URSI models, respectively. Additionally, the model exhibits superior prediction accuracy under various latitudes, local times, solar, and geomagnetic activity conditions when compared to IRI-2020. This research not only significantly enhances the foF2 prediction accuracy for China and its surrounding regions but also lays a critical foundation for improving the accuracy and reliability of GNSS globally.

     

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