TY  - JOUR
A1  - Guo, Yingjie
A1  - Ni, Binbin
A1  - Fu, Song
A1  - Wang, Dedong
A1  - Shprits, Yuri Y.
A1  - Zhelavskaya, Irina
A1  - Feng, Minghang
A1  - Guo, Deyu
T1  - Identification of controlling geomagnetic and solar wind factors for magnetospheric chorus intensity using feature selection techniques
T2  - Journal of geophysical research : A, Space physics
N2  - Using over-5-year EMFISIS wave measurements from Van Allen Probes, we present a detailed survey to identify the controlling factors among the geomagnetic indices and solar wind parameters for the 1-min root mean square amplitudes of lower band chorus (LBC) and upper band chorus (UBC). 
A set of important features are automatically determined by feature selection techniques, namely, Random Forest and Maximum Relevancy Minimum Redundancy. Our analysis results indicate the AE index with zero-time-delay dominates the intensity evolution of LBC and UBC, consistent with the evidence that chorus waves prefer to occur and amplify during enhanced substorm periods. Regarding solar wind parameters, solar wind speed and IMF B-z are identified as the controlling factors for chorus wave intensity. Using the combination of all these important features, a predictive neural network model of chorus wave intensity is established to reconstruct the temporal variations of chorus wave intensity, for which application of Random Forest produces the overall best performance. 

Plain Language Summary
Whistler mode chorus waves are electromagnetic waves observed in the low-density region near the geomagnetic equator outside the plasmapause. The dynamics of Earth's radiation belts are largely influenced by chorus waves owing to their dual contributions to both radiation belt electron acceleration and loss. In this study, we use feature selection techniques to identify the controlling geomagnetic and solar wind factors for magnetospheric chorus waves. Feature selection techniques implement the processes which can select the features most influential to the output. 
In this study, the inputs are geomagnetic indices and solar wind parameters and the output is the chorus wave intensity. The results indicate that AE index with zerotime delay dominates the chorus wave intensity. Furthermore, solar wind speed and IMF B-z are identified as the most important solar wind drivers for chorus wave intensity. 
On basis of the combination of all these important geomagnetic and solar wind controlling factors, we develop a neural network model of chorus wave intensity, and find that the model with the inputs identified using the Random Forest method produces the overall best performance.
Y1  - 2022
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/63997
SN  - 2169-9380
SN  - 2169-9402
VL  - 127
IS  - 1
PB  - Wiley
CY  - Hoboken, NJ
ER  -