# Ionospheric Electron Density Model by Electron Density Grid Deep Neural Network (EDG-DNN)
## 5 Conclusions  
Based on the above results, our conclusions are as follows:  
1. The EDG-DNN model can present a gridded ionospheric electron density distribution with more accurate predictions. It can also be used as a reliable electron density reference model for further studies of the ionosphere. 2. The EDG-DNN model learns the distribution features of a vast quantity of electron density data in order to better depict the structure of the ionosphere, such as the equatorial bimodal structure, in the forecast. It can also reflect the electron density variation and distribution during magnetic storms to some extent, which is important for further research into the electron density variation and dispersion during magnetic storms. This study shows that DNN can be used to build a four-dimensional electron density model of the ionosphere. This demonstrates that deep learning can be applied to a wider range of earth sciences, particularly to the problem of predicting features such as ionospheric electron density. In the future, the study plans to use more accurate deep-learning models and electron density data to obtain better electron density models, be more accurate in predicting electron density, construct detailed electron density features during magnetic storms, and analyse and predict electron density during storms. Conceptualization, Z.C. ; Methodology, Z.C. ; Software, Z.C. and B.A. ; Validation, Z.C. and B.A. ; Formal analysis, B.A. and W.L. ; Investigation, B.A. and W.L. ; Resources, W.L. and R.T.; Data curation, R.T.; Writing--review & editing, Y.W. ; Visualization, X.D. ; Project administration, J.W. ; Funding acquisition, J.W. All authors have read and agreed to the published version of the manuscript. This work is funded by the National Natural Science Foundation of China (grants no. 41974183, 41974195, 41774195, 41604136 and 41674144), and the Interdisciplinary Innovation Fund of Natural Science from Nanchang University (grant no. 9166-27060003-YB14). Not applicable. Note applicable. NoteNoteNoteNoteNoteNoteNoteNoteNoteNoteNote  
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