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ABSTRACT
The increasing availability of global observational data has sparked a demand for deep learning algorithms on spherical grids to enable intelligent analysis at a global scale. However, a spherical surface cannot be subdivided into completely identical grid cells through recursive division, and its nonuniformity and irregular deformations lead to uncertainties in the spherical convolutional neural network (SCNN). This paper proposes a multimetric evaluation method to assess the impact of the icosahedral diamond grid quality on the performance of the SCNN by introducing the random forest algorithm to establish nonlinear relationships between multiple grid quality metrics and the SCNN performance and using feature importance analysis to assign impact weights to each grid quality metric considering the SCNN performance. The results show an R2 score of 0.80 for the evaluation method, with four indicators having different weights: cell wall midpoint ratio (0.47), distance between grid points and neighbouring points (0.29), zone standardized compactness (0.13), and angle between a grid point and its two neighbours (0.11). The cell wall midpoint ratio indicator has the most significant impact on the SCNN performance among all grid indicators.
Acknowledgement
The authors would like to thank the editors and the anonymous reviewers for their constructive comments and suggestions, which greatly helped to improve the quality of the manuscript.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
All grids in this study are generated by the open-source DGGS implementation – DGGRID, which can be retrieved from https://www.discreteglobalgrids.org/software/. SCNN-IDG code and data can be obtained from https://github.com/Seraph0317/Data-Multi-Metric-Evaluation-Method-.git.