An Interpretable Machine Learning Procedure Which Unravels Hidden Interplanetary Drivers of the Low Latitude Dayside Magnetopause

An Interpretable Machine Learning Procedure Which Unravels Hidden Interplanetary Drivers of the Low Latitude Dayside Magnetopause

Abstract In this study, we propose an interpretable machine learning procedure to unravel the importance of multiple interplanetary parameters to the Earth's magnetopause standoff distance (MSD). We construct the interpretable procedure based on SHapley Additive exPlanations. A magnetopause cro...

Full description

Saved in:
Bibliographic Details
Main Authors: Sheng Li, Yang‐Yi Sun, Chieh‐Hung Chen
Format: Article
Language:English
Published: Wiley 2023-03-01
Series:Space Weather
Subjects:
magnetopause
interpretable machine learning
IMF magnitude
SHAP value
Online Access:https://doi.org/10.1029/2022SW003391
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items