An explainable Machine Learning model for Large-Scale Travelling Ionospheric Disturbances forecasting | Journal of Space Weather and Space Climate

Topical Issue - Observing, modelling and forecasting TIDs and mitigating their impact on technology

Open Access

Figure 1

CatBoost working principle, which learns subsequent trees to reduce the error made by the previous ones, thus minimizing the loss function in equation (1).

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.