The coupling between the magnetosphere and solar wind contributes to the energy, momentum, and mass transfer between the systems. However, geomagnetic pulsations facilitate the continuation of this process in the magnetosphere and the production of discrete auroral arcs. Therefore, remote-sensing the magnetospheric conditions. Data analytics with machine learning (ML) gives insight into scalability, adaptability, and feature extraction compared to traditional empirical models. The availability of big data in the Svalbard network spanning 25years from 1996 motivated the current study. Hence, we present the forecasting of auroral Pc5 pulsations from solar wind parameters using the ML technique. In the training phase, there was a regression of 0.75 and MSE=11.90 nT2. The relationship between Pc5 forecast and observations in low and high geomagnetic activity and solar activity showed good consistency with R=0.76 and MSE= 11.4 nT2. For instance, the model adapted well to the St. Patrick geomagnetic storm of March 17th, 2015 despite uncertainties in the data. In addition, the model also adapted well with stunning performance in all Svalbard observatories with HOP leading with 6949 prediction events and NAL with the least. Thus, this was consistent with previous studies in terms of Pc5 pulsations latitudinal or L-shell dependence. Finally, validation with Kp and F10.7 indices presented excellent coherence between the models. Overall, The ML studied the connection between solar wind and interplanetary magnetic field properties to the ground magnetic field perturbations with good correlation results. Hence, the model will be fit for use by the magnetospheric community for space weather studies.
